# Spring 2017

February 8, 2017

### Maximum likelihood estimation of determinantal point processes

Speaker: Victor-Emmanuel Brunel (Math)

Determinantal point processes (DPPs) are a very useful and elegant tool to model repulsive interactions, hence they have become very popular in data science and machine learning, among other fields. In a learning prospective, many estimators...February 15, 2017

### Censored Demand Estimation in Retail

Speaker: Jehangir Amjad (LIDS)

Our goal is to estimate the true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. We introduce a framework to make inference from multiple...February 22, 2017

### Peter Krafft - talk canceled

Speaker: Peter Krafft - talk canceled (Media Lab)

Today's LIDS & Stats Tea talk has been canceled - our apologies for any inconvenience.March 1, 2017

### Communication complexity and lifting theorems

Speaker: Shalev Ben-David (CSAIL)

I'll talk about some recent developments in the field of communication complexity. Specifically, I'll explain the concept of a lifting theorem, which connects the communication complexity model to the more tractable query complexity model...March 8, 2017

### The minimax algorithm for a sequential prediction game with square loss

Speaker: Alan Malek (IDSS)

Consider the following game-theoretic model of sequential prediction: at every round 1 through T, the learner plays an action, the opponent observes this action and plays a response, and the learner incurs the square difference as a loss....March 22, 2017

### Principal Differences Analysis: Interpretable Characterization of Differences between Distributions

Speaker: Jonas Mueller (CSAIL)

I will introduce principal differences analysis (PDA), a method for analyzing differences between high-dimensional distributions which operates by finding the projection that maximizes the statistical divergence between the resulting...April 5, 2017

### High Dimensional Linear Regression: Mean Squared Error and Phase Transitions

Speaker: Ilias Zadik (ORC)

In this talk we will focus on the sparse high dimensional regression Y=X\beta^{*}+W where X is a n\times pmatrix with i.i.d. standard normal entries, W is a n\times 1 vector with i.i.d. N(0,\sigma^{2}) entries and \beta^{*} is a p\times 1...April 12, 2017

### Graph Signal Processing with Applications to Network Topology Inference

Speaker: Santiago Segarra (IDSS)

Advancing a holistic theory of networks necessitates fundamental breakthroughs in modeling, identiﬁcation, and controllability of distributed network processes – often conceptualized as signals deﬁned on the vertices of a graph. Under the...