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

October 4, 2023

Learning threshold neurons via the Edge of Stability, and Sharpness Aware Minimization

Kwangjun Ahn (LIDS)

n this talk, I'll be talking about interesting phenomena arising in Deep Learning optimization. In particular, based on a simple yet canonical model, we will discuss how the neural networks learn the "correct" classifiers. Surprisingly,...

October 24, 2023

Resilience of Electric Vehicle Charging in Cold Climate

Guangchun (Grant) Ruan (LIDS)

The performance and service life of electric vehicle (EV) batteries degrade deadly in cold climate, and heating is a common-but-expensive option to survive EVs in this case. Coordinating the heating and charging demand at scale has great potential...

October 25, 2023

Sample Complexity Bounds for Estimating Probability Divergences under Invariances

Behrooz Tahmasebi (CSAIL)

Group-invariant probability distributions appear in many data-generative models in machine learning, such as graphs, point clouds, and images. In practice, one often needs to estimate divergences between such distributions. In this work, we study...

October 2, 2024

LIDS & Stats Tea Talk

Swati Padmanabhan (LIDS, IDSS)

"Some recent theoretical results in algorithms for nonconvex optimization" In this talk, we present some recent results on the theoretical guarantees for nonconvex optimization. We will first briefly describe first-order algorithms for attaining...

October 9, 2024

LIDS and Stats Tea Talk

Jiachun Li (LIDS, IDSS)

“The Power of Adaptivity in Experimental Design” Given experiment subjects with potentially heterogeneous covariates and two possible treatments, namely active treatment and control, our work quantifies the optimal accuracy in estimating the...

October 16, 2024

LIDS and Stats Tea Talk

Yassir Jedra (LIDS)

“Exploiting Observation Bias to Improve Matrix Completion” We consider a variant of matrix completion where entries are revealed in a biased manner. Our aim is to address the extent to which such bias can be exploited in improving predictions....

November 13, 2024

LIDS and Stats Tea Talk

Prem Talwai (LIDS, ORC)

“Nonparametric Regression in Dirichlet Spaces: A Random Obstacle Approach” In this paper, we consider nonparametric estimation over general Dirichlet metric measure spaces. Unlike the more commonly studied reproducing kernel Hilbert space, whose...

November 20, 2024

LIDS and Stats Tea Talk

Yuexing Hao (EECS, LIDS)

"Objective Approaches in a Subjective Medical World" In today’s healthcare system, patients often feel disconnected from clinical professionals and their care journey. They receive a “one-size-fits-all” plan and are left out of the decision-making...