Papers
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio Calmon
Replica Conditional Sequential Monte Carlo
Alex Shestopaloff, Arnaud Doucet
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau, Tomer Michaeli
Revisiting precision recall definition for generative modeling
Loic Simon, Ryan Webster, Julien Rabin
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song, Ron Parr, Lawrence Carin
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra
Robust Decision Trees Against Adversarial Examples
Hongge Chen, Huan Zhang, Duane Boning et al.
Robust Estimation of Tree Structured Gaussian Graphical Models
Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee et al.
Robust Influence Maximization for Hyperparametric Models
Dimitris Kalimeris, Gal Kaplun, Yaron Singer
Robust Learning from Untrusted Sources
Nikola Konstantinov, Christoph Lampert
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf et al.
Rotation Invariant Householder Parameterization for Bayesian PCA
Rajbir Nirwan, Nils Bertschinger
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq et al.
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet Des Combes
SAGA with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller, Alexander Finkelstein, Uri Almog et al.
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features
Lin Yang, Mengdi Wang
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder et al.
Scalable Fair Clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak et al.
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gärtner
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote et al.
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Peter Bartlett, Victor Gabillon, Jennifer Healey et al.