AI-generated summaries

Today's ML research,
without the noise.

Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.

48 Papers today
8h Update frequency
7 Days of history
Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models
Heqiang Qi, Wei Huang, Mingyuan Bai, Xiangming Meng
NLP Large Language Models Generative Models
  • Introduction of confidence-induced clusters (CICs) as span-level update units for MDLMs.
  • Development of CLAD, a training-free cluster-level decoder that enhances parallel decoding.
  • Utilization of self-attention maps to model inter-cluster dependencies and ensure compatibility.
  • Demonstrated significant speed improvements over existing token-level decoding methods.
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The Hamilton-Jacobi Theory of Deep Learning
Jose Marie Antonio Miñoza, Erika Fille T. Legara, Christopher P. Monterola
Theory Optimization Interpretability
  • Training neural networks corresponds to solving Hamilton-Jacobi initial-value problems.
  • Log-sum-exp activation functions are smooth deformations of tropical max operations.
  • A single parameter ε connects various perspectives on neural networks, including tropical algebra and PDEs.
  • The framework provides actionable design principles for optimizing neural network architectures.
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How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions
Jeff A. Bilmes, Gantavya Bhatt, Arnav M. Das
Theory Optimization Efficient ML
  • Dataset value is influenced by factors beyond size and compute budget.
  • The Vendi Score and neural scaling laws are shown to be submodular.
  • Matrix spectral functions provide a broader framework for dataset appraisal.
  • A new optimization method yields a 35,000× speedup for maximizing the Vendi Score.
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The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu, K. Selçuk Candan
Theory Graph Learning Efficient ML
  • Restricting regressors to the Markov boundary can improve prediction, especially in larger and sparser feature spaces.
  • Causal discovery methods often fail to provide a useful boundary for prediction due to computational constraints and misalignment of objectives.
  • The exact Markov boundary is not the only effective feature set; alternative sets can also yield better performance than using all features.
  • The study highlights the trade-offs between minimality, sufficiency, and scalability in feature selection for tabular prediction.
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OISD: On-Policy Internal Self-Distillation of Language Models
Xinyu Liu, Darryl Cherian Jacob, Yang Zhou, Jindong Wang, Pan He
NLP Large Language Models Reinforcement Learning
  • Introduction of On-Policy Internal Self-Distillation (OISD) for language models.
  • Utilizes the final layer as an internal teacher to guide intermediate layers.
  • Employs logit and attention alignment mechanisms for effective knowledge transfer.
  • Demonstrates substantial improvements in reasoning tasks over strong RL baselines.
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The Long-Term Effects of Data Selection in LLM Fine-Tuning
Yuxin Yang, Aoxiong Zeng, Xiangquan Yang
NLP Large Language Models Theory
  • Introduces the concept of myopic selection in LLM fine-tuning, highlighting the trade-off between short-term gains and long-term adaptability.
  • Develops a unified multi-stage evaluation protocol for comparing various data selection strategies.
  • Demonstrates through experiments that short-term effective selectors can hinder future learning and increase forgetting.
  • Proposes the Long-Horizon Aware Selection (LHAS) objective to improve long-term adaptation and robustness.
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RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood
Yifu Zheng
Reinforcement Learning NLP Large Language Models
  • RL2ML connects standard reinforcement learning, maximum-likelihood training, and beyond-maximum-likelihood objectives.
  • Introduces a closed-form unbiased gradient estimator for finite-rollout surrogate objectives.
  • Identifies a subcritical-supercritical update-scale transition that influences the effectiveness of surrogate objectives.
  • Demonstrates that the optimal choice of surrogate objective depends on evaluation metrics, local sensitivity, and estimator variance.
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Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf, Samuel Tovey, Christian Holm
Theory Optimization
  • Introduction of spectral position as a scalable measure of eigenvalue contributions to loss reduction.
  • Larger models achieve lower losses by accessing weak spectral signals in the eNTK spectrum.
  • Feature learning is identified as a key enabler of spectral reach, amplifying gradients during training.
  • The study provides a framework for understanding the dynamics of scaling in large-scale neural networks.
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UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
Yi Liu, Hongji Zhang, Lei Chen, Mingxuan Yuan, Qiang Xu
Multimodal Graph Learning
  • UniRTL integrates RTL code and CDFG for enhanced representation learning.
  • The framework employs mutual masked modeling for fine-grained cross-modal alignment.
  • A hierarchical training strategy is utilized to maximize data utilization.
  • UniRTL outperforms existing methods in performance prediction and code retrieval tasks.
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Generalized Intention Modeling in Multi-Agent Reinforcement Learning
Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar, Amanda Prorok
Reinforcement Learning
  • Introduction of a task-adaptive opponent modeling framework that combines multiple intent representations.
  • Development of reward-predictive intention embeddings that enhance the ego-agent's understanding of opponent impact on returns.
  • Demonstration of improved performance stability and robustness compared to traditional single-component modeling methods.
  • Insights into the varying effectiveness of opponent modeling strategies across different environments.
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Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
Ruoran Xu, Borong She, Xiaobo Jin, Qiufeng Wang
Optimization Theory
  • Introduction of Singularity-aware Adam (S-Adam) to address issues in non-smooth optimization.
  • Development of the Local Geometric Instability (LGI) metric for estimating instability in loss landscapes.
  • Adaptive damping mechanism that modulates step sizes based on local geometric conditions.
  • Rigorous convergence guarantees established through differential inclusions.
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Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
Jun Hu
Theory Generative Models Efficient ML
  • Introduction of CerT-MCMC framework for learned-transport MCMC with convergence certificates.
  • Development of two complementary certificates: covering certificate and quantile-core certificate.
  • Quantile-core certificate provides non-vacuous spectral-gap bounds in high dimensions.
  • Demonstrated effectiveness on various datasets, including synthetic and real-world applications.
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Improving Relative Representations with Learned Anchors and Whitened Inner Products
Oscar Thorsted Svendsen, Nikolaj Holst Jakobsen, Fabian Mager, Hiba Nassar
Multimodal
  • Introduces learned anchors as robust semantic prototypes for improved relative representations.
  • Utilizes a geometry-aware similarity metric that preserves magnitude information and is invariant to affine transformations.
  • Demonstrates significant performance gains in cross-model communication across vision and language tasks.
  • Enables stable zero-shot communication between models of varying scales and architectures.
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Automating Formal Verification with Reinforcement Learning and Recursive Inference
Max Tan
Reinforcement Learning Large Language Models Theory
  • Introduces RLVR to improve formal verification in LLMs.
  • Achieves a verified reward increase from 2.2% to 58.1% using RLVR.
  • Identifies and addresses specification hacking in model outputs.
  • Develops a verifier-guided inference scaffold that improves proof generation success rates.
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Solving Integer Linear Programming with Parallel Tempering
Kyuil Sim, Sanghyeok Choi, Jinkyoo Park
Optimization
  • Introduces a solver-free, sampling-based approach for ILP optimization.
  • Utilizes Locally-Balanced Proposal and Parallel Tempering to explore discrete feasible regions.
  • Demonstrates superior performance compared to SCIP and competitive results against Gurobi.
  • Shows robustness to distribution shifts compared to learning-based methods.
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Test Time Training for Supervised Causal Learning
Zizhen Deng, Jiaru Zhang, Rui Ding, Huang Bojun, Jinzhuo Wang, Qiang Fu, Shi Han, Dongmei Zhang
Graph Learning Theory Efficient ML
  • Identifies critical limitations in existing Supervised Causal Learning methods.
  • Introduces TTT-SCL, a framework for dynamic training set generation at test time.
  • Establishes a theoretical basis connecting TTT-SCL with score-based methods.
  • Demonstrates significant performance improvements across various datasets.
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On Distributional Reinforcement Learning in Chaotic Dynamical Systems
James Rudd-Jones, Mirco Musolesi, María Pérez-Ortiz
Reinforcement Learning Theory Optimization
  • Distributional RL objectives are smoother than expectation-based objectives in chaotic systems.
  • Return distributions under mild statistical stability assumptions are Lipschitz continuous in the 1-Wasserstein metric.
  • Empirical analysis shows that distributional objectives lead to smoother loss landscapes and lower variance targets.
  • Distributional Q-learning methods outperform non-distributional approaches in chaotic control experiments.
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FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
Zijie Zhao, Roy E. Welsch
Interpretability
  • FlagGAM provides a rule-defined basis framework for GAM-style tabular prediction.
  • It extends rule construction to handle both numerical and categorical features across classification and regression tasks.
  • The framework retains a sparse rule-basis matrix, allowing for feature-specific weighting and flexible prediction heads.
  • FlagGAM demonstrates competitive performance against modern additive models and tree-based methods, especially under challenging data conditions.
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Apertus LLM Family Expansion via Distillation and Quantization
Andrei Panferov, Davit Melikidze, Martin Jaggi, Dan Alistarh
Large Language Models Efficient ML NLP
  • Introduction of the Apertus-v1.1 model family through distillation and quantization.
  • Demonstration of cost-effective model expansion without the need for extensive pre-training.
  • Validation of pre-training distillation as a method to enhance model performance with fewer resources.
  • Exploration of quantization techniques to optimize models for various hardware constraints.
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Learning Multi-Agent Coordination via Sheaf-ADMM
Jeffrey Seely, Bartłomiej Cupiał, Llion Jones
Optimization Graph Learning Robotics
  • Introduces Sheaf-ADMM, a framework for multi-agent coordination using differentiable optimization.
  • Utilizes cellular sheaf theory to define inter-agent constraints for heterogeneous global consensus.
  • Demonstrates improved robustness and performance in tasks like MNIST classification and Sudoku solving.
  • Enables distinct analysis of coordination dynamics through the separation of state variables.
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Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger, Kjell Jorner, Andreas Krause
Generative Models Optimization
  • Introduction of a formal framework for Constrained Generative Optimization.
  • Development of the Constrained Flow Optimization (CFO) algorithm for balancing reward maximization and constraint satisfaction.
  • CFO provides convergence guarantees for constrained generative optimization.
  • Experimental results show consistent improvements in reward and constraint satisfaction.
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LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
Jintao Li, Yong-Yi Wang, Zheng-An Wang, Heng Fan
Graph Learning Optimization Efficient ML
  • LoRe introduces a per-step operator budgeting framework for iterative graph solvers.
  • The method dynamically routes computation to high-conflict interactions, improving efficiency.
  • LoRe achieves a 15× speedup and 44× memory reduction on the TSP problem.
  • The framework is a drop-in solution that does not require retraining of existing models.
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IRIS: time-structured manifold projections
Brian Ondov, Chia-Hsuan Chang, Weipeng Zhou, Xingjian Zhang, Xueqing Peng, Yutong Xie, Huan He, Qiaozhu Mei, Hua Xu
Time Series Optimization Theory
  • IRIS integrates time-structured layouts with manifold learning, enhancing the visualization of dynamic biomedical data.
  • The algorithm operates in two phases: optimizing radial distances for timestamps and adjusting angular positions for high-dimensional structure.
  • Evaluation across multiple datasets shows IRIS outperforms UMAP in representing temporal relationships while retaining class structure.
  • The method is open-source, promoting accessibility and further research in the field.
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Revisiting Zeroth-Order Hessian Approximation: A Single-Step Policy Optimization Lens
Junbin Qiu, Zhaowei Hong, Renzhe Xu, Yao Shu
Optimization Theory Efficient ML
  • Introduces a unified framework for ZO Hessian approximation using single-step Policy Optimization.
  • Presents ZoVH, a comprehensive suite of variance-reduced Hessian estimators.
  • Establishes theoretical guarantees for the unbiasedness and variance optimality of the proposed methods.
  • Demonstrates significant improvements in estimation accuracy and convergence performance in empirical evaluations.
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BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies
Anya Singh, Cabrel Happi, Jai Relan, Varun Nair, Vidyut Baradwaj
Robotics Theory Multimodal
  • BOKBO is the first conformal abstention layer for K-sample VLA inference, providing safety guarantees.
  • The method achieves high reliability and task success rates while addressing silent failures in traditional K-sampling.
  • A critical analysis reveals that existing nonconformity scores fail to measure policy uncertainty accurately.
  • The introduction of a learned violation predictor improves safety calibration significantly.
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CalArena: A Large-Scale Post-Hoc Calibration Benchmark
Eugène Berta, David Holzmüller, Francis Bach, Michael I. Jordan
Computer Vision Theory Optimization
  • Introduction of a large-scale benchmark for post-hoc calibration covering diverse tasks and models.
  • Standardized evaluation framework for comparing dozens of calibration methods.
  • Post-Hoc Improvement (PHI) proposed as a new metric for assessing calibration quality.
  • Empirical results show that smooth calibration functions are superior to binning methods.
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Reducing the GPU Memory Bottleneck with Lossless Compression for ML -- Extended
Aditya K Kamath, Arvind Krishnamurthy, Marco Canini, Simon Peter
Efficient ML Graph Learning Large Language Models
  • Introduces Invariant Bit Packing (IBP), a lossless compression algorithm tailored for ML workloads.
  • IBP achieves significant performance improvements, including 74% faster GNN training and 180% faster DLRM embedding lookup.
  • The method minimizes GPU memory usage while avoiding the accuracy trade-offs associated with lossy compression.
  • Provides easy-to-use APIs for integration into existing ML frameworks.
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Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
Santanu Das, Sagnik Chatterjee, Jatin Batra
Theory Efficient ML Optimization
  • Introduces a robust recovery algorithm for Gaussian Single Index Models with non-monotonic link functions.
  • Establishes the existence of a convex basin in the loss landscape that aids in robust recovery.
  • Demonstrates efficient convergence to low estimation error under adversarial conditions.
  • Fills a significant gap in robust statistics literature for non-monotonic link functions.
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CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu, Roderik M. Kortlever, Gerard I. Evan, David L. Bentley, Mihaela van der Schaar
Graph Learning Time Series Interpretability
  • CellBRIDGE augments Optimal Transport with interaction-aware costs derived from ligand-receptor signaling.
  • The method improves trajectory inference by explicitly modeling cell-cell communication.
  • CellBRIDGE enables interpretable in silico perturbations that align with expected biological outcomes.
  • The approach shows broad applicability across various trajectory inference frameworks.
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The Sample Complexity of Multiclass and Sparse Contextual Bandits
Liad Erez, Fan Chen, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran, Alexander Rakhlin
Theory Reinforcement Learning Efficient ML
  • Introduces improved sample complexity bounds for contextual bandits with sparse rewards.
  • Establishes algorithms that achieve ε-optimal policies with significantly reduced dependence on the number of actions.
  • Bridges a gap in existing literature by providing tight bounds that are minimax optimal up to logarithmic factors.
  • Utilizes two complementary approaches: DEC-based exploration and low-variance exploration techniques.
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Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
Yujia Guo, Mikhail Kabeshov, Tat Hong Duong Le, Samuel Genheden, Marco V. Mijangos, Varvara Voinarvoska, Giulia Bergonzini, Ola Engkvist, Samuel Kaski
Interpretability
  • Introduces an expert-augmented framework combining machine learning with chemists' expertise for route evaluation.
  • Utilizes a DeepSets-based model trained on tree edit distances and fine-tuned with expert evaluations.
  • Achieves significant improvements in scoring accuracy and interpretability compared to existing methods.
  • Provides a dual-output evaluation system that aligns with real-world decision-making in synthesis planning.
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Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions
Zixuan Song, Uwe Mueller, Dimitris V. Manatakis
Optimization Graph Learning Interpretability
  • COAST provides a principled framework for designing interventions that induce state transitions in complex systems.
  • The framework integrates causal discovery and multi-objective optimization to balance efficacy, complexity, and stability of interventions.
  • COAST is modular and domain-agnostic, making it applicable across various fields, particularly in biomedicine.
  • The approach successfully identifies causal drivers and robust intervention strategies from both synthetic and real datasets.
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SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction
Arunkumar Ramachandran
Time Series
  • Introduces a novel temporal CWT-LSTM architecture for ICU alarm classification.
  • Achieves a mean AUC of 0.822, significantly outperforming static classification methods.
  • Demonstrates the importance of temporal chunking and multi-channel signal fusion.
  • Identifies specific alarm types that are easier or harder to classify.
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Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach
James Sargant, Seyedeh Ava Razi Razavi, Renata Dividino, Sheridan Houghten
Generative Models Graph Learning Optimization
  • Combines WGANs with Genetic Algorithms for graph generation refinement.
  • Addresses structural deviations in generated graphs compared to real data.
  • Implements evolutionary edge editing to optimize graph connectivity.
  • Demonstrates improved alignment with real graph statistical properties.
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Towards Continuous-time Causal Foundation Models
Dennis Thumm, Ruben Wiedemann, Ying Chen
Time Series
  • Introduces a continuity criterion for continuous-time causal priors based on trajectory-law invariance.
  • Develops a three-tier taxonomy for categorizing causal priors in time series analysis.
  • Demonstrates that fine-grid integration outperforms naive integration in empirical tests.
  • Proposes a construction for continuous-time causal models using OU processes and random DAGs.
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PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
Junru Zhang, Lang Feng, Jinbo Wang, Xu Guo, Yucheng Wang, Han Yu, Min Wu, Yabo Dong, Duanqing Xu
Generative Models Time Series
  • PrismFlow addresses mode collapse in time-series generation by using a bank of Koopman-inspired experts.
  • The method employs a Winner-Take-All training objective to promote expert specialization and reduce averaging effects.
  • PrismFlow achieves state-of-the-art performance with significant improvements in key evaluation metrics.
  • The approach is robust in low-data settings and effective for various time-series tasks, including forecasting and imputation.
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When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
Vahideh Zolfaghari
NLP Large Language Models Interpretability
  • Demonstrates that synthetic dishonesty can be rapidly induced in language models through supervised fine-tuning.
  • Linear representations of dishonesty are highly detectable, achieving near-perfect AUC in most models evaluated.
  • Probes trained on one domain (TruthfulQA) generalize effectively to diverse reasoning tasks (MMLU) with minimal performance loss.
  • Identifies two architectural regimes in models regarding their handling of dishonesty: collapse-type and high-dimensional models.
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Momentum Based Reward Design for Low Emission Traffic Signal Control
Chinmay Mundane, Amith Manoharan, Arun Singh
Reinforcement Learning Optimization
  • Introduction of a Momentum-Based Reward Function (MBRF) that promotes continuous vehicle movement.
  • Evaluation of the MBRF in SUMO shows better performance than traditional delay and queue-based rewards.
  • The proposed method leads to improved throughput-emission trade-offs and more stable learning behaviors.
  • Demonstrates the effectiveness of DRL in adaptive traffic signal control without requiring major infrastructure changes.
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Non-destructive Identification of Oyster Species is possible from Hyperspectral Images with Machine Learning
Ethan Kane Waters, Max Wingfield, Aiden Mellor, Paul Stewart, Iman Tahmasbian
Computer Vision
  • Hyperspectral imaging can non-destructively differentiate between oyster species.
  • PLS-DA outperformed CNN in classification accuracy for oyster species identification.
  • Distinct elemental compositions were found between Black-Lip and Sydney rock oysters.
  • The methodology has potential applications in aquaculture for species traceability and broodstock management.
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MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
Dang Hong Nguyen, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham
NLP Efficient ML Optimization
  • Introduction of MIC framework for optimizing multi-granular embeddings.
  • Development of Soft Collapse Regularization to manage redundancy in nested subspaces.
  • Implementation of Spectral Isotropy Regularization for ensuring uniformity in embeddings.
  • Demonstrated significant performance improvements over existing MRL baselines.
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How's it going? Reinforcement learning in language models recruits a functional welfare axis
Andy Q Han, David J. Chalmers, Pavel Izmailov
NLP Large Language Models Reinforcement Learning
  • Reinforcement learning recruits a pre-existing representation of functional welfare in language models.
  • The study demonstrates that punishment and reward vectors behave as representations of negative and positive welfare, respectively.
  • The effects of these vectors are robust across various training conditions and persist even in pre-trained models.
  • The functional welfare axis influences model behavior in unrelated domains, indicating a generalization of learned representations.
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De-attribute to Forget for LLM Unlearning
Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim, See-Kiong Ng, Anthony Kum Hoe Tung, Bryan Kian Hsiang Low
NLP Large Language Models Reinforcement Learning
  • Introduces a novel data de-attribution objective for LLM unlearning.
  • Presents DareU, the first LLM unlearning framework utilizing reinforcement learning.
  • Demonstrates effective unlearning while preserving model utility.
  • Outperforms existing LLM unlearning methods in empirical evaluations.
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Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
Xabier Belaunzaran, Antonio Nappa, Arkaitz Artetxe, Basilio Sierra
Time Series
  • Introduces a hybrid prognostic framework for RUL estimation that incorporates uncertainty quantification.
  • Utilizes a bifurcated approach to classify engine states into healthy and degraded regimes.
  • Employs an LSTM-based autoencoder for state classification and a Conditional Weibull model for RUL estimation.
  • Generates continuous state probabilities for improved prediction accuracy and uncertainty characterization.
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Retriever Portfolios: A Principled Approach to Adaptive RAG
Miltiadis Stouras, Vincent Cohen-Addad, Silvio Lattanzi, Ola Svensson
NLP Large Language Models Optimization
  • Introduces retriever portfolio optimization to enhance RAG systems by selecting diverse retrievers for heterogeneous queries.
  • Formalizes an expected best-of-k objective to evaluate retriever portfolios, ensuring coverage of different query types.
  • Demonstrates that fixed portfolios can achieve comparable or better accuracy with lower latency than adaptive hyperparameter tuning methods.
  • Empirical results show significant improvements in retrieval recall and answer accuracy across multiple QA benchmarks.
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MAAT: Multi-phase Adapter-Aware Targeted Unlearning
Suryash Yagnik, Shubham Gaur, Saksham Thakur, Vinija Jain, Aman Chadha, Amitava Das
NLP Large Language Models Theory
  • Introduction of 5WBENCH, a benchmark that quantifies causal unlearning failures.
  • MAAT framework achieves high forgetting and retention of Why-type causal knowledge.
  • Demonstrates the challenges of gradient dilution and multi-hop reasoning in unlearning.
  • Outperforms existing methods on the forget-retain tradeoff across multiple models.
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Scaling Higher-Order Graph Learning with Maximal Clique Complexes
Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Graph Learning
  • Introduction of sCWL and fCWL tests that preserve expressivity while improving scalability.
  • Development of the maximal clique complex for efficient higher-order graph representation.
  • CliqueWalk method for sampling maximal cliques, enabling linear scaling with graph size.
  • Competitive performance on classification benchmarks compared to existing GNNs.
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Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
Shervin Khalafi, Alejandro Ribeiro, Dongsheng Ding
Generative Models Optimization Theory
  • Introduces a principled constrained optimization framework for unlearning in diffusion models.
  • Formulates three optimization problems based on KL divergences and likelihood constraints.
  • Establishes strong duality for the proposed problems, enabling effective solution characterization.
  • Demonstrates superior performance of KL-constrained methods over traditional weight-based approaches.
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Early Prediction of Future Behavioral Strategy from Process Traces
Robert Kasumba, Dennis Barbour, Chien-Ju Ho
Reinforcement Learning Time Series Robotics
  • The paper formulates early cross-task behavioral strategy prediction as a relevant problem.
  • Introduction of the Process-Level Latent Variable Model (PLVM) for fusing task-specific traces.
  • PLVM outperforms traditional outcome-based models and single-task models in predicting behavior.
  • Controlled simulations validate the effectiveness of PLVM in recovering behavioral phenotypes.
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