Papers
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model
Libby Zhang, Tim Dunn, Jesse Marshall et al.
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang
A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits
Kei Takemura, Shinji Ito, Daisuke Hatano et al.
Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri et al.
Approximate Message Passing with Spectral Initialization for Generalized Linear Models
Marco Mondelli, Ramji Venkataramanan
Approximating Lipschitz continuous functions with GroupSort neural networks
Ugo Tanielian, Gerard Biau
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization
Moses Charikar, Lunjia Hu
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
A Spectral Analysis of Dot-product Kernels
Meyer Scetbon, Zaid Harchaoui
Associative Convolutional Layers
Hamed Omidvar, Vahideh Akhlaghi, Hao Su et al.
A Statistical Perspective on Coreset Density Estimation
Paxton Turner, Jingbo Liu, Philippe Rigollet
A Stein Goodness-of-test for Exponential Random Graph Models
Wenkai Xu, Gesine Reinert
A Study of Condition Numbers for First-Order Optimization
Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud et al.
Asymptotics of Ridge(less) Regression under General Source Condition
Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure et al.
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models
Samet Oymak, Talha Cihad Gulcu
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
Yishay Mansour, Mehryar Mohri, Jae Ro et al.
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
Martin Royer, Frederic Chazal, Clément Levrard et al.
A unified view of likelihood ratio and reparameterization gradients
Paavo Parmas, Masashi Sugiyama
Automatic Differentiation Variational Inference with Mixtures
Warren Morningstar, Sharad Vikram, Cusuh Ham et al.
Automatic structured variational inference
Luca Ambrogioni, Kate Lin, Emily Fertig et al.
A Variational Inference Approach to Learning Multivariate Wold Processes
Jalal Etesami, William Trouleau, Negar Kiyavash et al.
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
Changhee Lee, Mihaela van der Schaar