AI-generated summaries

Today's ML research,
without the noise.

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

41 Papers today
8h Update frequency
7 Days of history
Correlation-Guided Fast Machine Unlearning via Hessian Analysis
Ayushi Thakur, Ruchir Gupta, Amit Kumar Jaiswal, Prayag Tiwari
Efficient ML Theory
  • Introduces a computationally efficient unlearning framework using Pearson correlation.
  • Derives a closed-form parameter update rule that eliminates costly Hessian computations.
  • Establishes theoretical guarantees and error bounds for the proposed method.
  • Demonstrates significant speedup and improved accuracy compared to existing unlearning techniques.
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Attention Quantization for Tabular Foundation Models
Jonas M. Kübler, Benjamin Jäger, Klemens Flöge, Noah Hollmann, Frank Hutter
Efficient ML
  • Focus on attention calculation quantization rather than weight quantization for tabular models.
  • Development of a quantization strategy for queries, keys, and values to FP8 format.
  • Achieved up to 1.7x speedup in attention calculations with minimal accuracy loss.
  • Importance of aligning quantization errors between training and testing datasets.
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ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
Kaixuan Du, Xin Wan, YuKun Wang, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, Ni Li
Multimodal
  • ProactiveBench evaluates streaming video models at one-second intervals without explicit response cues.
  • The framework includes six subtasks that assess different aspects of response timing and trigger clarity.
  • Premature responses are identified as the predominant error in the evaluated systems.
  • The study reveals a substantial gap in the temporal decision-making required for human-like interaction.
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Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma, Mira Mezini
Large Language Models Reinforcement Learning Efficient ML
  • Offline RL can significantly improve the performance of code-generating LLMs without online sampling.
  • The study reveals that model performance is sensitive to learning rates and training epochs.
  • Prolonged training can lead to model collapse, necessitating early stopping strategies.
  • Logit variance is identified as a key source of instability in offline RL training.
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Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Erdem Koyuncu
Theory Efficient ML
  • Establishes a concentration theorem for one-shot magnitude pruning in single neurons.
  • Introduces the conditional perceptron model for adaptive early exit with proven generalization error decay.
  • Characterizes the accumulation of pruning distortions in deep networks and derives compute-accuracy tradeoffs.
  • Provides numerical simulations that support theoretical predictions regarding compute reduction mechanisms.
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Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models
Zhendong Mi, Shaoyi Huang
NLP Large Language Models Reinforcement Learning
  • DF-Sample is a training-free framework that enhances reasoning accuracy in LLMs.
  • The method constructs a hierarchical reasoning tree and evaluates global trajectories instead of making local decisions.
  • DF-Sample consistently outperforms traditional sampling strategies and RL-trained models across various benchmarks.
  • The findings suggest that high-quality reasoning paths are latent in base models and can be accessed without additional training.
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On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health
Ibukunoluwa Soyebo, Alyssa Donawa, Rodrigo Aguilar Barrios, Brice Patchou, Corey E. Baker
NLP Large Language Models Multimodal
  • ODLMs can effectively predict stress using multimodal data without cloud dependency.
  • Objective sensor data marginally outperforms subjective self-reports in stress prediction.
  • Lightweight models (sub-2B parameters) provide low latency and efficient resource usage.
  • Zero-shot prompting strategies enhance the flexibility of stress prediction models.
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Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Yuxin Xiao, Sheng Zhang, Chandan Singh, Tristan Naumann, Hoifung Poon, Jianfeng Gao, Xiaodong Liu
Reinforcement Learning Generative Models Time Series
  • Proposes RL fine-tuning for EHR foundation models to enhance clinical reasoning.
  • Introduces time-aware, rollout-sensitive rewards for optimizing patient trajectory generation.
  • Demonstrates that smaller models can outperform larger pre-trained models in data-limited settings.
  • Shows positive transfer across multiple clinical reasoning tasks through multi-task RL.
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Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
Gyeolhee Lee, Moosun Kim, Taewook Kwon, Jaehun Kim, Dongjin Lee
Time Series
  • Introduces a multifidelity approach for railway-bogie response prediction.
  • Combines low-fidelity simulation data with high-fidelity experimental measurements.
  • Utilizes a time-delay neural network (TDNN) and a residual-correction network.
  • Achieves significant accuracy in response predictions with a mean coefficient of determination of 0.8197.
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TokenMapper: A Step Toward Interoperable Speech Token Translation
Tal Kozakov, Tal Rosenwein, Eliya Nachmani
Audio & Speech
  • TokenMapper enables direct token-to-token translation, eliminating the need for waveform decoding and re-encoding.
  • The framework handles structural mismatches between different token spaces, including single and multi-codebook representations.
  • Empirical results show that TokenMapper preserves intelligibility across different tokenizers with minimal WER.
  • Significant reductions in latency (up to 94.5%) are achieved compared to conventional methods.
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Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
Yeonjae Jung, Minwoo Shin
Time Series Computer Vision
  • Introduction of Observation-Anchored Selective Assimilation (OASA) for tumor-state proxy forecasting.
  • Utilization of longitudinal MRI data to enhance patient-specific tumor-state estimates.
  • Comparison of OASA with multiple forecasting methods, showing competitive performance.
  • Demonstration of improved calibration metrics alongside high Dice scores.
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Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent
Jamie Haddock, Anna Ma, Elizaveta Rebrova
Optimization Theory
  • Introduction of Quantile-k-Loss SGD (QkL-SGD) for robust optimization against outliers.
  • Linear convergence guarantees under convexity assumptions with sample size scaling based on corruptions.
  • Probabilistic analysis for small sample sizes, linking convergence to outlier selection probability.
  • Experimental results demonstrate superior performance of QkL-SGD compared to standard SGD and min-k-loss SGD.
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SCOPE-OPSD: Fisher-Conditioned Privileged Subspaces for On-Policy Self-Distillation
Yunmeng Chen, Kunyu Wang, Peihan Li, Yi Wang, Shuyin Xia, Yi Liu, Xinyong Cheng, Dehui Wang, Xiangyong Zhai, Yanxing Liu, Song Liu
NLP Large Language Models Theory
  • Introduces a final-layer privileged-residual objective that enhances OPSD without additional rollouts.
  • Utilizes a Fisher-conditioned rank-64 subspace to isolate the effects of privileged information.
  • Demonstrates consistent performance improvements across multiple model scales and checkpoints.
  • Provides empirical evidence supporting the effectiveness of structured versus random orientations in training.
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Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Zihan Zhu, Zhehang Du, Xuyang Chen, Tim Tsz-Kit Lau, Jiayuan Wu, X. Y. Han, Qi Long, Weijie Su
NLP Large Language Models Optimization
  • Effective rank utilization is crucial for optimizing LoRA adaptations.
  • Different optimizers can significantly impact the effective rank achieved during training.
  • Iso-LoRA optimizes LoRA updates by focusing on tangent-space updates, improving rank utilization.
  • The proposed method shows substantial performance gains across various language models.
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SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling
Junquan Gu, Shibo Cui, Xiangfeng Luo, Hang Yu
Large Language Models Optimization Interpretability
  • SAGE-Loop introduces a closed-loop trial-and-correction mechanism for AutoML, enhancing reliability.
  • The framework utilizes LLMs for adaptive model generation and ensemble strategies.
  • Validation on 20 datasets shows consistent performance improvements across various tasks.
  • SAGE-Loop can automatically repair execution failures, ensuring reliable run completion.
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Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise
Amartya Mukherjee, Jun Liu
Optimization Theory
  • SGD with clipping and additive noise converges almost surely under specific conditions.
  • Clipping introduces bias, but the conditional mean of the clipped gradient remains a descent direction if the clipping threshold is sufficiently large.
  • The analysis extends to momentum variants, providing similar convergence guarantees.
  • The results suggest stability of SGD-CN in both convex and nonconvex optimization problems.
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A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Amine Andam, Jamal Bentahar, Mustapha Hedabou
Reinforcement Learning Optimization Theory
  • Unification of regularization-based methods for robust DRL through new performance gap bounds.
  • Introduction of a constrained optimization framework for robust training that adapts regularization weight.
  • Empirical validation of theoretical analysis through extensive adversarial evaluations.
  • Demonstration of improved robustness when combining different regularization techniques.
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ParaRecover: A Process-Level Benchmark for Error Localization and Recovery in Parallel Tool-Use Agents
Bowen Guan, Zhentao Yin, Yanming Shen
NLP Large Language Models Reinforcement Learning
  • Introduction of ParaRecover, a benchmark for evaluating error localization and recovery in parallel tool-use agents.
  • Development of a fine-grained error taxonomy covering 14 error types relevant to multi-turn parallel execution.
  • Proposal of the SDE rubric for detailed assessment of agent behavior during error recovery.
  • Experiments reveal significant weaknesses in current state-of-the-art models regarding error propagation and recovery.
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Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion
Chen Min, Haowen Jiang, Zheng Ma, Xiongbin Yan
Generative Models Optimization Theory
  • PSG effectively combines physical velocity with a learned geological prior to enhance FWI.
  • The method preserves conventional initialization and optimization history while improving reconstruction accuracy.
  • PSG outperforms classical and diffusion-based baselines in various acquisition scenarios.
  • Ensemble variability in results is concentrated near geological interfaces, indicating a relationship with inversion errors.
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Benign Loss Landscapes Can Coexist with Worst-Case Hardness
Zach Furman, Stephan Wäldchen, Yangda Bei, Liam Hodgkinson
Theory Optimization
  • TTNs can express arbitrary read-once Boolean formulas, including hard-to-learn targets.
  • All minimum-norm local minima in TTNs are global minima, indicating benign loss landscapes.
  • Learning difficulty in TTNs is attributed to high-order degenerate saddle points caused by rank-deficiency.
  • The paper provides a case study on the parity function to illustrate the relationship between landscape geometry and computational hardness.
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Temporal Recurrence Favors Fewer Layers
Ivan Anokhin, Johan Obando-Ceron, Irina Rish, Sebastian Risi
Reinforcement Learning NLP Efficient ML
  • Temporal recurrence allows for fewer layers in recurrent models while achieving comparable or better performance.
  • The study formulates the problem as a compute-allocation challenge, comparing recurrent and non-recurrent models.
  • Increasing parallel capacity benefits both model types, but gains from additional layers saturate earlier with recurrence.
  • The findings suggest a broader hypothesis that temporal recurrence may favor shallower updates in other streaming contexts.
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DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Wenrui Xu, Anas Enanaa, Keshab K. Parhi
Time Series
  • Introduction of DCRA framework leveraging forward diffusion for structured corruption in time-series learning.
  • Feature-level consistency mechanism enhances representation alignment between clean and corrupted signals.
  • DCRA shows improved robustness and sensitivity in seizure detection tasks on the CHB-MIT EEG dataset.
  • Framework is encoder-agnostic, compatible with various model architectures.
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Dual-guided Hierarchical Edge Localization for Large-scale Optimal Transport Across Dimensions
Wenzhou Xia, Qiaoqiao Ding, Jingwei Liang, Xiaoqun Zhang
Optimization Efficient ML Theory
  • HELLO offers a scalable solution for large-scale discrete optimal transport problems.
  • The dual-guided approach enhances both initialization and refinement processes.
  • Achieves significant runtime improvements and lower transport objectives at large scales.
  • Proven finite termination at a global optimum under specific conditions.
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Write on Paper and Get the Online Digital Trace: A New Era for Handwriting
Florent Imbert, Yann Soullard, Eric Anquetil, Tanja Harbaum, Alexey Serdyuk, Fabian Kress, Tim Hamann, Peter Kampf
Robotics Efficient ML Multimodal
  • Introduction of Digipen, a digital pen that captures handwriting on regular paper.
  • Combination of hardware and software innovations to reconstruct handwriting traces using inertial sensors.
  • Utilization of deep learning algorithms for real-time handwriting trace reconstruction.
  • Focus on enhancing learning experiences by allowing handwriting on paper while capturing digital traces.
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Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration
Yuning Wang, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg
NLP Large Language Models Multimodal
  • Introduction of the Concept-Integrated Transformer (CIT) framework for explainable health predictions.
  • Utilization of LLM for annotation-free concept abnormality supervision.
  • High performance on two longitudinal datasets, achieving F1 scores of 0.756 and 0.765.
  • Demonstration of interpretable behavioral patterns through learned concept scores.
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MAxBench: A Multinomial Concept Recovery Benchmark
Divya Appapogu, Freya Behrens, Yonatan Belinkov, Aaron Mueller
NLP Large Language Models Interpretability
  • Introduces MAxBench, a framework for evaluating multinomial concept representations.
  • Demonstrates that affine subspaces outperform other geometries in terms of reliability and recall.
  • Finds that better non-zero offsets contribute significantly to the advantages of affine subspaces.
  • Shows that manifold steering can be competitive but does not consistently outperform prompting.
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DynSHAP: Towards Explainable Dynamic Survival Analysis
Nastasya Anokhina, Jonas Jürß, Pietro Liò
Time Series Interpretability
  • DynSHAP extends SHAP to dynamic survival analysis by discretizing time into time-feature pairs.
  • Temporal DynSHAP improves the recovery of temporally dependent features compared to traditional methods.
  • The framework is validated on both synthetic and real-world clinical datasets, demonstrating practical applicability.
  • DynSHAP provides insights into the timing of feature influence on survival predictions, enhancing interpretability.
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Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Zhenghong Huang, Hongfan Wu, Jiheng Zhang
Optimization Large Language Models Efficient ML
  • Introduces a fixed-pool routing problem for quantized MoE instances with a focus on quality constraints.
  • Develops Fragility-Weighted Perplexity (FWP) as a predictive metric for request-specific risk.
  • Demonstrates that FWP allocation significantly improves throughput compared to static and request-agnostic methods.
  • Establishes a two-timescale framework separating provisioning decisions from routing actions.
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FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Tyler Farnan, Benjamin Eng, Adam Abate, Xirui Hou, Rizal Fathony, Nam H. Nguyen, Senthil Kumar
Multimodal Time Series
  • FINESSE provides a novel agent-based model for generating synthetic, structured financial event sequences.
  • The benchmark dataset, FINESSE-Bench, supports four representative financial tasks.
  • Baseline results are reported using state-of-the-art methods, highlighting the effectiveness of the framework.
  • The simulator addresses privacy concerns by generating data independent of sensitive information.
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Certifying Concept Unlearning in Text-to-Image Diffusion Models
Mansi, Luca Marzari, Francesco Leofante
Generative Models Computer Vision Theory
  • Introduces a certification framework for concept unlearning in T2I diffusion models.
  • Demonstrates that existing ASR metrics do not reliably bound residual leakage.
  • Establishes upper bounds on leakage probabilities across a continuous concept neighborhood.
  • Evaluates the framework on NSFW content, artistic styles, and celebrity identities.
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GSF-χ: Global Stereochemical Fields for Chiral Graph Transformers
Jiaqing Xie, Yuxin Wang, Xipeng Qiu
Graph Learning
  • GSF-χ introduces a global stereochemical field that modulates pairwise interactions among all atoms.
  • The Chiral-RoPE operator allows for reflection-invariant attention mechanisms that respect molecular chirality.
  • The model achieves significant improvements in accuracy for chirality classification and ECD predictions.
  • Extensive validation through property tests confirms the model's robustness and adherence to chirality principles.
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Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Ben Opperman, Eduardo Alonso, Esther Mondragón
Reinforcement Learning
  • Introduction of a groupoid-based framework for reinforcement learning that captures local symmetries.
  • Dynamic discovery of equivalence structures allows for more efficient learning in complex environments.
  • Empirical results show improved sample efficiency and convergence compared to traditional Q-learning.
  • The framework supports local generalization without requiring global symmetry, enhancing robustness.
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Reinforcement Learning for Syndrome Extraction
John Zhuoyang Ye, Aarav Pabla, Jens Palsberg
Reinforcement Learning Optimization Theory
  • Introduces FastSched, a reinforcement learning-based tool for syndrome extraction in quantum error correction.
  • Achieves significant reductions in logical error rates compared to state-of-the-art tools.
  • Combines quality and scalability in schedule synthesis, addressing limitations of previous methods.
  • Utilizes importance sampling for efficient evaluation of schedules.
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Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning
Ting Xu, Henry Leung
Federated Learning
  • Introduction of the Fed-Equilibrium framework to address knowledge dominance in clinical federated learning.
  • Implementation of a two-stage gradient control cascade for enhanced robustness and fairness.
  • Validation through bi-national simulations shows effective representation of minority clinical data.
  • Demonstrates that minority nodes can achieve convergence similar to larger hubs, promoting equitable learning.
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Decoding Mixture Perception through Computational Modeling of Component Interactions
Fei Wang, Xiaoya Xie, Junfei Liu, Huihao Wang, Yixiao Wang, Yintao Wang, Yi Li, Hao Dong, Xing Chen
Robotics
  • Proposed a deep learning framework for accurate odor perception recognition of multi-molecule mixtures.
  • Developed a fusion strategy that integrates multi-receptor and multi-molecule response curves.
  • Achieved a recognition accuracy of 92.2% in identifying complex odor perceptions.
  • Established a computational pathway from chemical blending to neural encoding and perceptual formation.
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PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability
Vipul Bansal, Himanshu Buckchash, Balasubramanian Raman, Deepak Dhungana
Theory
  • Introduction of the CREDibility Score (CREDS) for OOD prediction.
  • Development of credibility curves and heat maps to analyze model behavior.
  • Demonstration of CREDS as a valid measure of model trustworthiness.
  • Ability to evaluate model performance without the need for OOD datasets.
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GUIDE: Generative Utility Inference and Decision Engine
Anagha Tiwari, Alexander G. Gray, Nick Feamster, Brian Jabarian, Alex Imas, Alex Kale
NLP Large Language Models Optimization
  • GUIDE integrates Bayesian adaptive sampling with symbolic representation learning for effective preference elicitation.
  • The architecture allows for diverse question types and produces interpretable domain-specific preference models.
  • In silico experiments show GUIDE outperforms existing methods in cold-start scenarios and minimizes recommendation regret.
  • The framework enhances transparency and expert oversight in the elicitation process.
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Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri, Joseph Broach
Optimization
  • Developed a machine learning framework for estimating pedestrian volumes from GIS data.
  • Improved upon traditional Negative Binomial GLM by incorporating feature selection and gradient boosting.
  • Achieved significant reductions in RMSE compared to the baseline model.
  • Released code on GitHub for public access and further research.
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Information-Induced Training Geometry: Exact Reduction, Canonical Completion, and Structured Expressivity
Zavier Li
Optimization Theory Efficient ML
  • Establishes a framework for understanding how training data constrains optimizer geometry.
  • Introduces a unique completion for full-column-rank SPD channels under Riemannian geometry.
  • Derives a closed-form pair metric that separates visible-metric motion from subspace rotation.
  • Characterizes exact reduction and structured expressivity in the context of finite-dimensional AIRM models.
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A Full Adam Theorem for Spectral Heavy-Tail Onset
Zongmin Liu
Theory Optimization
  • Establishes a full Adam theorem for spectral heavy-tail onset in a closed state-evolution model.
  • Derives population gradients using Stein-Hermite calculus and proves covariance concentration.
  • Demonstrates the conversion of Adam momentum into a non-centered Gaussian sign kernel.
  • Proves approximate-target KL contraction with upper and lower hitting bounds.
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Robust Policy Optimization via Adversarial Importance Sampling
Amine Andam, Jamal Bentahar, Mustapha Hedabou
Reinforcement Learning Optimization Robotics
  • Introduction of Adversarial Importance Sampling (Advis) for robust policy optimization.
  • Development of advrl, a modular library for implementing and evaluating robustness methods.
  • Emphasis on the need for diverse adversarial configurations in robustness evaluation.
  • Demonstration of the effectiveness of the proposed method in continuous control environments.
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