Papers
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
Phase Retrieval Under a Generative Prior
Paul Hand, Oscar Leong, Vlad Voroninski
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li, Mingchao Yu, Songze Li et al.
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden et al.
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan et al.
PointCNN: Convolution On X-Transformed Points
Yangyan Li, Rui Bu, Mingchao Sun et al.
Point process latent variable models of larval zebrafish behavior
Anuj Sharma, Robert Johnson, Florian Engert et al.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi et al.
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
Andrea Tirinzoni, Marek Petrik, Xiangli Chen et al.
Policy Optimization via Importance Sampling
Alberto Maria Metelli, Matteo Papini, Francesco Faccio et al.
Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov et al.
Porcupine Neural Networks: Approximating Neural Network Landscapes
Soheil Feizi, Hamid Javadi, Jesse Zhang et al.
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
Yuanxiang Gao, Li Chen, Baochun Li
Posterior Concentration for Sparse Deep Learning
Nicholas G Polson, Veronika Ročková
Power-law efficient neural codes provide general link between perceptual bias and discriminability
Michael Morais, Jonathan W Pillow
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, François Fleuret
Practical exact algorithm for trembling-hand equilibrium refinements in games
Gabriele Farina, Nicola Gatti, Tuomas Sandholm
Practical Methods for Graph Two-Sample Testing
Debarghya Ghoshdastidar, Ulrike von Luxburg
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik et al.
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash et al.
Predictive Uncertainty Estimation via Prior Networks
Andrey Malinin, Mark Gales
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
David Madras, Toni Pitassi, Richard Zemel
Preference Based Adaptation for Learning Objectives
Yao-Xiang Ding, Zhi-Hua Zhou
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Borja Balle, Gilles Barthe, Marco Gaboardi
Probabilistic Matrix Factorization for Automated Machine Learning
Nicolo Fusi, Rishit Sheth, Melih Elibol