📄 Paper Explainer

Recent Papers from arXiv

• arXiv:2610.32971
  RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
  Xi Chen, Fengqi Hu, Zeyu Shen, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
• arXiv:2610.32951
  Where Rounding Happens Matters: Rounding-Location Alignment Stabilizes LLM Reinforcement Learning
  Yijing Ke, Tianle Cai, Ruosong Wang
  Machine Learning (cs.LG)
• arXiv:2610.32926
  ZENO: Nonlinear filtering with Zakai Equation Neural Operators
  Aaditya Prasad, Steven W. Flavell, Brian DePasquale
  Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
• arXiv:2610.32914
  A Tutorial on Physics Constrained Machine Learning for Hybrid Modeling, Optimization, and Control
  Rahul Golder, Bimol Nath Roy, MM Faruque Hasan
  Machine Learning (cs.LG); Emerging Technologies (cs.ET)
• arXiv:2610.32910
  Recommender regret for improving regularised RL estimation with bandits and MCTS
  Danilo Vucetic, Alexandre Larouche, Mohammad Pedramfar, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32887
  Reinforcement Learning with Multi-Step Transition Predictions: A Bellman Theory
  Chenbei Lu, Zaiwei Chen, Tongxin Li, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32866
  Interpretable sparse fine-tuning with TopKLoRA enables faithful circuit discovery
  Marek Masiak, Andrzej Szablewski, Nicola Cancedda, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• arXiv:2610.32864
  PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets
  Joao P. A. Dantas, Andre N. Costa, Diego Geraldo, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• arXiv:2610.32859
  Sustainability of TSFM Deployment and Pretraining: From Carbon Footprint Analysis to Mitigation via Pruning
  Cécile Luc, Nathanaël Lemonnier, Ievgen Redko
  Machine Learning (cs.LG)
• arXiv:2610.32855
  Regularised RL improves sim-to-real transfer
  Danilo Vucetic, Mohamad H. Danesh, Florence Clerc, et al.
  Machine Learning (cs.LG); Robotics (cs.RO)
• arXiv:2610.32853
  Near-Optimal List Replicability from Littlestone Dimension
  Ari Blondal, Pooya Hatami, Chavdar Lalov, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32849
  Neural LoFi with Backward Coupling: A Spectral Theory of Cross-Layer Feature Learning
  Yatin Dandi, Matteo Vilucchio, Hugo Tabanelli, et al.
  Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (stat.ML)
• arXiv:2610.32822
  On the Limits of Sparse Linear Model with Shuffled Differential Privacy
  Yan Zhang, Jiong Tuo, Meng Ding, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32811
  U-NeXt: General-purpose event detection for neonatal EEG
  Robert Hogan, Sean R. Mathieson, Aurel Luca, et al.
  Machine Learning (cs.LG); Signal Processing (eess.SP); Medical Physics (physics.med-ph)
• arXiv:2610.32801
  Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
  M. Harper Langston, Pierre-David Letourneau, Dalton Jones, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Numerical Analysis (math.NA); Optimization and Control (math.OC)
• arXiv:2610.32799
  CARES: Coverage-Aware Replay Exemplar Selection for Continual Learning
  Arundhati S. Shanbhag, Brian B. Moser, Federico Raue, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32774
  Deep Learning-Based Prediction of HER2 Status from Whole-Brain MRI in Breast Cancer Brain Metastases
  Sara Early, Chris Kang, Vibujithan Vigneshwaran, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32772
  Direction-Aware Token Credit Assignment with Reward-Model Gradients
  Yikuan Hu, Jifeng Zhu, Minle Su, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32748
  On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
  Yubo Ye, Maryam Toloubidokhti, Sumeet Vadhavkar, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32740
  Strongly Adaptive Online Learning with Time-Varying Movement Costs
  Andrew Jacobsen, Emmanuel Esposito, Hao Qiu, et al.
  Machine Learning (cs.LG); Machine Learning (stat.ML)
• arXiv:2610.32729
  Internal Discrepancy for Contrastive Learning: Generalization Bounds and Downstream Behaviors
  Jialiang Wang, Qing Ma, Xiong Zhou, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• arXiv:2610.32727
  PlayerEmbed: Learning Behavioral Fingerprint Representations from Raw Input Sequences
  Tobias Schneider, Lorenz Sparrenberg, Priya Tomar, et al.
  Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• arXiv:2610.32657
  DT-JEPA: Learning to Predict Future Representations for Decision-Making
  Changxu Ni, Yiyang Ni, Jun Li, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32650
  WL-SeqVAE: Sequence-Based Graph Generation Using Variational Autoencoders and Weisfeiler-Leman Colors
  Iakovos Evdaimon, Giannis Nikolentzos, Achilleas Tsimichodimos, et al.
  Machine Learning (cs.LG)
• arXiv:2610.32630
  The Standard Deviation in VICReg Buys a Safety Margin
  Hawar Dizayee
  Machine Learning (cs.LG)