Search Results for author: Sebastian Claici

Found 8 papers, 6 papers with code

Model Fusion with Kullback--Leibler Divergence

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.

Federated Learning

Incorporating Unlabeled Data into Distributionally Robust Learning

no code implementations16 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.

Active Learning

Alleviating Label Switching with Optimal Transport

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.

Hierarchical Optimal Transport for Document Representation

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.

Dynamical Optimal Transport on Discrete Surfaces

1 code implementation19 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

Wasserstein Measure Coresets

no code implementations18 May 2018 Sebastian Claici, Aude Genevay, Justin Solomon

The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification.

Bayesian Inference Clustering

Stochastic Wasserstein Barycenters

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.

Parallel Streaming Wasserstein Barycenters

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.

Bayesian Inference

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