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 18, 2019

Convex Relaxation of Combined Heat and Power Dispatch

Yibao Jiang (LIDS)

Combined heat and power dispatch are utilized for coordinated scheduling of electric power systems and district heating systems via interactions through combined heat and power plants and heat pumps. Nonlinear heating flow models generally make the...

September 25, 2019

Bounding Machine Learning Model Errors in Power Grid Analysis

Yuxiao Liu (LIDS)

Machine learning methods analysis power grids under incomplete physical information, and their accuracy has been mostly validated empirically using excessive testing datasets. We explore error bounds for machine learning methods under all possible...

October 2, 2019

Efficient Learning from Untrusted Batches

Sitan Chen (CSAIL)

In recent years there has been an explosion of interest in designing unsupervised learning algorithms able to tolerate adversarial corruptions in the input data. A notable instantiation of this, first introduced to the theory community by Qiao and...

October 9, 2019

Genomic Variety Estimation via Bayesian Nonparametrics

Lorenzo Masoero (EECS)

The exponential growth in size of human genomic studies, with tens of thousands of observations,  opens up the intriguing possibility to investigate the role of rare genetic variants in biological human evolution. A better understanding of rare...

October 16, 2019

Learning L2 Continuous Regression Functionals via Regularized Riesz Representers

Rahul Singh (Economics)

Many objects of interest can be expressed as an L2 continuous functional of regression, including average treatment effects, economic average consumer surplus, expected conditional covariances, and discrete choice parameters that depend on...

October 23, 2019

LR-GLM: High-Dimensional Bayesian Inference Using Low Rank Data Approximations

Brian Trippe (CSAIL)

Due to the ease of modern data collection, practitioners often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an...

October 30, 2019

Randomized Control Meets Synthetic Control

Anish Agrawal (LIDS)

Consider the setting where there are N units and I interventions (i.e., treatments) of interest. The aim is to find the best-personalized intervention for each unit. When the units are homogenous, the randomized control trial (RCT) framework (i.e.,...

November 6, 2019

The Distributed Setup, Some Definitions and Problem Statements

Cesar A Uribe (LIDS)

Cesar will show some recent results on the study of the optimal convergence rates for distributed convex optimization problems over networks, where the objective is to minimize a sum of local functions of the nodes in the network. We provide optimal...

November 13, 2019

WiFresh: Age-of-Information from Theory to Implementation

Igor Kadota (LIDS)

Emerging applications, such as autonomous vehicles and smart factories, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated...

November 20, 2019

Learning an Unknown Network State in Congestion Games

Manxi Wu (IDSS)

We study learning dynamics induced by myopic players who repeatedly play a congestion game on a network with an unknown state. The state impacts the cost functions of one or more edges of the network. In each stage, players choose equilibrium...

December 4, 2019

Permutation-Based Causal Structure Learning from Interventional Data with Unknown Targets

Chandler Squires (LIDS)

Recent innovations in gene editing and gene sequencing technologies have opened the door to developing a more complete understanding of gene regulatory networks, with applications for disease diagnosis, drug development, and biochemical engineering...