Past Events

The 13th annual Texas Wireless Summit (TWS) provides a forum on emerging technology and business models for industry leaders and academics. Hosted by the University of Texas at Austin's Wireless Networking and Communications Group (WNCG), the Summit offers direct access to cutting-edge research and innovations from industry leaders, investors, academics and startups.

Abstract: Given samples from an unknown distribution, p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far from every distribution in C, by at least ε in total variation distance? This fundamental question has received substantial attention in Statistics and Computer Science. Nevertheless, even for basic classes of distributions such as monotone, log-concave, unimodal, or product, the optimal sample complexity is unknown. We provide optimal testers for these families. (joint work with Jayadev Acharya and Gautam Kamath).  

We consider the task of summing (integrating) a non-negative function over a discrete domain, e.g., to compute the partition function of a graphical model.

Fitting a low-rank matrix to data is a fundamental and widely used primitive in machine learning. For most problems beyond the very basic PCA, theoretically sound methods have overwhelmingly combined statistical models of the data with convex optimization. As the size and dimensionality of data increases, this approach is overly computationally wasteful, not least because it represents an nr dimensional object with n^2 parameters. 

Anonymous messaging platforms, such as Secret, Whisper and Yik Yak, have emerged as important social media for sharing one's thoughts without the fear of being judged by friends, family, or the public. Further, such anonymous platforms are crucial in nations with authoritarian governments, where the right to free expression and sometimes the personal safety of the message author depends on anonymity.

Modern datasets are rapidly growing in size and complexity, and this wealth of data holds the promise for many transformational applications. Machine learning is seemingly poised to deliver on this promise, having proposed and rigorously evaluated a wide range of data processing techniques over the past several decades. However, concerns over scalability and usability present major roadblocks to the wider adoption of these methods.

Hosted by the Department of Mathematics and the Wireless Networking and Communications Group (WNCG) at the University of Texas at Austin, the Simons Conference brings together a diverse set of world-class researchers from various scientific communities to foster interdisciplinary engagement.

A fundamental problem in Markov Chains is estimating the probability of transitioning to a given terminal state in k steps from some initial distribution. This has received added attention in recent years due to the success of PageRank and related Markov-Chain based centrality measures for networks. Standard approaches to this problem use linear-algebraic methods (power iteration) or Monte Carlo.

Due to the privacy concerns of existing centralized Online Social Networks (OSN),  researchers and developers have tried to design, implement and deploy decentralized social networks (DSN) in  recent years. Despite numerous attempts and efforts, only a small portion of those projects have managed to achieve actual deployment status and none but one of them have more than one million users.

Join CalTech's Dr. Quentin Berthet in this one-hour seminar. We study the detection problem of finding planted solutions in random instances of flat satisfiability problems, a generalization of boolean satisfiability formulas. We describe the properties of random instances of flat satisfiability, as well of the optimal rates of detection of the associated hypothesis testing problem. We also study the performance of an algorithmically efficient testing procedure.