Papers
Empirical Mechanism Design: Designing Mechanisms from Data
Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad et al.
CCMI : Classifier based Conditional Mutual Information Estimation
Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan
Object Conditioning for Causal Inference
David Jensen, Javier Burroni, Matthew Rattigan
Learning Belief Representations for Imitation Learning in POMDPs
Tanmay Gangwani, Joel Lehman, Qiang Liu et al.
Learnability for the Information Bottleneck
Tailin Wu, Ian Fischer, Isaac L. Chuang et al.
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth et al.
Causal Inference Under Interference And Network Uncertainty
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
P3O: Policy-on Policy-off Policy Optimization
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola
Social Reinforcement Learning to Combat Fake News Spread
Mahak Goindani, Jennifer Neville
Real-Time Robotic Search using Structural Spatial Point Processes
Olov Andersson, Per Sidén, Johan Dahlin et al.
Generating and Sampling Orbits for Lifted Probabilistic Inference
Steven Holtzen, Todd Millstein, Guy Van den Broeck
Intervening on Network Ties
Eli Sherman, Ilya Shpitser
Exact Sampling of Directed Acyclic Graphs from Modular Distributions
Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto
Approximate Relative Value Learning for Average-reward Continuous State MDPs
Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations
Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash et al.
Convergence Analysis of Gradient-Based Learning in Continuous Games
Benjamin Chasnov, Lillian Ratliff, Eric Mazumdar et al.
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning
Smitha Milli, Anca D. Dragan
Fake It Till You Make It: Learning-Compatible Performance Support
Jonathan Bragg, Emma Brunskill
Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap et al.
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Roman Pogodin, Tor Lattimore
Guaranteed Scalable Learning of Latent Tree Models
Furong Huang, Niranjan Uma Naresh, Ioakeim Perros et al.
Variational Training for Large-Scale Noisy-OR Bayesian Networks
Geng Ji, Dehua Cheng, Huazhong Ning et al.
Wasserstein Fair Classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton et al.
Problem-dependent Regret Bounds for Online Learning with Feedback Graphs
Bingshan Hu, Nishant A. Mehta, Jianping Pan
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi et al.