Papers
Predicate Exchange: Inference with Declarative Knowledge
Zenna Tavares, Javier Burroni, Edgar Minasyan et al.
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff et al.
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee et al.
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
Casey Chu, Jose Blanchet, Peter Glynn
Processing Megapixel Images with Deep Attention-Sampling Models
Angelos Katharopoulos, Francois Fleuret
Projection onto Minkowski Sums with Application to Constrained Learning
Joong-Ho Won, Jason Xu, Kenneth Lange
Projections for Approximate Policy Iteration Algorithms
Riad Akrour, Joni Pajarinen, Jan Peters et al.
Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu et al.
Provable Guarantees for Gradient-Based Meta-Learning
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Provably Efficient Imitation Learning from Observation Alone
Wen Sun, Anirudh Vemula, Byron Boots et al.
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh et al.
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang et al.
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Lily Weng, Pin-Yu Chen, Lam Nguyen et al.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang et al.
Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse et al.
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization
Chengyue Gong, Jian Peng, Qiang Liu
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Ramchandran Kannan, Peter Bartlett
RaFM: Rank-Aware Factorization Machines
Xiaoshuang Chen, Yin Zheng, Jiaxing Wang et al.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori et al.
Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik Tiomoko, Romain Couillet, Florent Bouchard et al.
Random Shuffling Beats SGD after Finite Epochs
Jeff Haochen, Suvrit Sra
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Uthsav Chitra, Benjamin Raphael
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni et al.
Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang et al.