Papers
4,025 papers found
The Expressive Power of a Class of Normalizing Flow Models
Zhifeng Kong, Kamalika Chaudhuri
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions
Feras Saad, Cameron Freer, Martin Rinard et al.
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh et al.
The Implicit Regularization of Ordinary Least Squares Ensembles
Daniel LeJeune, Hamid Javadi, Richard Baraniuk
The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel Jiang, Aaditya Ramdas et al.
The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time
Travis Moore, Wong Weng-Keen
The Sylvester Graphical Lasso (SyGlasso)
Yu Wang, Byoungwook Jang, Alfred Hero
The True Sample Complexity of Identifying Good Arms
Julian Katz-Samuels, Kevin Jamieson
Thompson Sampling for Linearly Constrained Bandits
Vidit Saxena, Joakim Jalden, Joseph Gonzalez
Thresholding Bandit Problem with Both Duels and Pulls
Yichong Xu, Xi Chen, Aarti Singh et al.
Thresholding Graph Bandits with GrAPL
Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy
Quan Geng, Wei Ding, Ruiqi Guo et al.
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, Peter Richtarik
Towards Competitive N-gram Smoothing
Moein Falahatgar, Mesrob Ohannessian, Alon Orlitsky et al.
Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions
Giorgia Ramponi, Amarildo Likmeta, Alberto Maria Metelli et al.
Two-sample Testing Using Deep Learning
Matthias Kirchler, Shahryar Khorasani, Marius Kloft et al.
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Zepeng Huo, Arash PakBin, Xiaohan Chen et al.
Uncertainty Quantification for Sparse Deep Learning
Yuexi Wang, Veronika Rockova
Unconditional Coresets for Regularized Loss Minimization
Alireza Samadian, Kirk Pruhs, Benjamin Moseley et al.
Understanding Generalization in Deep Learning via Tensor Methods
Jingling Li, Yanchao Sun, Jiahao Su et al.
Understanding the Effects of Batching in Online Active Learning
Kareem Amin, Corinna Cortes, Giulia DeSalvo et al.
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang, Jinghui Chen, Quanquan Gu et al.
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
David Alvarez-Melis, Youssef Mroueh, Tommi Jaakkola
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel
Taeeon Park, Taesup Moon