290 citations · 1.8k across the 59 of their papers we have counts for
8 papers · 1 filter
Variational inference via Wasserstein gradient flows
Marc Lambert, Sinho Chewi, Francis Bach +2
Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI) has emerged as a central computational approach to large-scale Bayesian inference. Rather than sampli…
Gaussian Determinantal Processes: a new model for directionality in data
Subhro Ghosh, Philippe Rigollet
Determinantal point processes (a.k.a. DPPs) have recently become popular tools for modeling the phenomenon of negative dependence, or repulsion, in data. However, our understanding…
Statistical Optimal Transport via Factored Couplings
Aden Forrow, Jan-Christian Hütter, Mor Nitzan +3
We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension. Unlike plug-in rules tha…
Sparse Gaussian ICA
Nilin Abrahamsen, Philippe Rigollet
Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS wher…
Teacher Improves Learning by Selecting a Training Subset
Yuzhe Ma, Robert Nowak, Philippe Rigollet +2
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learn…
Minimax Rates and Efficient Algorithms for Noisy Sorting
Cheng Mao, Jonathan Weed, Philippe Rigollet
There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data. Despite being structurally richer and more robust than par…