Papers
Probabilistic Querying of Continuous-Time Event Sequences
Alex Boyd, Yuxin Chang, Stephan Mandt et al.
Probabilities of Causation: Role of Observational Data
Ang Li, Judea Pearl
Probing Graph Representations
Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov et al.
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
Michelle Chen, Olga Ohrimenko
Provable Hierarchy-Based Meta-Reinforcement Learning
Kurtland Chua, Qi Lei, Jason Lee
Provable Safe Reinforcement Learning with Binary Feedback
Andrew Bennett, Dipendra Misra, Nathan Kallus
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Honghao Wei, Arnob Ghosh, Ness Shroff et al.
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.