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 13, 2019

Seeding with Partial Network Data: Hardness and Guarantees

Amin Rahimian (LIDS)

The spread of behavior over social networks depends on the contact structure among individuals, and seeding the most influential agents can substantially enhance the extent of the spread. While the choice of the best seed set, known as influence...

February 20, 2019

Sharing Economy in Energy Systems: An Equilibrium Model in Electricity-Heat Nexus

Jingwei Yang (Tshinghua University)

District heating system is the major form of heat supply in many countries. However, the system lacks the motivation to utilize the thermal energy storage in the heat generation scheduling of combined heat and power plants, which leads to...

February 27, 2019

Understanding How Forced Oscillations Propagate in the Electrical Power Grid

Samuel Chevalier (Mechanical Engineering)

With the recent large-scale deployment of phasor measurement units (PMUs), power systems operators have noticed the persistent presence of low frequency forced oscillations. Due to their extraneous nature, locating the sources of these oscillations...

March 6, 2019

Model Agnostic High-Dimensional Error-In-Variable Regression

Anish Agarwal (LIDS)

We consider the problem of high-dimensional error-in-variable regression where instead of directly observing the covariates, we observe its sparse, noisy version, a common thread of modern datasets. For this setup, we propose an algorithm that...

March 13, 2019

Memorization in Overparameterized Autoencoders

Adit Radha (LIDS & EECS)

Memorization of data in deep neural networks has become a subject of significant research interest. We prove that overparameterized single layer fully connected autoencoders memorize training data: they produce outputs in (a non-linear version of)...

March 20, 2019

Age-Delay Tradeoffs in Queueing Systems

Rajat Talak (LIDS)

Information freshness and low latency communication is important to many emerging applications. Age of Information (AoI) serves as a metric of information freshness, and packet delay is a traditional metric of communication latency. We prove that...

April 3, 2019

Polytopic Trees for Verification and Control of Hybrid Systems

Sadra Sadraddini (CSAIL)

Hybrid systems demonstrate both continuous and discrete behaviors, making it computationally difficult to verify and control. We introduce "polytopic trees”, which is a novel, efficient representation for reachability properties. The central idea is...

April 10, 2019

Online Least-Squares Optimization with Convex Sample-Constraints

Omer Tanovic (LIDS)

We consider infinite-dimensional convex quadratic optimization problems, with shift-invariant quadratic forms, subject to convex sample-wise constraints. From a systems perspective, the latter correspond to those of designing discrete-time systems...

April 16, 2019

Fast Semidefinite Programming In The Large

David Rosen (LIDS)

Convex relaxations based upon semidefinite programming provide a powerful class of techniques for addressing challenging optimization problems across a broad spectrum of disciplines, including combinatorics, semialgebraic geometry, control theory,...

April 17, 2019

Amortized Lower Bounds for Shared Memory Data Types

Siddhartha Jayanti (CSAIL)

As data sets become larger, there is an increasing need to develop parallel algorithms. For this reason, there has been a new burst of interest in efficient data structures designed for asynchronous shared-memory multiprocessors. In this model, the...

April 24, 2019

Hierarchical Bayesian Network Model for Probabilistic Estimation of EV Battery Life

Mehdi Jafari (LIDS)

Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving, recharging and grid services such...

May 1, 2019

Generalization and Learning under Dobrushin's Condition

Yuval Dagan (EECS)

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on learning and...

May 8, 2019

Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model

Espen Flo Boedal (Norwegian University of Science and Technology)

Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making...