Papers
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
Yossi Arjevani, Ohad Shamir
Ordinal Graphical Models: A Tale of Two Approaches
Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
Vatsal Sharan, Gregory Valiant
Pain-Free Random Differential Privacy with Sensitivity Sampling
Benjamin I. P. Rubinstein, Francesco Aldà
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp et al.
Parallel Multiscale Autoregressive Density Estimation
Scott Reed, Aäron Oord, Nal Kalchbrenner et al.
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave et al.
Partitioned Tensor Factorizations for Learning Mixed Membership Models
Zilong Tan, Sayan Mukherjee
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov, Christoph H. Lampert
Post-Inference Prior Swapping
Willie Neiswanger, Eric Xing
Practical Gauss-Newton Optimisation for Deep Learning
Aleksandar Botev, Hippolyt Ritter, David Barber
Prediction and Control with Temporal Segment Models
Nikhil Mishra, Pieter Abbeel, Igor Mordatch
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou et al.
Preferential Bayesian Optimization
Javier González, Zhenwen Dai, Andreas Damianou et al.
Priv’IT: Private and Sample Efficient Identity Testing
Bryan Cai, Constantinos Daskalakis, Gautam Kamath
Probabilistic Path Hamiltonian Monte Carlo
Vu Dinh, Arman Bilge, Cheng Zhang et al.
Probabilistic Submodular Maximization in Sub-Linear Time
Serban Stan, Morteza Zadimoghaddam, Andreas Krause et al.
Programming with a Differentiable Forth Interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky et al.
Projection-free Distributed Online Learning in Networks
Wenpeng Zhang, Peilin Zhao, Wenwu Zhu et al.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal et al.
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations
Yuanzhi Li, Yingyu Liang
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Lihong Li, Yu Lu, Dengyong Zhou
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks
Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao
Random Feature Expansions for Deep Gaussian Processes
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi et al.
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco et al.