Papers
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.
Real-Time Adaptive Image Compression
Oren Rippel, Lubomir Bourdev
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain et al.
Recurrent Highway Networks
Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnı́k et al.
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Noam Brown, Tuomas Sandholm
Regret Minimization in Behaviorally-Constrained Zero-Sum Games
Gabriele Farina, Christian Kroer, Tuomas Sandholm
Regularising Non-linear Models Using Feature Side-information
Amina Mollaysa, Pablo Strasser, Alexandros Kalousis
Reinforcement Learning with Deep Energy-Based Policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel et al.
Relative Fisher Information and Natural Gradient for Learning Large Modular Models
Ke Sun, Frank Nielsen