1.1k citations · 7.8k across the 180 of their papers we have counts for
9 papers · 1 filter
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
Tianyuan Jin, Pan Xu, Xiaokui Xiao +1
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers ma…
Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen +2
Knowledge Distillation (KD) consists of transferring “knowledge†from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacit…
Homotopy Analysis for Tensor PCA
Anima Anandkumar, Yuan Deng, Rong Ge +1
Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic g…
Online and Differentially-Private Tensor Decomposition
Yining Wang, Animashree Anandkumar
In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition. We propose simple variant…
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Eric Nalisnick, Anima Anandkumar, Padhraic Smyth
Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization…
Fast and Guaranteed Tensor Decomposition via Sketching
Yining Wang, Hsiao-Yu Tung, Alexander Smola +1
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomize…
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Majid Janzamin, Animashree Anandkumar
Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. Too often, sparsity assumptions on the fitted model are too…
Learning loopy graphical models with latent variables: Efficient methods and guarantees
Animashree Anandkumar, Ragupathyraj Valluvan
The problem of structure estimation in graphical models with latent variables is considered. We characterize conditions for tractable graph estimation and develop efficient methods…
Learning Latent Tree Graphical Models
Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar +1
We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. We propose two consistent and computationally efficient…