Papers
Probabilistic Generating Circuits
Honghua Zhang, Brendan Juba, Guy Van Den Broeck
Probabilistic Programs with Stochastic Conditioning
David Tolpin, Yuan Zhou, Tom Rainforth et al.
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Zixin Zhong, Wang Chi Cheung, Vincent Tan
Problem Dependent View on Structured Thresholding Bandit Problems
James Cheshire, Pierre Menard, Alexandra Carpentier
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun et al.
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
Jiawei Zhang, Linyi Li, Huichen Li et al.
Projection Robust Wasserstein Barycenters
Minhui Huang, Shiqian Ma, Lifeng Lai
Projection techniques to update the truncated SVD of evolving matrices with applications
Vasileios Kalantzis, Georgios Kollias, Shashanka Ubaru et al.
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei, Yuan Cao, Quanquan Gu
Provable Lipschitz Certification for Generative Models
Matt Jordan, Alex Dimakis
Provable Meta-Learning of Linear Representations
Nilesh Tripuraneni, Chi Jin, Michael Jordan
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Difan Zou, Spencer Frei, Quanquan Gu
Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal et al.
Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang et al.
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Shuang Qiu, Xiaohan Wei, Jieping Ye et al.
Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti et al.
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Dongruo Zhou, Jiafan He, Quanquan Gu
Provably End-to-end Label-noise Learning without Anchor Points
Xuefeng Li, Tongliang Liu, Bo Han et al.
Provably Strict Generalisation Benefit for Equivariant Models
Bryn Elesedy, Sheheryar Zaidi
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin et al.
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal et al.
Pure Exploration and Regret Minimization in Matching Bandits
Flore Sentenac, Jialin Yi, Clement Calauzenes et al.
Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation
Elbert Du, Franklyn Wang, Michael Mitzenmacher
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
Uthsav Chitra, Kimberly Ding, Jasper C.H. Lee et al.
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
Mihaela Curmei, Sarah Dean, Benjamin Recht