Papers
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Alex Lu, Amy Lu, Wiebke Schormann et al.
Fair Algorithms for Clustering
Suman Bera, Deeparnab Chakrabarty, Nicolas Flores et al.
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov
Online Convex Matrix Factorization with Representative Regions
Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota et al.
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Aditya Gangrade, Praveen Venkatesh, Bobak Nazer et al.
Blended Matching Pursuit
Cyrille Combettes, Sebastian Pokutta
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses
Ananya Uppal, Shashank Singh, Barnabas Poczos
Primal-Dual Block Generalized Frank-Wolfe
Qi Lei, JIACHENG ZHUO, Constantine Caramanis et al.
On the Downstream Performance of Compressed Word Embeddings
Avner May, Jian Zhang, Tri Dao et al.
KNG: The K-Norm Gradient Mechanism
Matthew Reimherr, Jordan Awan
Efficient online learning with kernels for adversarial large scale problems
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz et al.
Neural Diffusion Distance for Image Segmentation
Jian Sun, Zongben Xu
Exact sampling of determinantal point processes with sublinear time preprocessing
Michal Derezinski, Daniele Calandriello, Michal Valko
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Guang-He Lee, Yang Yuan, Shiyu Chang et al.
The Broad Optimality of Profile Maximum Likelihood
Yi Hao, Alon Orlitsky
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Boris Muzellec, Marco Cuturi
Multi-Agent Common Knowledge Reinforcement Learning
Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar et al.
Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio et al.
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Normalization Helps Training of Quantized LSTM
Lu Hou, Jinhua Zhu, James Kwok et al.
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
Shangyu Chen, Wenya Wang, Sinno Jialin Pan
Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong et al.
Verified Uncertainty Calibration
Ananya Kumar, Percy Liang, Tengyu Ma