Papers
Practical and Private (Deep) Learning Without Sampling or Shuffling
Peter Kairouz, Brendan Mcmahan, Shuang Song et al.
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Mateusz Wilinski, Andrey Lokhov
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Yujia Bao, Shiyu Chang, Regina Barzilay
Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
Sourbh Bhadane, Aaron B Wagner, Jayadev Acharya
Principal Component Hierarchy for Sparse Quadratic Programs
Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari et al.
Principled Exploration via Optimistic Bootstrapping and Backward Induction
Chenjia Bai, Lingxiao Wang, Lei Han et al.
Principled Simplicial Neural Networks for Trajectory Prediction
T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Varun A Kelkar, Mark Anastasio
Prioritized Level Replay
Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Privacy-Preserving Feature Selection with Secure Multiparty Computation
Xiling Li, Rafael Dowsley, Martine De Cock
Privacy-Preserving Video Classification with Convolutional Neural Networks
Sikha Pentyala, Rafael Dowsley, Martine De Cock
Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah et al.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene et al.
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
Hilal Asi, Vitaly Feldman, Tomer Koren et al.
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