Papers
Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar et al.
Particle Flow Bayes’ Rule
Xinshi Chen, Hanjun Dai, Le Song
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani et al.
Per-Decision Option Discounting
Anna Harutyunyan, Peter Vrancx, Philippe Hamel et al.
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Niels Ipsen, Lars Kai Hansen
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang et al.
Poission Subsampled Rényi Differential Privacy
Yuqing Zhu, Yu-Xiang Wang
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei et al.
Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis, Murray Shanahan, Claudia Clopath
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia et al.
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Lily Weng et al.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho, Eric Liang, Xi Chen et al.
Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
Power k-Means Clustering
Jason Xu, Kenneth Lange
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.