Upcoming Seminars

Mar 29
11:00 AM - 12:00 PM
EER 1.516 (North Tower)
Anshumali Shrivastava
Assistant Professor, Rice University
In this talk, I will discuss some of my recent and surprising findings on the use of hashing algorithms for large-scale estimations. Locality Sensitive Hashing (LSH) is a hugely popular algorithm for sub-linear near neighbor search. However, it turns out that fundamentally LSH is a constant time (amortized) adaptive sampler from which efficient near-neighbor search is one of the many possibilities. Our observation adds another feather in the cap for LSH. LSH offers a unique capability to do smart sampling and statistical estimations at the cost of few hash lookups.
Apr 10
12:00 PM - 1:00 PM
EER 0.706
Natasha Devroye
Associate Professor, University of Illinois at Chicago
Information theory can characterize one-way, non-interactive communication, where one source sends a message to one destination, very well. When the communication is interactive, as in (1) channels with feedback, or (2) two-way channels where two users exchange messages over a shared channel, much less is understood. We outline what is understood about interactive communications as in (1) and (2) from an information theoretic perspective, and why they are so challenging to characterize. Many open problems and connections to related fields will be presented.
Apr 12
11:00 AM - 12:00 PM
EER 1.516 (North Tower)
Mylene C.Q. Farias
Associate Professor, University of Brasilia (UnB)

The great progress achieved by communications in the last twenty years can be attested by the amount of audio-visual multimedia services available nowadays, such as digital television and IP-based video transmission. The success of these kind of services relies on their trustworthiness and the delivered quality of experience. Therefore, the development of efficient real-time quality monitoring tools that can quantify the audio-visual experience (as perceived by the end user) is key to the success of any multimedia service or application.

Apr 19
11:00 AM - 12:00 PM
EER 1.516 (North Tower)
Sheng Xu
Assistant Professor, University of California, San Diego

Adding a new sensing dimension to soft electronics: from the skin to below the skin

Recent Seminars

15 Mar 2019

Abstract: Approximate probabilistic inference is a key computational task in modern machine learning, which allows us to reason with complex, structured, hierarchical (deep) probabilistic models to extract information and quantify uncertainty.

22 Feb 2019

Abstract: We present a new statistical framework to quantify uncertainty (UQ) for recovering low-rank matrices from incomplete and noisy observations. We further develop a sequential active sampling approach guided by the uncertainties. The motivation comes from two related and widely studied problems, matrix completion, which aims to recover a low-rank matrix X from a partial, noisy observation of its entries, and low-rank matrix recovery, which recovers X from a set of linear combination its entries with additive noise.

01 Feb 2019

Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems. The premise is that despite nonconvexity, the loss function may possess benign geometric properties that enable fast global convergence under carefully designed initializations, such as local strong convexity, local restricted convexity, etc.