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.
Events
Upcoming Events
Fri
Mar 29
11:00 AM
EER 1.516 (North Tower)
Wed
Apr 10
12:00 PM
EER 0.706
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.
Fri
Apr 12
11:00 AM
EER 1.516 (North Tower)
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.
Fri
Apr 19
11:00 AM
EER 1.516 (North Tower)
Adding a new sensing dimension to soft electronics: from the skin to below the skin
Recent Events
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.
25 Jan 2019
*PLEASE NOTE CORRECTION: Seminar will take place in EER 3.646 (North Tower)
14 Dec 2018
The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. These measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. Existing systems operate far away from these physical limits.