AI-generated summaries
Today's ML research,
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Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.
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From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
Theory
- AI serves as both a diagnostic tool and an intervention in existing institutional failures.
- A 'rupture test' is proposed to evaluate the impact of AI on human relationships and institutional weaknesses.
- The paper critiques the transition from ethical principles to protocols in AI governance.
- The RISE AI framework is introduced to make explicit and evidence-based claims about AI's societal impact.
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From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
Summary
This paper discusses the dual role of Artificial Intelligence (AI) as both a diagnostic tool and an intervention in societal institutions that have historically failed to provide essential human values such as responsiveness, belonging, care, and accountability. The authors argue that AI should not only be evaluated for its technical performance but also for its impact on existing institutional weaknesses. They propose a 'rupture test' to assess the conditions that necessitate AI deployment and its effects on human relationships and capacities. The paper critiques the current shift from ethical principles to protocols, highlighting the need for a moral framework that encompasses the broader socio-economic and political context in which AI operates. Drawing from Pope Leo XIVโs 'Magnifica Humanitas', the authors introduce the RISE AI framework, which emphasizes making bounded, evidence-based claims about responsibility, inclusivity, safety, and empowerment in AI systems. The authors conclude that responsible AI requires better engineering, institutional repair, and ongoing moral and political judgment.
Methodology
The authors develop a conceptual framework that integrates ethical considerations with practical assessments of AI deployment, including the introduction of the 'rupture test' and the RISE AI architecture.
Results
The paper outlines the limitations of current AI governance frameworks and emphasizes the need for a more holistic approach that considers institutional contexts and human dignity. The RISE AI framework is proposed as a means to articulate bounded claims about AI's responsibilities and impacts.
Implications
The findings suggest that AI governance should not only focus on technical compliance but also address deeper institutional issues. The proposed frameworks can guide policymakers and practitioners in implementing AI systems that genuinely serve the common good.
Measuring the Value of World-Model Updates: A Counterfactual Utility Protocol for Continual Adaptation
Reinforcement Learning
Robotics
Optimization
- Introduction of the fork ledger for evaluating individual world-model updates.
- Demonstrated that fixed update mechanisms can negatively impact performance across multiple tasks.
- Counterfactual comparisons reveal that the value of updates varies with task and environmental drift.
- The methodology allows for observable counterfactual utility in simulation, enhancing decision-making processes.
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Measuring the Value of World-Model Updates: A Counterfactual Utility Protocol for Continual Adaptation
Summary
This paper addresses the challenge of determining when to update continual world models based on new data. Traditional methods rely on fixed replay schedules and prediction-error triggers, which do not provide insight into the value of individual updates. The authors introduce the 'fork ledger,' a novel protocol that allows for counterfactual evaluation of updates by branching deployment streams at pre-registered decision points into matched UPDATE and HOLD continuations. This method enables the comparison of returns from both scenarios under common random conditions. The study finds that consistently applying a fixed update mechanism can decrease performance across various tasks, including CartPole, Walker, and Cheetah. The results indicate that the value of updates is not uniform and can vary significantly depending on the task and the nature of environmental drift. The fork ledger thus provides a framework for assessing the utility of updates in a more granular manner, allowing for better-informed decisions regarding model adaptations.
Methodology
The fork ledger protocol involves deploying a pretrained world model and branching the deployment stream at decision points into UPDATE and HOLD scenarios. Both branches are evaluated under identical conditions to measure the difference in returns (โR). The study employs a fixed update mechanism and assesses its impact across three control tasks, utilizing matched random numbers to ensure comparability.
Results
The application of a fixed update mechanism consistently lowered returns across all three tasks: CartPole (โ144.0), Walker (โ82.8), and Cheetah (โ18.6). When focusing on the 693 successful forks that did not collapse, the results remained negative for CartPole and Walker, while Cheetah's outcome was inconclusive. The findings suggest that the choice of update mechanism significantly influences performance and that the fork ledger provides a valuable tool for understanding the implications of model updates.
Implications
The fork ledger can enhance the decision-making process in continual learning systems by providing a clearer understanding of the value of updates. This could lead to more effective model management strategies in dynamic environments, ultimately improving the performance of reinforcement learning systems.
General Quantification of Covariate and Concept Shifts
Theory
- Introduces ฮณโ-concept shifts to address ill-defined concept shifts due to support mismatch.
- Derives a general error bound that unifies covariate and ฮณโ-concept shifts applicable to various learning tasks.
- Proposes two estimators for accurately estimating shifts from finite samples.
- Develops the DataShifts algorithm for practical quantification of distribution shifts.
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General Quantification of Covariate and Concept Shifts
Summary
This paper addresses the challenge of generalization under distribution shift in machine learning, where the training and test data distributions differ significantly. The authors identify limitations in existing theoretical frameworks that fail to account for support mismatch between source and target distributions, rendering the concept shift ill-defined and non-estimable. They introduce a new notion called ฮณโ-concept shifts, which is well-defined even in cases of support mismatch. By leveraging entropic optimal transport, the authors derive a general error bound that unifies covariate and ฮณโ-concept shifts applicable to a wide range of loss functions and label spaces. They also propose two estimators for these shifts, ensuring that they can be accurately estimated from samples. The DataShifts algorithm is developed to quantify distribution shifts and estimate the error bounds in practical applications. The theoretical framework and algorithm are validated through experiments on tasks such as enzyme prediction and image classification.
Methodology
The authors employ entropic optimal transport to define ฮณโ-concept shifts and derive a general error bound. They develop two estimators for covariate and ฮณโ-concept shifts, proving their concentration inequalities. The DataShifts algorithm integrates these theoretical results to provide a practical tool for quantifying shifts in labeled data.
Results
The paper presents a new error bound that is agnostic to specific hypothesis spaces and loss functions, making it applicable to both binary and multiclass classification, as well as regression tasks. The proposed estimators demonstrate concentration around true values, and the DataShifts algorithm effectively quantifies shifts in various applications, confirming the robustness of the theoretical framework.
Implications
The findings have significant implications for improving model performance in real-world applications where distribution shifts occur. The theoretical advancements and practical tools provided can enhance the robustness of machine learning models across diverse domains, facilitating better generalization under varying data distributions.
TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription
Audio & Speech
- TART addresses limitations in existing guitar transcription systems by capturing expressive techniques.
- The modular design allows for improved accuracy in string-fret assignments and generalization to noisy audio.
- TART achieves state-of-the-art results in zero-shot evaluations across multiple benchmarks.
- The framework generates tablature with detailed fingering and expressive annotations directly from audio.
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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription
Summary
The paper presents TART, a novel modular tool designed to enhance Automatic Music Transcription (AMT) for guitar by addressing key challenges such as capturing expressive techniques, resolving string-fret ambiguities, and improving generalization to noisy audio. TART consists of a four-stage pipeline: (1) an audio-to-MIDI transcription model that converts audio into a MIDI note sequence, (2) an expressive technique classifier that identifies techniques like slides and bends, (3) a T5 encoder-decoder model that assigns string-fret combinations based on audio and MIDI inputs, and (4) an automated tablature generator that produces a beat-aligned MusicXML score. The authors evaluate TART in a zero-shot setting across multiple benchmarks, demonstrating significant improvements over prior models in audio-to-MIDI transcription and string-fret assignment. TART is the first framework capable of generating guitar tablature with both fingering and expressive technique annotations directly from audio, marking a substantial advancement in the field of guitar transcription.
Methodology
TART employs a four-stage pipeline: (1) a high-resolution CRNN for audio-to-MIDI transcription, (2) a temporal CNN-BiLSTM classifier for expressive technique recognition, (3) a T5 encoder-decoder model for resolving string-fret ambiguities, and (4) an automated tablature generator that compiles the results into a MusicXML format.
Results
TART achieves an average audio-to-MIDI F50 score of 81.35%, a string-fret Tab F1 score of 71.8%, and an overall end-to-end Tab F1 score of 54.08%, surpassing previous state-of-the-art models by notable margins.
Implications
The development of TART has significant implications for music education, performance analysis, and music production, enabling musicians to transcribe and analyze guitar performances more effectively, even in challenging audio conditions.
When More Is Not Better: Component Anti-Synergy in a P300 Speller
Theory
- Component contributions in P300 spellers are conditional rather than additive.
- Calibration is the most crucial component for performance in P300 spellers.
- Adding components can lead to anti-synergy, reducing overall system performance.
- Language model support is not universally beneficial and depends on EEG evidence quality.
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When More Is Not Better: Component Anti-Synergy in a P300 Speller
Summary
This paper investigates the effectiveness of various components in P300 brain-computer interface (BCI) spellers, challenging the common assumption that adding more components always enhances performance. The authors conducted a full-factorial experiment with four components: Euclidean Alignment (EA), xDAWN spatial filtering, subject calibration, and language model (LM) priors, using a public P300 dataset with 10 subjects across three sessions. The study found that the contribution of these components is conditional rather than additive, with calibration being the most significant factor. Interestingly, the presence of certain components could lead to reduced performance due to anti-synergy effects. For instance, while EA was beneficial in zero-calibration settings, its combination with other components could hinder efficiency. Additionally, the utility of LM support was found to depend heavily on the strength of the underlying EEG pipeline, indicating that language models may not always provide the expected benefits. These findings suggest a need for more nuanced designs in BCI systems, emphasizing the importance of selecting components based on the quality of EEG evidence rather than simply maximizing the number of components used.
Methodology
The authors employed a full-factorial experimental design, varying the inclusion of four components (EA, xDAWN, calibration, and LM) across 16 configurations. Performance metrics included accuracy, repetitions per character, and information transfer rate (ITR), analyzed using linear mixed-effects models on a public P300 dataset.
Results
The results revealed that calibration was the dominant contributor to performance, while EA could compensate for its absence in zero-calibration scenarios. The study also found that the addition of components could lead to performance degradation due to anti-synergy, and that LM support was context-dependent, being harmful when EEG evidence was weakly aligned or personalized.
Implications
These findings have significant implications for the design of BCI systems, suggesting that a careful selection of components based on the quality of EEG data is essential. This could lead to more efficient and effective communication aids for individuals with severe motor impairments.
Beyond Solver Verdicts: Generative Reward Models for Autoformalization
Theory
Generative Models
NLP
- Introduction of Verdict-Preserving-Unfaithfulness (VPU) as a formalized failure mode in autoformalization.
- Development of Generative Verification (GenV) that distills offline equivalence checks into a continuous scoring system.
- Empirical validation showing GenV's effectiveness in detecting VPU across diverse formal styles and unseen translators.
- Achieved an AUROC score of 0.961 in reference-equivalence verification, demonstrating high accuracy.
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Beyond Solver Verdicts: Generative Reward Models for Autoformalization
Summary
This paper addresses a critical vulnerability in neurosymbolic systems, termed Verdict-Preserving-Unfaithfulness (VPU), where incorrect formal translations can yield valid solver verdicts despite not being logically equivalent to the intended formalization. The authors theoretically prove that traditional structural verification methods are limited in their ability to detect such failures. To overcome this, they introduce Generative Verification (GenV), which transforms offline equivalence checks into a continuous reference-equivalence score that operates without a designated reference during inference. This approach leverages the language model's inherent capabilities to assess the correctness of formal encodings. The paper presents a comprehensive evaluation of GenV, demonstrating its effectiveness in identifying VPUs across various unseen translators and formal styles, achieving a notable AUROC score of 0.961 in reference-equivalence verification. Additionally, the method shows significant improvements in downstream accuracy for agentic test-time compute allocation, indicating its potential to enhance the reliability of autoformalization processes.
Methodology
The authors formalize the concept of VPU and develop GenV, which utilizes the language model's token-prediction capabilities to generate a continuous equivalence score. The methodology includes a mechanistic analysis using decision-projected logit lenses and sparse autoencoders to extract error coordinates without explicit localization training. The evaluation involves controlled experiments across various translator architectures and formal styles, assessing the model's performance against traditional verification methods.
Results
GenV+HN achieved an AUROC of 0.961 in reference-equivalence verification, significantly outperforming traditional verdict-only checks which yielded chance-level detection (AUROC of 0.500). The method also demonstrated generalization capabilities across unseen translators and formal styles, leading to an 11.3-point increase in downstream accuracy for agentic test-time compute allocation.
Implications
The findings suggest that GenV can significantly enhance the reliability of neurosymbolic systems by providing a robust mechanism for verifying formal translations. This has potential applications in various domains where autoformalization is critical, such as logical question answering and policy checks, improving the overall accuracy and trustworthiness of automated reasoning systems.
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Theory
Graph Learning
- CausalArena introduces a unified benchmark for causal discovery, addressing inconsistencies in evaluation protocols.
- The benchmark includes synthetic, semantic operational, and formula-grounded SCMs to provide diverse evaluation environments.
- Experiments show substantial ranking shifts among methods across different SCM families, indicating the complexity of causal discovery evaluation.
- CausalArena supports the introduction of new causal environments as pretrained models evolve, ensuring relevance and adaptability.
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CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Summary
Causal discovery is essential for understanding causal relationships from data, yet its evaluation is inconsistent due to varying structural causal models (SCMs) and evaluation protocols. The introduction of causal discovery foundation models (CDFMs) complicates this further, as their performance may reflect both causal learning ability and the overlap between pretraining and evaluation environments. To address these challenges, the authors propose CausalArena, a comprehensive and adaptable benchmark for causal discovery. CausalArena includes three types of SCMs: synthetic, semantic operational, and formula-grounded, each serving distinct roles in evaluating causal discovery methods. The benchmark consists of 1,200 executable SCM specifications and evaluates various classical, neural, and pretrained methods under a standardized protocol. Experimental results reveal significant shifts in method rankings across different SCM families and evaluation protocols, emphasizing the need for benchmark diversity and the impact of pretraining on evaluation outcomes. CausalArena aims to provide a more reliable framework for assessing causal discovery methods in the evolving landscape of foundation models.
Methodology
CausalArena employs a unified evaluation framework that includes three families of SCMs: synthetic for controlled breadth, semantic operational for human-auditable environments, and formula-grounded for explicit scientific mechanisms. The benchmark evaluates various causal discovery methods using a common protocol, allowing for consistent comparisons across different models and settings.
Results
The experiments conducted with CausalArena reveal that the performance of causal discovery methods varies significantly across different SCM families and evaluation protocols. Strong performance in one benchmark does not guarantee similar results in another, highlighting the challenges posed by benchmark diversity and pretraining overlaps.
Implications
CausalArena provides a robust framework for evaluating causal discovery methods, which can enhance scientific reasoning and decision-making processes. Its adaptability ensures that it remains relevant as new causal environments and models emerge, potentially leading to improved methodologies in causal inference and related fields.
When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions
Graph Learning
- Introduces a hybrid estimator for connectivity loss in road networks using GNNs.
- Demonstrates the effectiveness of a spectral prior in improving graph learning under specific conditions.
- Finds that the benefits of the spectral prior are context-dependent and not universally superior.
- Highlights the limitations of using a residual prior in the presence of available training data.
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When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions
Summary
This paper investigates the effectiveness of using a spectral prior in graph learning, specifically for estimating connectivity loss in road networks following multiple edge deletions. The author proposes a hybrid estimator that combines exact spectral recomputation with local approximation, leveraging graph neural networks (GNNs) to learn corrections to first-order Fiedler sensitivity. The study evaluates the performance of this approach under various disruption scenarios, including independent, spatially clustered, and edge-betweenness-targeted failures, across 13 OpenStreetMap (OSM) networks in six countries. The results indicate that while the residual GCN model significantly improves mean absolute error (MAE) for spatial failures, the benefits are less pronounced for targeted failures. The findings suggest that the spectral prior can serve as a useful inductive bias in certain contexts, but its effectiveness may vary based on the nature of the disruptions and the availability of training data. The paper concludes by highlighting the limitations of the residual prior and its applicability to structural connectivity rather than other aspects of transport robustness.
Methodology
The methodology involves using graph neural networks (GNNs) to learn a bounded correction to the first-order Fiedler sensitivity for estimating the relative loss of algebraic connectivity after multi-edge deletions. The study employs various GNN architectures, including GCN, GraphSAGE, and edge-aware MPNN, and conducts experiments under different failure scenarios. Additionally, it utilizes area-clustered hierarchical bootstrapping for uncertainty estimation and performs zero-shot transfer across different OSM networks.
Results
The residual GCN model improved spatial-failure MAE by 0.0391, while the residual GraphSAGE model improved targeted-failure MAE by 0.0257. The second-order perturbation showed minimal improvement over first-order MAE, indicating that the residual prior's effectiveness varies with the type of disruption. The study also found that the residual prior is not uniformly superior when training data is available, suggesting a nuanced relationship between the spectral prior and graph learning performance.
Implications
The findings have implications for urban planning and infrastructure management, particularly in rapidly assessing the impact of road disruptions on connectivity. The results suggest that while spectral priors can enhance graph learning, their effectiveness is context-dependent, which may inform future research and applications in network resilience and robustness.
A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
Graph Learning
Time Series
Optimization
- Introduction of AmazonWSE, a dataset for WSE imputation covering 19K river sections in the Amazon basin.
- The dataset presents extreme sparsity, with less than 1% of sections observed daily, challenging existing imputation methods.
- A novel bidirectional selective state space model is proposed, outperforming traditional methods by leveraging subgraph sampling.
- The model achieves significant RMSE reductions compared to state-of-the-art methods, providing broader coverage and accuracy.
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A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
Summary
This paper addresses the challenge of monitoring water surface elevation (WSE) across river networks, which is crucial for flood forecasting and water resource management. The authors introduce AmazonWSE, a novel dataset that integrates satellite altimetry measurements from various sources, including the SWOT sensor, covering approximately 19,000 river sections in the Amazon basin over a decade (2016-2026). The dataset is characterized by extreme sparsity, with less than 1% of sections observed daily, presenting significant challenges for existing spatiotemporal graph imputation methods. The authors propose a simple bidirectional selective state space model that outperforms traditional methods by sampling connected subgraphs and utilizing topology-aware positional encodings. This model reduces the root mean square error (RMSE) against in situ gauges by 18-39% compared to the current state-of-the-art method, which relies on statistical and physical modeling, and it generates predictions for all river sections, regardless of satellite coverage. The findings highlight the limitations of existing graph neural networks in this context and suggest that better performance can be achieved through innovative sampling and encoding strategies rather than conventional graph convolutions.
Methodology
The authors developed a bidirectional selective state space model that flattens space and time into a single token sequence, incorporating topology-aware positional encodings. This approach allows for effective handling of the dataset's extreme sparsity by sampling connected subgraphs, rather than relying on full graph inputs typical of existing spatiotemporal graph neural networks.
Results
The proposed model demonstrated an RMSE reduction of 18-39% against in situ gauges compared to the best existing method for SWOT-based WSE densification. It successfully produced predictions for all river sections, overcoming the limitations of previous methods that only predicted for areas with sufficient satellite coverage.
Implications
The findings have significant implications for flood forecasting, water resource management, and the understanding of the global water cycle, particularly in sparsely monitored regions like the Amazon basin. The dataset and model can serve as benchmarks for future research in spatiotemporal graph imputation and satellite altimetry applications.
DR-LabStack: Design and Implementation of a Clinician-Facing Web System for Diabetic Retinopathy Prediction
Computer Vision
- DR-LabStack integrates multiple pretrained diabetic retinopathy prediction models into a single web interface.
- The system addresses the challenges of heterogeneous input requirements and preprocessing for different models.
- Functional evaluations confirmed the successful integration and operational consistency of the models.
- The design emphasizes a clinician-friendly interface while maintaining model-specific distinctions.
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DR-LabStack: Design and Implementation of a Clinician-Facing Web System for Diabetic Retinopathy Prediction
Summary
The paper presents DR-LabStack, a web system designed to facilitate diabetic retinopathy (DR) prediction by integrating four pretrained models: RuleFit, Pruned RuleFit, Elaborative XGBoost, and Two-level Ensemble. The authors address the challenges of varying input fields, serialization formats, and preprocessing requirements across these models, proposing a common clinical interface that ensures usability for clinicians. The system employs a React frontend and a Flask backend to manage model-specific features and preprocessing, while providing a unified response format for binary classification. Functional evaluations confirmed that all models were successfully integrated, with tests verifying input ordering, result rendering, and service behavior. The study emphasizes the importance of a reusable interaction and serving workflow for heterogeneous DR models, although it notes that clinical effectiveness and usability require further evaluation.
Methodology
The authors designed and implemented a web system using React for the frontend and Flask for the backend. They integrated four pretrained models, ensuring that the input features were correctly ordered and processed according to each model's requirements. Functional evaluations were conducted using real model artifacts in an isolated environment, with tests focusing on service behavior, input validation, and result rendering.
Results
The evaluation demonstrated that all four models were successfully loaded and exposed their respective input contracts. A total of 62 Flask test-client requests characterized the service behavior, while 12 limited-vector checks confirmed the consistency of invocation paths and thresholds. Additionally, 24 browser-component scenarios validated input ordering and result rendering, establishing the system's functionality.
Implications
DR-LabStack has the potential to streamline the process of diabetic retinopathy prediction in clinical settings by providing a unified interface for multiple models. This could enhance the accessibility of predictive analytics for clinicians, ultimately improving patient outcomes. Future evaluations could assess clinical effectiveness and usability in real-world scenarios.
Halo: Improving forecast accuracy through heteroscedastic estimation
Time Series
- Halo enhances point estimate accuracy in time series forecasting through heteroscedastic estimation.
- The method involves modifying existing deep learning architectures to include scale parameter estimation.
- Significant improvements in forecast accuracy were observed across multiple models and metrics.
- The approach is effective even with hyperparameters tuned for baseline point estimates.
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Halo: Improving forecast accuracy through heteroscedastic estimation
Summary
This paper introduces Halo, a novel approach to heteroscedastic forecasting that enhances the accuracy of point estimates in time series forecasting by estimating both scale and location parameters. Traditionally, heteroscedastic forecasting is primarily associated with uncertainty quantification; however, this study demonstrates that it can also improve the point estimates contrary to previous findings in non-time series contexts. Halo modifies existing deep learning architectures by adding a second output for the scale of the predicted distribution and training the model using matching negative log likelihood. The methodology is applied to three state-of-the-art models: a transformer, a graph network with a variational autoencoder, and a single-layer convolutional network, evaluated under both Gaussian and Laplacian losses. The results show significant improvements in forecast accuracy across five electricity price markets, with reductions in mean squared error (MSE) by 2.6% to 16.5% and mean absolute error (MAE) by 1.7% to 11.0% in 28 out of 30 comparisons. The findings suggest that the method of estimating scale is less critical than the act of estimating it, and that the improvements can be achieved without the need for hyperparameter retuning.
Methodology
The Halo technique modifies existing deep learning architectures by adding a second output for scale estimation, training under matching negative log likelihood. It evaluates three state-of-the-art models (transformer, graph network with variational autoencoder, and single-layer convolutional network) using both Gaussian and Laplacian losses.
Results
Halo improved MSE and MAE in 28 out of 30 model-market-metric comparisons, achieving average MSE reductions of 2.6% to 16.5% and average MAE reductions of 1.7% to 11.0%. The method's effectiveness was confirmed across various architectures and loss functions.
Implications
The findings suggest that incorporating heteroscedastic estimation can be a valuable strategy for enhancing forecast accuracy in time series analysis, potentially leading to better decision-making in fields such as finance and energy markets.
RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Time Series
- RDDMPI reformulates MTSI as a baseline-residual decomposition, separating deterministic reconstruction from probabilistic uncertainty modeling.
- The framework operates in residual space, allowing for focused correction of systematic errors.
- A reliability-aware conditioning mechanism is introduced to manage the influence of baseline predictions adaptively.
- RDDMPI shows improved performance over existing methods in reconstruction accuracy and uncertainty estimation.
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RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Summary
The paper introduces RDDMPI, a novel framework for multivariate time series imputation (MTSI) that addresses the challenges of recovering missing values in interdependent temporal data. Traditional methods often struggle with the complexity of modeling both temporal dynamics and cross-variable dependencies. RDDMPI innovatively reformulates the imputation task into a baseline-residual decomposition, where a pretrained model captures the dominant signal and a diffusion process focuses on modeling the residual uncertainty. This approach simplifies the generative task by allowing the diffusion model to concentrate on correcting systematic errors rather than reconstructing the entire signal. Additionally, RDDMPI employs a reliability-aware conditioning mechanism to adaptively control the influence of baseline predictions, enhancing the robustness of the imputation process. Experiments on benchmark datasets demonstrate that RDDMPI consistently outperforms existing methods in terms of reconstruction accuracy and uncertainty quantification, showcasing its effectiveness in real-world applications such as healthcare and traffic monitoring.
Methodology
RDDMPI utilizes a two-stage process: first, a deterministic imputer generates a baseline-completed signal and a structured latent representation. Second, a conditional diffusion model is trained in residual space, focusing on generating correction terms for missing values. The diffusion process is conditioned on both the baseline signal and its latent representation, with an adaptive mechanism to modulate the influence of the baseline based on its reliability.
Results
The experiments conducted on multiple benchmark datasets indicate that RDDMPI significantly enhances both the accuracy of the reconstructed time series and the quality of uncertainty quantification compared to existing imputation methods.
Implications
The proposed framework has significant implications for fields that rely on accurate multivariate time series data, such as healthcare monitoring, traffic systems, and energy management, where missing data can severely impact decision-making and system performance.
Phase-Decoupled, Model-Calibrated Power Control for Disaggregated LLM Serving
Large Language Models
Optimization
Efficient ML
- Optimal power settings depend on the specific model and hardware combination, not just GPU class.
- Disaggregated architectures allow for distinct power profiles for prefill and decode phases.
- The proposed model-calibrated controller outperforms traditional static profiles in efficiency and latency management.
- Dynamic calibration can convert latency headroom into energy savings effectively.
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Phase-Decoupled, Model-Calibrated Power Control for Disaggregated LLM Serving
Summary
This paper addresses the challenge of optimizing GPU power consumption in datacenter environments for Large Language Model (LLM) serving, particularly under disaggregated architectures. The authors propose a novel phase-decoupled, model-calibrated power control mechanism that adjusts power settings based on the specific model, quantization, engine, and hardware combination being used. They argue that traditional static power profiles are inadequate as they do not account for the distinct operational characteristics of the prefill and decode phases of LLM serving. The proposed controller allows for dynamic adjustments, ensuring that each lane (prefill and decode) operates under optimal conditions. Experimental results demonstrate that this approach significantly improves efficiency, achieving up to 20.4% more tokens per joule while maintaining latency guarantees. The findings suggest that tailored power management strategies can lead to substantial energy savings and enhanced performance in LLM serving environments.
Methodology
The authors developed a phase-decoupled, model-calibrated power control mechanism that dynamically adjusts power settings based on real-time measurements of the deployed model and hardware. They conducted experiments on an 8ร B200 node serving various LLMs, comparing their approach against traditional NVIDIA Max-Q profiles and other static power management strategies.
Results
The proposed power control mechanism achieved a 20.4% increase in tokens per joule with only a 3.5% increase in mean end-to-end latency compared to the Max-Q profile, which had a modest 8.6% efficiency gain at a higher latency cost. The new approach consistently met latency SLOs, outperforming static profiles in multiple tests and demonstrating significant energy savings over sustained operations.
Implications
The findings suggest that adopting model-calibrated power control strategies in datacenter environments can lead to more efficient LLM serving, reducing energy consumption while maintaining performance. This has broader implications for the design of power management systems in high-performance computing and AI workloads.
Flow Duality and Source Geometry for Categorical Generation
Generative Models
Theory
NLP
- Establishes a duality between continuous and discrete flow matching for categorical generation.
- Demonstrates how different source geometries affect transition timing and vocabulary size dependence.
- Derives discrete interpolation behavior for Gaussian, bounded-uniform, and centered negative-exponential sources.
- Highlights the significance of continuous source distribution as a design choice in generative modeling.
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Flow Duality and Source Geometry for Categorical Generation
Summary
This paper explores the duality between continuous and discrete flow matching in generative modeling, particularly for categorical data. It establishes that projecting continuous convex-interpolant paths with one-hot targets through a position-wise argmax results in discrete convex-interpolant paths. The findings hinge on the properties of source distributions, specifically their coordinate symmetry and boundary regularity. The author derives the discrete interpolation behavior for various source types, including Gaussian, bounded-uniform, and centered negative-exponential sources, revealing that different geometries of source distributions lead to distinct transition timings and dependencies on vocabulary size. The paper also includes visual diagnostics and preliminary language modeling experiments that suggest these source-design effects may influence learned transports and generative quality. Overall, the work emphasizes the importance of the continuous source distribution as a design choice in categorical generation, highlighting how it affects the competition among target and source coordinates during the generation process.
Methodology
The paper employs theoretical analysis to derive the duality between continuous and discrete flows, focusing on convex interpolants. It examines the properties of source distributions and their effects on the induced discrete coefficients. The methodology includes mathematical proofs and derivations, supported by visualizations of toy trajectories to illustrate the concepts.
Results
The main results indicate that under specific conditions, the projected process from continuous flows yields discrete flows with convex interpolants. The derived coefficients for different source families reveal that the Gaussian source leads to delayed coefficients as vocabulary size increases, while bounded uniform and centered negative-exponential sources exhibit distinct behaviors. The visual diagnostics suggest that the choice of source can qualitatively impact the generative process.
Implications
The findings have implications for the design of generative models, particularly in applications involving discrete data such as language and symbolic sequences. Understanding the duality and source geometry can inform better model architectures and training strategies, potentially improving generative quality and efficiency.
Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study
Computer Vision
NLP
Multimodal
- Combining synthetic and real data significantly improves OCR accuracy for low-resource languages.
- Real training images raise the performance of VLMs to 95.09-96.28% accuracy, compared to 87.92% for synthetic-only models.
- Joint and sequential training methods yield similar results in archival accuracy.
- A compact CRNN achieves high performance when real images are included, indicating model scale is not the sole determinant of accuracy.
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Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study
Summary
This study addresses the challenge of optical character recognition (OCR) for the critically endangered Manchu language, which has a rich historical archive that remains difficult to analyze due to its low-resource status. Previous research demonstrated that vision-language models (VLMs) trained solely on synthetic Manchu word images achieved an accuracy of 87.4% on real historical documents, indicating a significant synthetic-to-real gap. The authors investigate how to effectively combine synthetic and real training data to improve OCR performance. Utilizing a dataset of 60,000 synthetic and 20,306 real historical word images, they evaluate three pretrained VLMs and a compact convolutional recurrent neural network (CRNN) across four training regimes: synthetic-only, real-only, joint synthetic-real, and sequential synthetic-to-real training. The results reveal that incorporating real training images significantly enhances performance, with accuracies ranging from 95.09% to 96.28% for configurations that include real data, while synthetic-only configurations do not exceed 87.92%. The study also finds that joint and sequential training methods yield similar results, and that a compact CRNN can achieve high accuracy when real images are included. Furthermore, complementary errors among models allow for a voting mechanism that boosts accuracy to 98.27% without additional training, demonstrating the potential for improved OCR in low-resource settings.
Methodology
The authors employed a dataset comprising 60,000 synthetic and 20,306 real historical Manchu word images. They evaluated three pretrained VLMs and a compact CRNN under four training regimes: synthetic-only, real-only, joint synthetic-real, and sequential synthetic-to-real training. The evaluation followed a common checkpoint-selection and archival evaluation protocol.
Results
The study found that all leading configurations that included real training images achieved word accuracy between 95.09% and 96.28%. In contrast, no synthetic-only configuration surpassed 87.92%. The incorporation of synthetic data improved the performance of VLMs, while the CRNN's performance was sensitive to the training objective. Voting among the best-performing models raised accuracy to 98.27%.
Implications
The findings suggest that combining synthetic and real data can effectively enhance OCR capabilities for low-resource languages, which may facilitate better access to historical documents and support computational studies in linguistics and history.
Conformal Calibration Transfer
Theory
Multimodal
Efficient ML
- Introduces Transported Conformal Calibration (TCC) for transferring calibration from source to target space.
- Utilizes unlabeled paired observations to transport labeled calibration effectively.
- Offers two correction methods: TCC-KS for conservative adjustments and weighted-TCC for efficiency.
- Demonstrates reliable target-domain coverage transfer across multiple datasets without labeled target data.
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Conformal Calibration Transfer
Summary
This paper addresses the challenge of conformal prediction when labeled calibration data is only available in a source space, while predictions are needed in a target space. The authors propose a novel framework called Transported Conformal Calibration (TCC), which utilizes unlabeled paired observations to transport labeled calibration from the source space to the target space. TCC corrects residual mismatches through two methods: TCC-KS, which employs a label-free uncertainty surrogate to detect discrepancies, and weighted-TCC, which reweights transported calibration based on the target domain. The paper provides finite-sample coverage guarantees that adapt to observable measures of mismatch. Empirical results demonstrate that TCC achieves reliable target-domain coverage transfer across various datasets, including CIFAR-100-C, Tiny-ImageNet-C, and SEN12MS, without requiring labeled target calibration data. The proposed diagnostics can predict when corrections are necessary, enhancing the robustness of the calibration transfer process.
Methodology
The methodology involves transporting labeled calibration data from a source space to a target space using a learned transport map from unlabeled paired observations. The TCC framework includes two main correction mechanisms: TCC-KS, which uses a label-free uncertainty surrogate to measure discrepancies, and weighted-TCC, which reweights the transported calibration based on density ratios estimated from unlabeled samples in the target space.
Results
The empirical evaluation shows that TCC maintains reliable target-domain coverage across datasets with significant corruptions, achieving coverage guarantees without the need for labeled target calibration data. The methods demonstrate effectiveness in adapting to the residual mismatch between transported and true target distributions.
Implications
The findings suggest that TCC can be applied in scenarios where labeled calibration data is scarce or costly to obtain, such as in sensor transitions or modality changes. This approach can enhance the deployment of machine learning models in real-world applications by ensuring robust predictions even in the absence of extensive labeled data.
Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Multimodal
Time Series
Large Language Models
- Introduction of SolCloudLLM, a multimodal framework for solar forecasting.
- Effective integration of sky images and time-series data enhances forecasting accuracy.
- Achieved a maximum relative MSE reduction of 25.4% on the SIRTA dataset.
- Demonstrated superior performance in few-shot learning scenarios.
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Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Summary
This paper presents SolCloudLLM, a novel multimodal forecasting framework that integrates sky images and time-series data for short-term solar power and global horizontal irradiance (GHI) forecasting. The authors highlight the limitations of traditional forecasting methods that rely solely on historical numerical data, particularly in the context of rapidly changing weather conditions caused by clouds. By incorporating ground-based sky imagery, which provides a forward-looking context, the framework aims to enhance forecasting accuracy. SolCloudLLM employs a bidirectional multimodal fusion approach, aligning sky-image patches with time-series patches and fusing their representations before mapping them into the embedding space of a large language model (LLM). Extensive experiments on the SIRTA and SKIPPโD datasets demonstrate that SolCloudLLM significantly outperforms baseline methods in Mean Squared Error (MSE) across various forecasting horizons, achieving a maximum relative MSE reduction of 25.4% on the SIRTA dataset. The results indicate that the benefits of multimodal fusion are particularly pronounced under cloudy conditions and in few-shot settings, where the model maintains high performance with limited training data.
Methodology
The methodology involves a bidirectional multimodal fusion approach where sky imagery frames are encoded using a lightweight CNN. The resulting visual features are temporally aligned with time-series patches. Instead of simple concatenation, a token-level Bidirectional Multimodal Fusion module is employed, allowing each modality to influence the other dynamically. This enables the model to interpret numerical data in the context of current cloud states and vice versa, enhancing the overall forecasting capability.
Results
SolCloudLLM consistently outperformed baseline methods in Mean Squared Error (MSE) across all tested forecasting horizons (16, 32, 64 minutes). The maximum relative MSE reduction achieved was 25.4% on the SIRTA dataset, with stratified analysis indicating that the advantages of multimodal fusion are particularly evident under cloudy conditions at specific forecasting horizons.
Implications
The findings suggest that integrating visual data with numerical time-series can significantly improve the accuracy of solar power forecasting, which is crucial for effective grid operations and energy management. This approach could be applied to other domains where multimodal data can enhance predictive performance.
Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Theory
- ERM and any proper consistent learner are shown to be relatively smart for binary classification.
- A semi-supervised relatively smart learner can achieve quadratic blowup only in unlabeled sample complexity.
- The proposed learner generalizes the One-Inclusion Graph to a leave-most-out transductive problem.
- The trade-off between efficiency and tractability is highlighted, with implications for oracle calls in semi-supervised learning.
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Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Summary
This paper continues the exploration of relatively smart learning, which was initially introduced by Dughmi and Pour. The authors investigate whether Empirical Risk Minimization (ERM) or other natural learners can achieve comparable guarantees to the One-Inclusion Graph (OIG) learner, which has a quadratic blowup in sample complexity. The authors demonstrate that ERM, as well as any proper consistent learner, is relatively smart for binary classification in a distribution-free setting, showing that a small certifiable error with m samples leads to a small error on a uniform distribution over a sample size of O(mยฒ). This establishes that a cover of size at most 2m+1 can control the error of consistent learners with O(mยฒ) samples. Furthermore, the paper addresses the semi-supervised learning aspect, revealing that a semi-supervised relatively smart learner exists with only a quadratic blowup in unlabeled sample complexity, while labeled sample complexity remains unaffected. This learner is based on a generalization of the OIG to a leave-most-out transductive problem. However, the authors also prove that this efficiency in labeled sample complexity comes at the cost of simplicity and tractability, as any semi-supervised relatively smart learner with significantly sub-quadratic blowup in labeled sample complexity must make super-polynomially many oracle calls, even when the marginal distribution is known.
Methodology
The authors utilize theoretical proofs to establish the properties of ERM and the proposed semi-supervised learner, focusing on the relationship between certifiable errors and sample complexity. They analyze the implications of these findings in the context of distribution-fixed learning and the challenges posed by indistinguishability phenomena in unlabeled data.
Results
The paper confirms that ERM is relatively smart with a quadratic blowup in sample complexity and introduces a semi-supervised learner that maintains efficiency in unlabeled sample complexity. However, it also establishes that achieving this efficiency requires significant complexity in terms of oracle calls.
Implications
The findings suggest that while relatively smart learning can be achieved with simpler learners like ERM, the trade-offs in complexity and tractability must be carefully considered, especially in practical applications of semi-supervised learning where oracle access is limited.
MUtE: A Dual Framework for Concept Erasure and Counterfactual Interventions
NLP
Large Language Models
Interpretability
- Introduction of MUtE*, a class of optimal erasure functions that defines a dual counterfactual mapping.
- Development of a computationally efficient implementation that utilizes a translational bias for erasure and counterfactual generation.
- Empirical validation shows significant improvements in bias mitigation and counterfactual text generation.
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MUtE: A Dual Framework for Concept Erasure and Counterfactual Interventions
Summary
The paper presents MUtE, a novel framework designed for concept erasure and counterfactual interventions in continuous representation spaces. The primary goal is to erase specific concept-related information from representations while preserving unrelated information, thereby enhancing algorithmic fairness and interpretability in natural language processing (NLP) tasks. The author revisits the optimal bounds of concept erasure and introduces a class of erasure functions that facilitate a deterministic dual counterfactual mapping. This duality allows for seamless transitions between erasing concepts and generating counterfactual representations. The framework is grounded in theoretical optimality and is implemented with a translational bias that aligns with the geometric nature of concepts in modern language models. Empirical evaluations demonstrate the framework's effectiveness in improving fairness and generating coherent counterfactual texts across various NLP benchmarks.
Methodology
The methodology involves defining optimal erasure functions that maximize utility while ensuring minimal information retention about the target concept. The framework employs a translational bias on counterfactual trajectories, allowing for effective manipulation of representations in continuous spaces. Theoretical foundations are established to support the dual mapping between erasure and counterfactual generation, and empirical tests are conducted on synthetic data and NLP benchmarks.
Results
The results indicate that the MUtE framework successfully enhances algorithmic fairness by effectively erasing concept-specific information while retaining essential utility. Additionally, it demonstrates the capability to generate coherent counterfactual texts, validating its practical applicability in real-world NLP tasks.
Implications
The implications of this work extend to various applications in NLP, particularly in addressing biases in language models and improving interpretability. The framework can be utilized in developing more equitable AI systems by mitigating the influence of sensitive demographic attributes in model outputs.
Phases in a class of associative memories via hidden neurons
Theory
- Introduces a bipartite architecture for associative memory that integrates hidden neurons as an order parameter.
- Analyzes retrieval dynamics under polynomial and exponential loads using statistical mechanics methods.
- Identifies distinct retrieval phases and their statistical behaviors based on the type of visible neurons.
- Demonstrates the role of Lagrangians in fixing stability and storage capacity in associative memory models.
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Phases in a class of associative memories via hidden neurons
Summary
This paper investigates the phases of associative memory in a bipartite architecture, termed class H, which combines elements from Krotov and Hopfield networks. The authors analyze how hidden neurons can serve as an order parameter for memory retrieval, exploring both polynomial and exponential load scenarios. Utilizing the replica method, they derive phase diagrams and storage capacities for models with Ising and spherical visible neurons. The study reveals that at polynomial loads, the retrieval mechanisms exhibit central-limit behavior, while at exponential loads, large-deviation statistics dominate. The hidden layer's softmax representation leads to distinct retrieval phases characterized as paramagnetic, condensed, and frozen. The findings suggest that the stability of retrieval is influenced by the visible Lagrangian, while the hidden Lagrangian determines the storage scale, providing insights into the design of new Lagrangians for associative memory systems.
Methodology
The authors employ the replica method from statistical mechanics to analyze the phase diagrams and storage capacities of different models within the class H architecture. They explore the dynamics of visible and hidden neurons, using Lagrangians to govern their interactions and retrieval processes.
Results
The study finds that the class H architecture allows for a clear distinction between retrieval phases based on the type of visible neurons and the nature of hidden neuron interactions. At polynomial loads, the retrieval is stable and follows central-limit statistics, while at exponential loads, retrieval becomes destabilized and follows large-deviation statistics. The hidden layer's softmax representation leads to a richer set of retrieval dynamics, including paramagnetic, condensed, and frozen phases.
Implications
The findings have implications for the design of associative memory systems and neural networks, particularly in understanding how different architectures can optimize memory retrieval and storage. The insights into the roles of visible and hidden Lagrangians could guide future research in developing more efficient memory models and potentially enhance applications in machine learning tasks that require content-addressable memory.
GEOSTEER: Geodesic Optimization for Activation Steering in Large Language Models
NLP
Large Language Models
Optimization
- GEOSTEER formulates activation steering as a Riemannian optimization problem.
- It replaces fixed one-step updates with adaptive, multi-step geodesic updates.
- The method improves stability and expressiveness of steering while preserving activation norms.
- Experiments show consistent performance improvements over existing activation steering baselines.
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GEOSTEER: Geodesic Optimization for Activation Steering in Large Language Models
Summary
The paper introduces GEOSTEER, an innovative method for norm-preserving activation steering in large language models (LLMs). Activation steering is a technique that modifies hidden activations during inference to control model behavior without altering model parameters. Traditional methods face limitations due to predefined steering trajectories and reliance on one-step updates, which can fail to capture the complex structure of activation distributions. GEOSTEER addresses these issues by formulating the steering process as a Riemannian optimization problem, allowing for a sequence of small geodesic updates on the representation manifold. This approach enables adaptive steering based on a nonlinear activation-space objective that distinguishes between desired and undesired activations. The results demonstrate that GEOSTEER provides smoother, more stable, and consistent steering behavior while maintaining the activation norm. Experiments conducted on benchmarks such as TruthfulQA, RealToxicityPrompts, and UltraFeedback show that GEOSTEER consistently outperforms existing state-of-the-art activation steering methods, suggesting that adaptive, geometry-aware optimization can enhance norm-preserving steering effectiveness.
Methodology
GEOSTEER employs Riemannian optimization to guide the activation steering process. It decomposes the steering into a series of small geodesic updates based on a nonlinear objective function that differentiates between desired and undesired activations. This allows for adaptive steering that captures the complex structure of activation distributions.
Results
The experiments on TruthfulQA, UltraFeedback, and RealToxicityPrompts indicate that GEOSTEER significantly outperforms state-of-the-art activation steering methods, demonstrating its effectiveness in achieving norm-preserving steering with improved stability and expressiveness.
Implications
The findings suggest that GEOSTEER can be a valuable tool for controlling large language models, potentially leading to better alignment with desired behaviors and reduced risks of harmful outputs such as bias and toxicity. This method could be applied in various applications where LLM behavior needs to be managed without extensive retraining.
Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Theory
- Hierarchical clustering can satisfy Kleinberg's axioms of scale invariance, richness, and consistency, unlike flat clustering methods.
- The authors construct several admissible hierarchical clustering methods, demonstrating the existence of uncountably many such methods.
- A partial order exists among admissible methods, revealing substantial diversity and the absence of a greatest method.
- Every admissible method contains a common backbone of sufficiently well-separated clusters.
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Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Summary
This paper addresses the limitations of flat clustering methods as outlined by Kleinberg's Impossibility Theorem, which states that no flat clustering method can satisfy the three axioms of scale invariance, richness, and consistency simultaneously. The authors investigate whether these axioms can be satisfied in the context of hierarchical clustering. They demonstrate that, unlike flat clustering, hierarchical clustering can indeed satisfy these axioms jointly. The paper introduces several admissible hierarchical clustering methods, including those based on well-separated clusters and a non-binary version of single linkage. The authors explore the structure of these methods under a refinement order, revealing a rich diversity of admissible methods with no greatest element and many maximal elements. They also establish a backbone property, indicating that all admissible methods share a common structure of sufficiently well-separated clusters. Furthermore, the paper discusses the concept of exactness on ultrametrics, showing that strongly admissible methods can recover canonical hierarchical structures from ultrametric inputs. The findings suggest that hierarchical clustering can provide a more nuanced understanding of data structure compared to flat clustering approaches.
Methodology
The authors formulate hierarchical analogs of the axioms of scale invariance, richness, and consistency, and impose permutation invariance as a symmetry requirement. They construct various admissible hierarchical clustering methods and analyze their relationships through a refinement order.
Results
The main results indicate that hierarchical clustering methods can jointly satisfy the three axioms, leading to a diverse set of admissible methods. The study reveals a backbone property shared among these methods and establishes the existence of strongly admissible methods that can recover hierarchies from ultrametric inputs.
Implications
The findings have significant implications for clustering in unsupervised learning, suggesting that hierarchical methods can provide richer representations of data structures. This could enhance applications in various fields where understanding the relationships within data is crucial.
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Generative Models
Computer Vision
Interpretability
- Introduction of counterfactual marginalisation as a test-time evaluation procedure for medical image classifiers.
- Development of new evaluation metrics that assess model robustness to nuisance variables without requiring disease labels.
- Demonstration of the framework's ability to expose biases in predictions better than traditional metrics.
- Emphasis on the importance of evaluating model sensitivity, stability, and worst-case performance in clinical settings.
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Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Summary
This paper introduces a novel framework called counterfactual (CF) marginalisation for evaluating the robustness of classification models, particularly in medical image analysis, against nuisance variables such as demographic attributes. The authors argue that while deep learning models may achieve high accuracy, they often rely on shortcuts related to demographic factors that can lead to biased predictions. The proposed framework utilizes a CF image generator to create counterfactual versions of test images by intervening on nuisance variables, allowing for the assessment of model predictions while marginalising these effects. The authors define several intervention-aware evaluation metrics, including counterfactual marginal risk, interventional expected risk, counterfactual stability, and worst-case counterfactual risk. These metrics aim to provide a more nuanced understanding of model performance, particularly regarding sensitivity to demographic changes. The paper demonstrates that this approach can reveal biases in predictors more effectively than traditional evaluation metrics, thus enhancing the robustness evaluation of medical image classifiers.
Methodology
The methodology involves using a Structural Causal Model (SCM)-based counterfactual image generator to create CF versions of test images by intervening on nuisance variables like age and sex. The authors then compute various metrics based on these counterfactuals, including counterfactual marginal risk and counterfactual stability, to evaluate the robustness of the model's predictions.
Results
The results indicate that the counterfactual marginalisation framework effectively identifies biases in medical image classifiers that traditional evaluation methods may overlook. The proposed metrics provide insights into the sensitivity and stability of model predictions under demographic interventions, highlighting the potential for improved clinical decision-making.
Implications
The implications of this work are significant for the field of medical image analysis, as it offers a systematic approach to evaluate and enhance the robustness of machine learning models against demographic biases. This could lead to more equitable and clinically relevant AI applications in healthcare.
Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs
Graph Learning
- Reification transforms facts into nodes, enabling a shared vocabulary across datasets.
- Five standard GNNs can achieve zero-shot transfer to multiple benchmarks using the proposed representation.
- The GAT model matches the performance of ULTRA, a specialized foundation model, in several evaluations.
- The approach is applicable to relational databases, showcasing its broad utility.
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Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs
Summary
This paper presents a novel approach to zero-shot link prediction in knowledge graphs by reifying the input graph, transforming every fact into a node connected to its subject, object, and relation type through a fixed vocabulary of six meta-relations. This method allows for a shared representation across different datasets, enabling five standard Graph Neural Networks (GNNs) to achieve zero-shot transfer to 40 inductive link-prediction benchmarks. The best-performing GNN, GAT, matches the performance of ULTRA, a dedicated foundation model, across various evaluation metrics. Additionally, the proposed representation extends to relational databases, demonstrating its versatility. The findings suggest that a fixed structural representation can facilitate significant zero-shot inductive transfer without the need for specialized architectures, thus simplifying the transfer mechanism in graph-based models.
Methodology
The authors reify the input graph by converting facts into nodes and connecting them through a fixed set of meta-relations. They then train five standard GNN architectures on a single knowledge graph and evaluate their performance on 40 inductive link-prediction benchmarks, as well as on two unseen relational databases.
Results
The GAT model achieved an average score of 0.3565 ยฑ 0.0049 across 40 benchmarks, closely matching ULTRA's score of 0.3732. In specific benchmarks, GAT outperformed ULTRA, indicating strong zero-shot transfer capabilities. The preliminary probe on relational databases showed that the model could rank foreign-key targets significantly above random initialization and degree controls.
Implications
This work suggests that a fixed structural representation can simplify the design of models for link prediction tasks, potentially leading to more efficient and versatile graph-based learning systems. The findings may influence future research on knowledge graphs and relational databases, promoting the development of models that can generalize across different data structures without extensive retraining.