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

February 5, 2020

Towards Testing Monotonicity of Distributions Over General Posets

Maryam Aliakbarpour (CSAIL)

In this talk, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is monotone if, for any pair of domain elements x and y such that x \preceq y, p(x) \leq p(y)....

February 12, 2020

Scalable Convex Optimization with Applications to Semidefinite Programming

Alp Yurtsever (LIDS)

Semidefinite programming is a powerful framework from convex optimization that has striking potential for data science applications. Even so, practitioners often critique this approach by asserting that it is not possible to solve semidefinite...

February 19, 2020

Belief Shaping Through Signaling in Non-Cooperative Multi-Agent Environments

Muhammed Sayin (LIDS)

In this talk, we will analyze the interaction between intelligent and selfish agents in non-cooperative environments with a specific focus on the transmission of some private information among them. We will seek to quantify the ability of informed...

February 26, 2020

Multi-Trek Separation in Linear Structural Equation Models 

Jean Baptiste Seby (IDSS)

Building on the theory of causal discovery from observational data, we study interactions between multiple (sets of) random variables in a linear structural equation model with non-Gaussian error terms. We give a correspondence between the structure...

March 4, 2020

tspDB: Time Series Predict Database

Anish Agarwal (LIDS)

A major bottleneck of the current Machine Learning workflow is the time consuming and error-prone engineering required to get data from a DB/warehouse so that an appropriately chosen prediction method can be applied to it. To address this challenge...

April 1, 2020

Via Zoom: Graph Matrices: Norm Bounds and Applications

Kwangjun Ahn (LIDS)

Graph matrices are a type of random matrix which has the following properties: (i) The entries of the matrix depend on random input. Moreover, this dependence can be described by a small graph. (ii) The matrix (as a function of the input) is...

April 8, 2020

Via Zoom: A Koopman Framework for Sampling in Stochastic Differential Equations

Benjamin Zhang (Center for Computational Science & Engineering and AeroAstro)

We propose a general framework to construct efficient sampling methods for stochastic differential equations (SDEs) using eigenfunctions of the system’s Koopman operator. Importance sampling for SDEs is typically done by adding a control term in the...

April 15, 2020

Via Zoom: Random Osborne: A Simple, Practical Algorithm for Matrix Balancing in Near-Linear Time

Jason Altschuler (LIDS)

We revisit Matrix Balancing, a pre-conditioning task used ubiquitously for computing eigenvalues and matrix exponentials. Since 1960, Osborne's algorithm has been the practitioners' algorithm of choice, and is now implemented in most numerical...

April 22, 2020

Via Zoom: Incentive-Aware Contextual Pricing with Non-Parametric Market Noise

Jason Cheuk Nam Liang (LIDS & ORC)

Motivated by the availability of the massive amount of real-time data in online advertising markets, we study a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term...

April 29, 2020

Via Zoom: Optimizing Capacity Restoration Service Time for Flow Batteries Participating in Energy Arbitrage Markets

Mehdi Jafari (LIDS)

Redox flow battery (RFB) is one of the emerging technologies in the energy storage systems area. Different chemistries of this technology shows promising performance and lifetime features that can potentially compete with Li-ion batteries in...

May 6, 2020

Via Zoom: A Theory of Uncertainty Variables for State Estimation and Inference

Rajat Talak (LIDS)

Rajat will discuss a new framework of uncertainty variables to model uncertainty. An uncertainty variable is characterized by a set in which its realization is bound to lie. Conditional uncertainty, on the other hand, is characterized by a set-map,...