1 code implementation • ICML 2020 • Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach.
no code implementations • 16 Dec 2019 • Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon
We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither.
1 code implementation • NeurIPS 2019 • Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon, Mikhail Yurochkin
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures.
1 code implementation • NeurIPS 2019 • Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora.
1 code implementation • 19 Sep 2018 • Hugo Lavenant, Sebastian Claici, Edward Chien, Justin Solomon
We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport.
Analysis of PDEs Numerical Analysis Numerical Analysis Optimization and Control
no code implementations • 18 May 2018 • Sebastian Claici, Aude Genevay, Justin Solomon
The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification.
3 code implementations • ICML 2018 • Sebastian Claici, Edward Chien, Justin Solomon
We present a stochastic algorithm to compute the barycenter of a set of probability distributions under the Wasserstein metric from optimal transport.
1 code implementation • NeurIPS 2017 • Matthew Staib, Sebastian Claici, Justin Solomon, Stefanie Jegelka
Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time.