Papers
Policy Optimization with Demonstrations
Bingyi Kang, Zequn Jie, Jiashi Feng
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudik et al.
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan et al.
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson
Prediction Rule Reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber et al.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang, Zhifeng Gao, Mingsheng Long et al.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth et al.
Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris Holmes, Christopher Yau
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg et al.
Probably Approximately Metric-Fair Learning
Gal Yona, Guy Rothblum
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette, Emma Brunskill
Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh et al.
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina et al.
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen, Christopher Harshaw, Hamed Hassani et al.
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Eric Wong, Zico Kolter
Provable Variable Selection for Streaming Features
Jing Wang, Jie Shen, Ping Li
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Elliot Meyerson, Risto Miikkulainen
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder et al.
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Giacomo Boracchi, Diego Carrera, Cristiano Cervellera et al.
Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Yichi Zhou, Jun Zhu, Jingwei Zhuo
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon, James Jordon, Mihaela Schaar
Randomized Block Cubic Newton Method
Nikita Doikov, Peter Richtarik, University Edinburgh