Papers
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Francis Song, Edward Hughes et al.
DL2: Training and Querying Neural Networks with Logic
Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen et al.
Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade et al.
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara et al.
Decentralized Exploration in Multi-Armed Bandits
Raphael Feraud, Reda Alami, Romain Laroche
The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt
Dead-ends and Secure Exploration in Reinforcement Learning
Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen et al.
Regret Circuits: Composability of Regret Minimizers
Gabriele Farina, Christian Kroer, Tuomas Sandholm
Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina, Christian Kroer, Noam Brown et al.
Multi-Frequency Vector Diffusion Maps
Yifeng Fan, Zhizhen Zhao
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass et al.
Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo et al.
Exploring the Landscape of Spatial Robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras et al.
Improved Convergence for ℓ_1 and ℓ_∞ Regression via Iteratively Reweighted Least Squares
Alina Ene, Adrian Vladu
GDPP: Learning Diverse Generations using Determinantal Point Processes
Mohamed Elfeki, Camille Couprie, Morgane Riviere et al.
Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, Brendan Mcmahan et al.
Imitating Latent Policies from Observation
Ashley Edwards, Himanshu Sahni, Yannick Schroecker et al.
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic, John Paparrizos, Sanjay Krishnan et al.
Autoregressive Energy Machines
Charlie Nash, Conor Durkan
Learning interpretable continuous-time models of latent stochastic dynamical systems
Lea Duncker, Gergo Bohner, Julien Boussard et al.
Wasserstein of Wasserstein Loss for Learning Generative Models
Yonatan Dukler, Wuchen Li, Alex Lin et al.