Spring 2018

February 7, 2018 to February 8, 2018

On Unlimited Sampling

Speaker: Ayush Bhandari (MIT Media Lab)

Shannon's sampling theorem provides a link between the continuous and the discrete realms stating that bandlimited signals are uniquely determined by its values on a discrete set. This theorem is realized in practice using so called analog-...

February 28, 2018 to March 1, 2018

Breaking the n^(-1/2) Barrier for Permutation-Based Ranking Models

Speaker: Cheng Mao (Mathematics Department, MIT)

There has been a recent surge of interest in studying permutation-based models, such as the noisy sorting (NS) model and the strong stochastic transitivity (SST) model, for ranking from pairwise comparisons. Although permutation-based...

March 7, 2018 to March 8, 2018

Private Sequential Learning

Speaker: Zhi Xu (LIDS)

We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, v*, by sequentially querying an external...

March 14, 2018 to March 15, 2018

High Dimensional Linear Regression using Lattice Basis Reduction

Speaker: Ilias Zadik (ORC)

In this talk, we focus on the high dimensional linear regression problem where the goal is to efficiently recover an unknown vector β* from n noisy linear observations Y = Xβ* + W ∈ ℝn, for known X ∈ ℝn × p and unknown W ∈ ℝn. Unlike most of...

March 21, 2018 to March 22, 2018

Relaxed Locally Correctable Codes

Speaker: Govind Ramnarayan (CSAIL)

Locally decodable codes (resp. locally correctable codes), or LDCs (resp. LCCs), are codes for which individual symbols of the message (resp. codeword) and be recovered by reading just a few bits from a noisy codeword, which is corrupted...

April 4, 2018 to April 5, 2018

Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models

Speaker: Raj Agrawal (LIDS)

Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships, but traditional methods can fail in modern applications, which often exhibit a larger number of observed variables than data...

April 11, 2018 to April 12, 2018

INSPECTRE: Privately Estimating the Unseen

Speaker: Gautam Kamath (CSAIL)

Gautam Kamath will discuss some his recent work on estimating distribution properties, with the additional constraint of guaranteeing the privacy of the sample. Some properties of interest include entropy, support size, and support coverage...

April 18, 2018 to April 19, 2018

Time Series Analysis via Matrix Estimation

Speaker: Anish Agarwal (LIDS)

We consider the task of interpolating and forecasting a time series in the presence of noise and missing data. As the main contribution of this work, we introduce an algorithm that transforms the observed time series into a matrix, utilizes...

May 9, 2018 to May 10, 2018

A Dynamical Systems Approach to Minimizing PAPR

Speaker: Omer Tanovic (LIDS)

In this talk, we consider a problem of designing discrete-time systems which are optimal in frequency-weighted least squares sense subject to a maximal output amplitude constraint. It can be shown for such problems, in general, that the...

May 16, 2018 to May 17, 2018

A Directional Approach to Uncertainty Representation for Robotics

Speaker: Igor Gilitschenski (CSAIL)

Robotic Perception tasks typically involve processing uncertain quantities that are defined on inherently nonlinear domains. The most important among these domains involve the 2d and 3d manifolds of orientations and poses (which combines...