Papers
Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian, Iyad Kanj, Sebastian Ordyniak et al.
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh
Path-Level Network Transformation for Efficient Architecture Search
Han Cai, Jiacheng Yang, Weinan Zhang et al.
Pathwise Derivatives Beyond the Reparameterization Trick
Martin Jankowiak, Fritz Obermeyer
PDE-Net: Learning PDEs from Data
Zichao Long, Yiping Lu, Xianzhong Ma et al.
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Paavo Parmas, Carl Edward Rasmussen, Jan Peters et al.
PixelSNAIL: An Improved Autoregressive Generative Model
XI Chen, Nikhil Mishra, Mostafa Rohaninejad et al.
Policy and Value Transfer in Lifelong Reinforcement Learning
David Abel, Yuu Jinnai, Sophie Yue Guo et al.
Policy Optimization as Wasserstein Gradient Flows
Ruiyi Zhang, Changyou Chen, Chunyuan Li et al.
Policy Optimization with Demonstrations
Bingyi Kang, Zequn Jie, Jiashi Feng
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudik et al.
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan et al.
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson
Prediction Rule Reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber et al.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang, Zhifeng Gao, Mingsheng Long et al.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth et al.
Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris Holmes, Christopher Yau
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg et al.
Probably Approximately Metric-Fair Learning
Gal Yona, Guy Rothblum
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette, Emma Brunskill
Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh et al.
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina et al.
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen, Christopher Harshaw, Hamed Hassani et al.
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni