Gradient Flows For Sampling Inference

Media Summary: Sahani Pathiraja (UNSW Sydney) Rocco Caprio (University of Warwick) Anna Korba (ENSAE/CREST) Paula Cordero Encinar ... Katy Craig (UC Santa Barbara) Geometric Methods in Optimization and CONFERENCE Recording during the thematic meeting : « Frontiers in interacting particle systems, aggregation-diffusion ...

Gradient Flows For Sampling Inference - Detailed Analysis & Overview

Sahani Pathiraja (UNSW Sydney) Rocco Caprio (University of Warwick) Anna Korba (ENSAE/CREST) Paula Cordero Encinar ... Katy Craig (UC Santa Barbara) Geometric Methods in Optimization and CONFERENCE Recording during the thematic meeting : « Frontiers in interacting particle systems, aggregation-diffusion ... High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning ... Seminar by Andrew Duncan at the UCL Centre for AI. Recorded on the 24th February 2021. Abstract Bayesian Michael Jordan, UC Berkeley Computational Challenges in Machine ...

Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods This talk was part of the Workshop on "PDE-constrained Bayesian inverse problems: interplay of spatial statistical models with ...

Photo Gallery

Gradient Flows For Sampling, Inference, and Learning

2nd RSS/Turing Workshop on Gradient Flows for Sampling, Inference, and Learning

Optimal Transport and PDE: Gradient Flows in the Wasserstein Metric

Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 1

Wuchen Li: "Accelerated Information Gradient Flow"

Geometric Aspects of Sampling and Optimization

(De)regularized Wasserstein Gradient Flows via Reproducing Kernels

On the geometry of Stein variational gradient descent and related ensemble sampling methods

Wasserstein gradient flows for machine learning by Anna Korba

On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic

Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods

Sinho Chewi Optimal transport and high dimensional probability Gradient Flows on Wasserstein Spa

View Detailed Profile

Gradient Flows For Sampling, Inference, and Learning

Gradient Flows For Sampling, Inference, and Learning

Gradient flow

2nd RSS/Turing Workshop on Gradient Flows for Sampling, Inference, and Learning

2nd RSS/Turing Workshop on Gradient Flows for Sampling, Inference, and Learning

Sahani Pathiraja (UNSW Sydney) Rocco Caprio (University of Warwick) Anna Korba (ENSAE/CREST) Paula Cordero Encinar ...

Optimal Transport and PDE: Gradient Flows in the Wasserstein Metric

Optimal Transport and PDE: Gradient Flows in the Wasserstein Metric

Katy Craig (UC Santa Barbara) https://simons.berkeley.edu/talks/tbd-335 Geometric Methods in Optimization and

Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 1

Anna Korba: Wasserstein gradient flows and applications to sampling in machine learning - Lecture 1

CONFERENCE Recording during the thematic meeting : « Frontiers in interacting particle systems, aggregation-diffusion ...

Wuchen Li: "Accelerated Information Gradient Flow"

Wuchen Li: "Accelerated Information Gradient Flow"

High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning ...

Geometric Aspects of Sampling and Optimization

Geometric Aspects of Sampling and Optimization

Philippe Rigollet (MIT) https://simons.berkeley.edu/talks/geometric-aspects-

(De)regularized Wasserstein Gradient Flows via Reproducing Kernels

(De)regularized Wasserstein Gradient Flows via Reproducing Kernels

TITLE: (De)regularized Wasserstein

On the geometry of Stein variational gradient descent and related ensemble sampling methods

On the geometry of Stein variational gradient descent and related ensemble sampling methods

Seminar by Andrew Duncan at the UCL Centre for AI. Recorded on the 24th February 2021. Abstract Bayesian

Wasserstein gradient flows for machine learning by Anna Korba

Wasserstein gradient flows for machine learning by Anna Korba

Minisymposia: Wasserstein

On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic

On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic

Michael Jordan, UC Berkeley Computational Challenges in Machine ...

Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods

Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods

Jeremias Knoblauch, How Wasserstein Gradient Flows connect Deep Ensembles and Bayesian Methods

Sinho Chewi Optimal transport and high dimensional probability Gradient Flows on Wasserstein Spa

Sinho Chewi Optimal transport and high dimensional probability Gradient Flows on Wasserstein Spa

Sinho Chewi MIT, USA.

Peng Chen - Projected Variational Methods for High-dimensional Bayesian Inference

Peng Chen - Projected Variational Methods for High-dimensional Bayesian Inference

This talk was part of the Workshop on "PDE-constrained Bayesian inverse problems: interplay of spatial statistical models with ...