| 2020 | Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. | Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar |
| 2020 | A Rule for Gradient Estimator Selection, with an Application to Variational Inference. | Tomas Geffner, Justin Domke |
| 2020 | Explaining the Explainer: A First Theoretical Analysis of LIME. | Damien Garreau, Ulrike von Luxburg |
| 2020 | Conservative Exploration in Reinforcement Learning. | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta |
| 2020 | Improved Regret Bounds for Projection-free Bandit Convex Optimization. | Dan Garber, Ben Kretzu |
| 2020 | Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. | Tho Galy-Fajou, Florian Wenzel, Manfred Opper |
| 2020 | GAIT: A Geometric Approach to Information Theory. | Jose Gallego-Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien |
| 2020 | Enriched mixtures of generalised Gaussian process experts. | Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas |
| 2020 | A Topology Layer for Machine Learning. | Rickard Brel Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba |
| 2020 | POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. | Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez |
| 2020 | Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. | Lukas P. Frhlich, Edgar D. Klenske, Julia Vinogradska, Christian Daniel, Melanie N. Zeilinger |
| 2020 | Approximate Inference with Wasserstein Gradient Flows. | Charlie Frogner, Tomaso A. Poggio |
| 2020 | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth |
| 2020 | GP-VAE: Deep Probabilistic Time Series Imputation. | Vincent Fortuin, Dmitry Baranchuk, Gunnar Rtsch, Stephan Mandt |
| 2020 | Fairness Evaluation in Presence of Biased Noisy Labels. | Riccardo Fogliato, Alexandra Chouldechova, Max G'Sell |
| 2020 | A Locally Adaptive Bayesian Cubature Method. | Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup |
| 2020 | Adaptive multi-fidelity optimization with fast learning rates. | Cme Fiegel, Victor Gabillon, Michal Valko |
| 2020 | Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity. | Mohsen Ferdosi, Arash Gholami Davoodi, Hosein Mohimani |
| 2020 | Learning with minibatch Wasserstein : asymptotic and gradient properties. | Kilian Fatras, Younes Zine, Rmi Flamary, Rmi Gribonval, Nicolas Courty |
| 2020 | AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. | Rizal Fathony, J. Zico Kolter |
| 2020 | Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. | Sebastian Farquhar, Michael A. Osborne, Yarin Gal |
| 2020 | Orthogonal Gradient Descent for Continual Learning. | Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li |
| 2020 | Online Binary Space Partitioning Forests. | Xuhui Fan, Bin Li, Scott A. Sisson |
| 2020 | Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. | Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander |
| 2020 | Towards Competitive N-gram Smoothing. | Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati |