Machine Learning (Statistics)

Papers

• Sigmoid-weighted Linear Units For Neural Network Function Approximation In Reinforcement Learning (2017)
  Stefan Elfwing, Eiji Uchibe, Kenji Doya
  24.15
• Distributional Reinforcement Learning With Quantile Regression (2017)
  Will Dabney, Mark Rowland, Marc G. Bellemare, et al.
  19.20
• Using Human Feedback To Fine-tune Diffusion Models Without Any Reward Model (2023)
  Kai Yang, Jian Tao, Jiafei Lyu, et al.
  17.39
• On the regularization of Wasserstein GANs (2017)
  Henning Petzka et al.
  15.83
• Reinforcement Learning In Economics And Finance (2020)
  Arthur Charpentier, Romuald Elie, Carl Remlinger
  14.73
• Reinforcement Learning With Perturbed Rewards (2018)
  Jingkang Wang, Yang Liu, Bo Li
  13.74
• T-soft Update Of Target Network For Deep Reinforcement Learning (2020)
  Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo
  13.39
• Data-efficient Domain Randomization With Bayesian Optimization (2020)
  Fabio Muratore, Christian Eilers, Michael Gienger, et al.
  13.28
• Measurement-based Adaptation Protocol With Quantum Reinforcement Learning (2018)
  F. Albarrán-Arriagada, J. C. Retamal, E. Solano, et al.
  12.93
• Deep Hierarchical Reinforcement Learning Algorithm In Partially Observable Markov Decision Processes (2018)
  Le Pham Tuyen, Ngo Anh Vien, Abu Layek, et al.
  12.87
• Deep Reinforcement Learning For Adaptive Learning Systems (2020)
  Xiao Li, Hanchen Xu, Jinming Zhang, et al.
  12.54
• Convergence Proof For Actor-critic Methods Applied To PPO And RUDDER (2020)
  Markus Holzleitner, Lukas Gruber, José Arjona-Medina, et al.
  11.67
• Reward Maximisation Through Discrete Active Inference (2020)
  Lancelot da Costa, Noor Sajid, Thomas Parr, et al.
  10.74
• Action Candidate Driven Clipped Double Q-learning For Discrete And Continuous Action Tasks (2022)
  Haobo Jiang, Jin Xie, Jian Yang
  10.61
• The Utility Of Sparse Representations For Control In Reinforcement Learning (2018)
  Vincent Liu, Raksha Kumaraswamy, Lei Le, et al.
  10.48
• Evolutionary Reinforcement Learning Of Dynamical Large Deviations (2019)
  Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
  10.21
• Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models (2026)
  Mingze Wang et al.
  10.00
• A Comparative Analysis Of Expected And Distributional Reinforcement Learning (2019)
  Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
  9.76
• Measuring And Characterizing Generalization In Deep Reinforcement Learning (2018)
  Sam Witty, Jun Ki Lee, Emma Tosch, et al.
  9.76
• Generalized Population-based Training For Hyperparameter Optimization In Reinforcement Learning (2024)
  Hui Bai, Ran Cheng
  9.59
• Estimating Scale-invariant Future In Continuous Time (2018)
  Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg, et al.
  9.59
• Bias-reduced Hindsight Experience Replay With Virtual Goal Prioritization (2019)
  Binyamin Manela, Armin Biess
  9.41
• Don't Start From Scratch: Behavioral Refinement Via Interpolant-based Policy Diffusion (2024)
  Kaiqi Chen, Eugene Lim, Kelvin Lin, et al.
  9.28
• Towards Applicable Reinforcement Learning: Improving The Generalization And Sample Efficiency With Policy Ensemble (2022)
  Zhengyu Yang, Kan Ren, Xufang Luo, et al.
  9.23
• Learning Sparse Representations In Reinforcement Learning With Sparse Coding (2017)
  Lei Le, Raksha Kumaraswamy, Martha White
  8.82
• Rethinking The Discount Factor In Reinforcement Learning: A Decision Theoretic Approach (2019)
  Silviu Pitis
  8.60
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model (2020)
  Gen Li et al.
  7.83
• Computably Continuous Reinforcement-learning Objectives Are Pac-learnable (2023)
  Cambridge Yang, Michael Littman, Michael Carbin
  7.81
• A Definition Of Continual Reinforcement Learning (2023)
  David Abel, André Barreto, Benjamin van Roy, et al.
  7.50
• Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning (2018)
  Jayakumar Subramanian, Aditya Mahajan
  7.50
• Self Punishment And Reward Backfill For Deep Q-learning (2020)
  Mohammad Reza Bonyadi, Rui Wang, Maryam Ziaei
  7.16
• Bayesian Reinforcement Learning With Limited Cognitive Load (2023)
  Dilip Arumugam, Mark K. Ho, Noah D. Goodman, et al.
  6.77
• Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across Mdps (2021)
  Ezgi Korkmaz
  6.77
• Adversary Agnostic Robust Deep Reinforcement Learning (2020)
  Xinghua Qu, Yew-Soon Ong, Abhishek Gupta, et al.
  6.77
• Reinforcement Learning In Pomdps With Memoryless Options And Option-observation Initiation Sets (2017)
  Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, et al.
  6.77
• A Unified Approach For Multi-step Temporal-difference Learning With Eligibility Traces In Reinforcement Learning (2018)
  Long Yang, Minhao Shi, Qian Zheng, et al.
  6.77
• Probabilistic Shielding for Safe Reinforcement Learning (2025)
  Edwin Hamel-De le Court et al.
  6.47
• Greedy Sampling Is Provably Efficient for RLHF (2025)
  Di Wu et al.
  6.34
• Nonparametric Bellman Mappings For Reinforcement Learning: Application To Robust Adaptive Filtering (2024)
  Yuki Akiyama, Minh Vu, Konstantinos Slavakis
  6.34
• Post Reinforcement Learning Inference (2023)
  Vasilis Syrgkanis et al.
  6.28
• $O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation (2025)
  Siddharth Chandak
  6.28
• Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent (2026)
  Ahanaf Hasan Ariq
  5.89
• Edge of Stability Selectively Shapes Learning Across the Data Distribution (2026)
  Shauna Kwag et al.
  5.89
• When Do Fewer Coordinates Suffice in DP-SGD? (2026)
  Huiqi Zhang et al.
  5.89
• Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD (2026)
  Xiaobo Huang et al.
  5.89
• When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks (2026)
  Berk Tinaz et al.
  5.89
• Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning (2026)
  Damian Lebied\'z et al.
  5.89
• Worker Utility as Hysteresis: A Preisach Model of Transaction Acceptance in Gig Labour Markets (2026)
  Piotr Frydrych
  5.89
• Identifying Gems from Roman RAPIDly (2026)
  Karan Gandhi et al.
  5.89
• Successor Feature Sets: Generalizing Successor Representations Across Policies (2021)
  Kianté Brantley, Soroush Mehri, Geoffrey J. Gordon
  5.84