897 citations · 1.2k across the 22 of their papers we have counts for
24 papers
The Silhouette Operator: Identifiability of Low-Rank Measures from One-Dimensional Projections
Robert A. Vandermeulen
Structured recovery phenomena, such as restricted isometry properties in compressed sensing, have shown that high-dimensional objects can often be reconstructed from remarkably low…
PMODE: Theoretically Grounded and Modular Mixture Modeling
Robert A. Vandermeulen
We introduce PMODE (Partitioned Mixture Of Density Estimators), a general and modular framework for mixture modeling with both parametric and nonparametric components. PMODE builds…
Dimension-independent rates for structured neural density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applic…
Breaking the curse of dimensionality in structured density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We consider the problem of estimating a structured multivariate density, subject to Markov conditions implied by an undirected graph. In the worst case, without Markovian assumptio…
Set Learning for Accurate and Calibrated Models
Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang +2
Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a…
Improving neural network representations using human similarity judgments
Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel +4
Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are e…