Papers
4,025 papers found
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu, Ruosong Wang, Jason Lee
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy et al.
Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves
David Bosch, Ashkan Panahi, Ayca Ozcelikkale et al.
Randomized geometric tools for anomaly detection in stock markets
Cyril Bachelard, Apostolos Chalkis, Vissarion Fisikopoulos et al.
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
Fares Fourati, Vaneet Aggarwal, Christopher Quinn et al.
Randomized Primal-Dual Methods with Adaptive Step Sizes
Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh, Necdet S. Aybat
Rank-Based Causal Discovery for Post-Nonlinear Models
Grigor Keropyan, David Strieder, Mathias Drton
Reconstructing Training Data from Model Gradient, Provably
Zihan Wang, Jason Lee, Qi Lei
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
Adrian N. Bishop, Edwin V. Bonilla
Reducing Discretization Error in the Frank-Wolfe Method
Zhaoyue Chen, Yifan Sun
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
Batiste Le Bars, Aurélien Bellet, Marc Tommasi et al.
Regression as Classification: Influence of Task Formulation on Neural Network Features
Lawrence Stewart, Francis Bach, Quentin Berthet et al.
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group
Zhenbang Wang, Emanuel Ben-David, Martin Slawski
Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Tarik Dzanic, Brenden Petersen et al.
Reinforcement Learning with Stepwise Fairness Constraints
Zhun Deng, He Sun, Steven Wu et al.
Representation Learning in Deep RL via Discrete Information Bottleneck
Riashat Islam, Hongyu Zang, Manan Tomar et al.
Rethinking Initialization of the Sinkhorn Algorithm
James Thornton, Marco Cuturi
Retrospective Uncertainties for Deep Models using Vine Copulas
Natasa Tagasovska, Firat Ozdemir, Axel Brando
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Jing Wang, Peng Zhao, Zhi-Hua Zhou
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws
Kush Bhatia, Wenshuo Guo, Jacob Steinhardt
Riemannian Accelerated Gradient Methods via Extrapolation
Andi Han, Bamdev Mishra, Pratik Jawanpuria et al.
Risk-aware linear bandits with convex loss
Patrick Saux, Odalric Maillard
Risk Bounds on Aleatoric Uncertainty Recovery
Yikai Zhang, Jiahe Lin, Fengpei Li et al.