LIDS & Stats Tea

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 seminars, please email lids_stats_tea[at]mit[dot]edu

September 15, 2021

Sensitivity and Counterfactual Analysis of Structural Econometric Models

Yaroslav Mukhin (LIDS)

I propose a framework to characterize the sensitivity of estimates and counterfactuals to the distributional assumptions about latent variables in structural econometric models. I characterize the lower and upper bounds on the counterfactual as the...

September 22, 2021

In Defense of Fluid Democracy

Manon Revel (LIDS & IDSS)

In response to the frustration with the current democratic institutions that failed to adapt to the evolving society, alternative decision-making processes emerged in recent years. Requests for more representativity of the citizenry resulted in the...

September 29, 2021

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

Xinyu Wu (LIDS)

Large blackouts in power grids are often the consequence of uncontrolled failure cascades. The ability to predict the failure cascade process in an efficient and accurate manner is important for power system contingency analysis. In this work, we...

October 6, 2021

Online Learning in Unknown Markov Games

Yi Tian (LIDS)

Sequential decision-making in a multi-agent environment arises frequently in the real world. We consider online learning in unknown Markov games: 1) a Markov game is used to model the multi-player dynamic environment, 2) online learning means that...

October 13, 2021

Causal Matrix Completion

Anish Agarwal (LIDS)

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 at random” (MCAR), i.e., each entry is revealed at...

October 20, 2021

Proximal-Primal-Dual Algorithms for Constrained Nonconvex Optimization Problems

Jiawei Zhang (LIDS)

In this talk, we introduce our recent works about proximal-primal-dual algorithms for constrained nonconvex optimization. The augmented Lagrangian method (ALM) and the alternating direction method of multipliers (ADMM) are popular for solving...

October 27, 2021

LIDS & Stats Tea Talk - Dan Wu

Dan Wu (LIDS)

November 3, 2021

LIDS & Stats Tea Talk - Amir Tohidi Kalorazi

Amir Tohidi Kalorazi (LIDS & IDSS)

November 10, 2021

LIDS & Stats Tea Talk - Arnab Kumar Sarker

Arnab Kumar Sarker (IDSS)

November 17, 2021

LIDS & Stats Tea Talk - George Stepaniants

George Stepaniants (Mathematics)

December 1, 2021

LIDS & Stats Tea Talk - Poorya Habibzadeh

Poorya Habibzadeh (LIDS & IDSS)

December 8, 2021

LIDS & Stats Tea Talk - Chenyang Yuan

Chenyang Yuan (LIDS)

December 15, 2021

LIDS & Stats Tea Talk - Zhongxia (Zee) Yan

Zhongxia (Zee) Yan (LIDS)