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October 2021

Breaking the Sample Size Barrier in Reinforcement Learning

October 15, 2021 @ 11:00 am - 12:00 pm

Yuting Wei (Wharton School at UPenn )

E18-304

Abstract: Reinforcement learning (RL), which is frequently modeled as sequential learning and decision making in the face of uncertainty, is garnering growing interest in recent years due to its remarkable success in practice. In contemporary RL applications, it is increasingly more common to encounter environments with prohibitively large state and action space, thus imposing stringent requirements on the sample efficiency of the RL algorithms in use. Despite the empirical success, however, the theoretical underpinnings for many popular RL algorithms remain…

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LIDS & Stats Tea Talk – Anish Agarwal

October 13, 2021 @ 4:00 pm - 4:30 pm

Anish Agarwal (LIDS & IDSS)

32 – LIDS Lounge

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Webinar: Applications of Data Science & Machine Learning

October 12, 2021 @ 11:00 am - 12:00 pm

Philippe Rigollet (MIT)

online

In an exclusive live session, Prof. Philippe Rigollet (Mathematics, MIT) will explain how the online 'Data Science and Machine Learning: Making Data-Driven Decisions' program helps real people make real-world, data-informed decisions.

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MechE Colloquium: Likelihood-weighted active learning with application to Bayesian optimization, uncertainty quantification, and decision making in high dimensions

October 8, 2021 @ 4:00 pm - 5:00 pm

Themis Sapsis (MIT)

3-270

In this talk we introduce a new class of acquisition functions that utilize a likelihood-weighted ratio that accounts for the importance of the output relative to the input.

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Recent results in planted assignment problems

October 8, 2021 @ 11:00 am - 12:00 pm

Yihong Wu (Yale University)

E18-304

Abstract: Motivated by applications such as particle tracking, network de-anonymization, and computer vision, a recent thread of research is devoted to statistical models of assignment problems, in which the data are random weight graphs correlated with the latent permutation. In contrast to problems such as planted clique or stochastic block model, the major difference here is the lack of low-rank structures, which brings forth new challenges in both statistical analysis and algorithm design.   In the first half of the…

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LIDS & Stats Tea Talk – Yi Tian

October 6, 2021 @ 4:00 am - 4:30 pm

Yi Tian (LIDS & IDSS)

32 – LIDS Lounge

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Causal Matrix Completion

October 1, 2021 @ 11:00 am - 12:00 pm

Devavrat Shah (MIT)

E18-304

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing completely atrandom” (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of “latent confounders”, i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix.  In general, these…

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September 2021

Representation Learning to Elucidate the Disease Mechanisms in Atrial Fibrillation – Jameel Clinic Seminar Series

September 29, 2021 @ 4:00 pm

Caroline Uhler (IDSS & EECS)

32-G449

For more information, please visit the Jameel Clinic website.

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Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework

September 29, 2021 @ 4:00 pm - 4:30 pm

Xinyu Wu (LIDS & IDSS)

32 – LIDS Lounge

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Perturbations, Therapeutics, and Machine Learning – Workshop organized by Jameel Clinic and Eric and Wendy Schmidt Center

September 28, 2021 @ 8:45 am - 5:00 pm

Caroline Uhler, Devavrat Shah, Regina Barzilay, Stefanie Jegelka, Tommi Jaakkola (MIT)

Online

IDSS's Caroline Uhler, Devavrat Shah, Regina Barzilay, Stefanie Jegelka, and Tommi Jaakkola participate in the online Perturbations, Therapeutics, and Machine Learning workshop on September 28, 2021.

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