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March 2021

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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Localization, Uniform Convexity, and Star Aggregation

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

Suhas Vijaykumar (MIT Sloan)

Zoom

ABSTRACT Offset Rademacher complexities have been shown to imply sharp, data-dependent upper bounds for the square loss in a broad class of problems including improper statistical learning and online learning. We show that in the statistical setting, the offset complexity upper bound can be generalized to any loss satisfying a certain uniform curvature condition; this condition is shown to also capture exponential concavity and self-concordance, uniting several apparently disparate results. By a unified geometric argument, these bounds translate to improper…

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December 2020

LIDS & Stats Tea Talk – Xiang Cheng (LIDS)

December 9, 2020 @ 4:00 pm - 4:30 pm

Xiang Cheng (LIDS)

Online

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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Train Simultaneously, Generalize Better: Stability of Gradient-Based Minimax Learners

December 2, 2020 @ 4:00 pm - 4:30 pm

Farzan Farnia (LIDS)

Online

ABSTRACT The success of minimax learning problems of generative adversarial networks (GANs) and adversarial training has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed and robustness properties of the underlying optimization algorithm. In this talk, we present theoretical and numerical results indicating that the optimization algorithm also plays a key role in the generalization performance of the trained minimax model. To this end, we analyze the…

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November 2020

Sensor-based Control for Fast and Agile Aerial Robotics

November 18, 2020 @ 4:00 pm - 4:30 pm

Ezra Tal (LIDS)

Online

ABSTRACT In recent years, autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., fast and agile) maneuvers have attracted significant attention. We focus on the design of control algorithms for accurate tracking of such maneuvers. This problem is complicated by aerodynamic effects that significantly impact vehicle dynamics at high speeds. In contrast, typical multicopter controllers that operate at low speeds may neglect vehicle aerodynamics all together. We propose a sensor-based approach to account for high-speed aerodynamics. Our controller directly…

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