November 2019
LIDS Seminar – Sujay Sanghavi (University of Texas at Austin)
Sujay Sanghavi (University of Texas at Austin)
32-155
TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.
Find out more »Understanding machine learning with statistical physics
Lenka Zdeborová (Institute of Theoretical Physics, CNRS)
E18-304
Abstract: The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. Current theoretical challenges and open questions about deep learning and statistical learning call for unified account of the following three ingredients: (a) the dynamics of the learning algorithm, (b) the architecture of the neural networks, and (c) the structure of the data. Most existing theories are not taking in account all of those…
Find out more »Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox
Xiao-Li Meng (Harvard University)
E18-304
Abstract: Advances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed,this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman, 2013). We first use the modeling-what-we-ignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory, which is essentially the same…
Find out more »SES PhD Admissions Info Session
Ali Jadbabaie (MIT)
E18-304
Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student who introduce the program and answer your questions. Please register in advance.
Find out more »SDP Relaxation for Learning Discrete Structures: Optimal Rates, Hidden Integrality, and Semirandom Robustness
Yudong Chen (Cornell University)
E18-304
Abstract: We consider the problems of learning discrete structures from network data under statistical settings. Popular examples include various block models, Z2 synchronization and mixture models. Semidefinite programming (SDP) relaxation has emerged as a versatile and robust approach to these problems. We show that despite being a relaxation, SDP achieves the optimal Bayes error rate in terms of distance to the target solution. Moreover, SDP relaxation is provably robust under the so-called semirandom model, which frustrates many existing algorithms. Our…
Find out more »Probabilistic Inference and Learning with Stein’s Method
Lester Mackey (Microsoft Research)
37-212
IDS.190 – Topics in Bayesian Modeling and Computation **PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** Speaker: Lester Mackey (Microsoft Research) Abstract: Stein’s method is a powerful tool from probability theory for bounding the distance between probability distributions. In this talk, I’ll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures. I’ll highlight applications to Markov chain sampler selection, goodness-of-fit testing, variational…
Find out more »One-shot Information Theory via Poisson Processes
Cheuk Ting Li (UC Berkeley)
E18-304
Abstract: In information theory, coding theorems are usually proved in the asymptotic regime where the blocklength tends to infinity. While there are techniques for finite blocklength analysis, they are often more complex than their asymptotic counterparts. In this talk, we study the use of Poisson processes in proving coding theorems, which not only gives sharp one-shot and finite blocklength results, but also gives significantly shorter proofs than conventional asymptotic techniques in some settings. Instead of using fixed-size random codebooks, we…
Find out more »Causal Inference in the Age of Big Data
Jasjeet Sekhon (UC Berkeley)
E18-304
The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social, behavioral, and medical treatments. With the availability of fine-grained data, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges, Machine Learning (ML) is now being…
Find out more »SES PhD Admissions Webinar (updated start time)
Ali Jadbabaie (MIT)
Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty who introduces the program and answers your questions. Please register in advance.
Find out more »LIDS@80: A Celebration
Tang Building (E51)
We are pleased to announce that registration is now open for the LIDS 80th-anniversary celebration. This free event will take place on November 1-2, 2019 at MIT. Advance registration is required. Registration closes on October 3, 2019.
Find out more »