Papers
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora et al.
A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet et al.
AUCμ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman, David Page
Automated Model Selection with Bayesian Quadrature
Henry Chai, Jean-Francois Ton, Michael A. Osborne et al.
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill, Anand Ramakrishnan
Automatic Posterior Transformation for Likelihood-Free Inference
David Greenberg, Marcel Nonnenmacher, Jakob Macke
Autoregressive Energy Machines
Charlie Nash, Conor Durkan
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Kaizhi Qian, Yang Zhang, Shiyu Chang et al.
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita et al.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer, David Pal, Balazs Szorenyi et al.
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic, John Paparrizos, Sanjay Krishnan et al.
Batch Policy Learning under Constraints
Hoang Le, Cameron Voloshin, Yisong Yue
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Francis Song, Edward Hughes et al.
Bayesian Counterfactual Risk Minimization
Ben London, Ted Sandler
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu, Fabio Ramos
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid et al.
Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Li, Tyler Mccormick, Samuel Clark
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Andersen, Johan Jonasson et al.
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh et al.
Bayesian Optimization Meets Bayesian Optimal Stopping
Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low et al.
Bayesian Optimization of Composite Functions
Raul Astudillo, Peter Frazier
BayesNAS: A Bayesian Approach for Neural Architecture Search
Hongpeng Zhou, Minghao Yang, Jun Wang et al.
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert, Haipeng Luo, Chen-Yu Wei