3k citations · 24.2k across the 299 of their papers we have counts for
44 papers · 1 filter
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing
Wenlong Mou, Nhat Ho, Martin J. Wainwright +2
We study the problem of sampling from the power posterior distribution in Bayesian Gaussian mixture models, a robust version of the classical posterior. This power posterior is kno…
A Control-Theoretic Perspective on Optimal High-Order Optimization
Tianyi Lin, Michael. I. Jordan
We provide a control-theoretic perspective on optimal tensor algorithms for minimizing a convex function in a finite-dimensional Euclidean space. Given a function $Φ: \mathbb{R}^d…
The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas +1
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve signific…
On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin, Nhat Ho, Marco Cuturi +1
We study the complexity of approximating the multimarginal optimal transport (MOT) distance, a generalization of the classical optimal transport distance, considered here between $…
Towards Understanding the Transferability of Deep Representations
Hong Liu, Mingsheng Long, Jianmin Wang +1
Deep neural networks trained on a wide range of datasets demonstrate impressive transferability. Deep features appear general in that they are applicable to many datasets and tasks…
How Does Learning Rate Decay Help Modern Neural Networks?
Kaichao You, Mingsheng Long, Jianmin Wang +1
Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empi…