Talks

Seminars, colloquiums, and tutorials

Z. Goldfeld,
“Gromov-Wasserstein alignment: statistical and computational advancements via duality”. [Video]
Given during October 2023 — May 2024 at:
1) CAM Colloquium, Center of Applied Mathematics, Cornell University, Ithaca, New York, US.
2) The Kantorovich Initiative, University of Washington, Seattle, Washington, US.
3) Institute for Machine Learning (IFML) Seminar, UT Austin, Austin, Texas, US.
4) North American School of Information Theory (NASIT-2023), University of Pennsylvania, Philadelphia, Pennsylvania, US.
5) Statistics Colloquium, Statistics Department, Penn State University, State College, Pennsylvania, US.
6) Statistics Seminar, SDS Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, US

Z. Goldfeld,
“Gromov-Wasserstein distances: entropic regularization, duality, and sample complexity”.
Information Systems Laboratory (ISL) Colloquium, Stanford University, Stanford, California, US, February 2023.

Z. Goldfeld,
“Statistical and computational aspects of sliced optimal transport”. [Video]
Machine Learning Lunch Seminar, CS Department, Vanderbilt University, Nashville, Tennessee, US, October 2022.

Z. Goldfeld,
“Sliced mutual information: a scalable measure of statistical dependence”. [Video]
Given during May — June 2022 at:
1) IBM Machine Learning Seminar, Online.
2) Workshop on PDE Methods in Data Science and Machine Learning, Fields Institute, Toronto, Canada.

Z. Goldfeld,
“A scalable statistical theory for smooth Wasserstein distances”.
Given during February 2022 at:
1) Statistics Seminar, SDS Department, Cornell University, Ithaca, New York, US.
2) ECE Theory Seminar, ECE Department, UC Santa Cruz, Santa Cruz, California, US.

Z. Goldfeld,
“Scaling Wasserstein distances to high dimensions via smoothing”. [Video]
Given during February — June 2021 at:
1) Math Machine Learning Seminar, Max Planck Institute and UCLA, Los Angeles, California, US.
2) Applied Math Seminar, Beijing International Center for Mathematical Research, Peking University, Beijing, China.
3) ECE Colloquium, ECE Department, University of Iowa, Iowa City, Iowa, US.
4) CS Theory Seminar, CS Department, Columbia University, New York, New York, US.
5) Information Theory Forum, EE Department, Stanford University, Stanford, California, US.

Z. Goldfeld,
“Smoothing probability distributions for high dimensional learning and inference”.
CS Brown Bag Seminar, CS Department, Cornell University, Ithaca, New York, US, December 2020.

Z. Goldfeld,
“Smooth Wasserstein distance: metric structure and statistical efficiency”.
CS Theory Seminar, CS Department, Cornell University, Ithaca, New York, US, April 2020.

Z. Goldfeld,
“Gaussian-smoothed optimal transport: metric structure and statistical efficiency”. [Video]
CAM Colloquium, Center of Applied Mathematics, Cornell University, Ithaca, New York, US, November 2019.

Z. Goldfeld,
“Estimating the flow of information in deep neural networks”.
Given during December 2018 — March 2019 at:
1) ECE Department, Cornell University, Ithaca, New York, US.