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April 2021

LIDS & Stats Tea Talk – Vishrant Tripathi (EECS/LIDS)

April 21, 2021 @ 4:00 pm - 5:00 pm

Vishrant Tripathi (EECS / LIDS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – Manon Revel (IDSS/LIDS)

April 14, 2021 @ 4:00 pm - 5:00 pm

Manon Revel (IDSS / LIDS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – Jason Altschuler (EECS/LIDS)

April 7, 2021 @ 4:00 pm - 5:00 pm

Jason Altschuler (EECS / LIDS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

Find out more »

March 2021

LIDS & Stats Tea Talk – Andreas Alexander Haupt (IDSS/LIDS)

March 31, 2021 @ 4:00 pm - 5:00 pm

Andreas Alexander Haupt (IDSS / LIDS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – Heng Yang (LIDS)

March 24, 2021 @ 4:00 pm - 5:00 pm

Heng Yang (LIDS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – Ryan Cory-Wright (ORC)

March 17, 2021 @ 4:00 pm - 5:00 pm

Ryan Cory-Wright (ORC)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – James Siderius (EECS)

March 10, 2021 @ 4:00 pm - 5:00 pm

James Siderius (EECS)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware of some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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LIDS & Stats Tea Talk – Raj Agrawal (CSAIL)

March 3, 2021 @ 4:00 pm - 5:00 pm

Raj Agrawal (CSAIL)

Zoom

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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February 2021

LIDS & Stats Tea Talk – Shuvomoy Das Gupta (ORC)

February 24, 2021 @ 4:00 pm - 5:00 pm

Shuvomoy Das Gupta (ORC)

Tea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks, please email lids_stats_tea@mit.edu.

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Generative Adversarial Training for Gaussian Mixture Models

February 17, 2021 @ 4:00 pm - 5:00 pm

Farzan Farnia (LIDS)

Zoom

ABSTRACT Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs achieve great success in learning the complex distribution of image and text data, they perform suboptimally in learning multi-modal distribution-learning benchmarks including Gaussian mixture models (GMMs). In this talk, we propose Generative Adversarial Training for Gaussian Mixture Models (GAT-GMM), a minimax GAN framework for learning GMMs. Motivated by optimal transport theory, we design the…

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