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May 2018

Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions

May 11, 2018 @ 11:00 am - 12:00 pm

Adel Javanmard (USC)

MIT Building E18, Room 304

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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Size-Independent Sample Complexity of Neural Networks

May 4, 2018 @ 11:00 am - 12:00 pm

Ohad Shamir (Weizman Institute)

MIT Building E18, Room 304

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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April 2018

Inference, Computation, and Visualization for Convex Clustering and Biclustering

April 27, 2018 @ 11:00 am - 12:00 pm

Genevera Allen (Rice)

MIT Building E18, Room 304

Abstract: Hierarchical clustering enjoys wide popularity because of its fast computation, ease of interpretation, and appealing visualizations via the dendogram and cluster heatmap. Recently, several have proposed and studied convex clustering and biclustering which, similar in spirit to hierarchical clustering, achieve cluster merges via convex fusion penalties. While these techniques enjoy superior statistical performance, they suffer from slower computation and are not generally conducive to representation as a dendogram. In the first part of the talk, we present new convex…

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Community-based and Peer-to-peer Electricity Markets

April 18, 2018 @ 2:00 pm - 3:00 pm

Pierre Pinson ( Technical University of Denmark )

MIT Building E18, Room 304

Abstract The deployment of distributed renewable generation capacities, new ICT capabilities, as well as a more proactive role of consumers, are all motivating rethinking electricity markets in a more distributed and consumer-centric fashion. After motivating the design of various forms of consumer-centric electricity markets, we will focus on two alternative constructs (which could actually be unified) consisting in community-based and peer-to-peer electricity markets. The mathematical framework for these markets will be described, with focus on negotiation and clearing algorithms in…

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Testing degree corrections in Stochastic Block Models

April 13, 2018 @ 11:00 am - 12:00 pm

Subhabrata Sen (Microsoft)

MIT Building E18, Room 304

Abstract:  The community detection problem has attracted signicant attention in re- cent years, and it has been studied extensively under the framework of a Stochas- tic Block Model (SBM). However, it is well-known that SBMs fit real data very poorly, and various extensions have been suggested to replicate characteristics of real data. The recovered community assignments are often sensitive to the model used, and this naturally begs the following question:  Given a network with community structure, how to decide whether…

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March 2018

When Inference is Tractable

March 16, 2018 @ 11:00 am - 12:00 pm

David Sontag (MIT)

MIT Building E18, Room 304

Abstract: A key capability of artificial intelligence will be the ability to reason about abstract concepts and draw inferences. Where data is limited, probabilistic inference in graphical models provides a powerful framework for performing such reasoning, and can even be used as modules within deep architectures. But, when is probabilistic inference computationally tractable? I will present recent theoretical results that substantially broaden the class of provably tractable models by exploiting model stability (Lang, Sontag, Vijayaraghavan, AI Stats ’18), structure in…

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Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment

March 9, 2018 @ 11:00 am - 12:00 pm

Afonso Bandeira (NYU)

MIT Building E18, Room 304

Abstract: Many problems in signal/image processing, and computer vision amount to estimating a signal, image, or tri-dimensional structure/scene from corrupted measurements. A particularly challenging form of measurement corruption are latent transformations of the underlying signal to be recovered. Many such transformations can be described as a group acting on the object to be recovered. Examples include the Simulatenous Localization and Mapping (SLaM) problem in Robotics and Computer Vision, where pictures of a scene are obtained from different positions andorientations; Cryo-Electron…

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One and two sided composite-composite tests in Gaussian mixture models

March 2, 2018 @ 11:00 am - 12:00 pm

Alexandra Carpentier (Otto von Guericke Universitaet)

MIT Building E18, Room 304

Abstract: Finding an efficient test for a testing problem is often linked to the problem of estimating a given function of the data. When this function is not smooth, it is necessary to approximate it cleverly in order to build good tests. In this talk, we will discuss two specific testing problems in Gaussian mixtures models. In both, the aim is to test the proportion of null means. The aforementioned link between sharp approximation rates of non-smooth objects and minimax testing…

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February 2018

Optimization’s Implicit Gift to Learning: Understanding Optimization Bias as a Key to Generalization

February 23, 2018 @ 11:00 am - 12:00 pm

Nathan Srebro-Bartom (TTI-Chicago)

MIT Building E18, Room 304

Abstract: It is becoming increasingly clear that implicit regularization afforded by the optimization algorithms play a central role in machine learning, and especially so when using large, deep, neural networks. We have a good understanding of the implicit regularization afforded by stochastic approximation algorithms, such as SGD, and as I will review, we understand and can characterize the implicit bias of different algorithms, and can design algorithms with specific biases. But in this talk I will focus on implicit biases…

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User-friendly guarantees for the Langevin Monte Carlo

February 16, 2018 @ 11:00 am - 12:00 pm

Arnak Dalalyan (ENSAE-Crest)

MIT Building E18, Room 304

Abstract: In this talk, I will revisit the recently established theoretical guarantees for the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth and (strongly) log-concave density. I will discuss the existing results when the accuracy of sampling is measured in the Wasserstein distance and provide further insights on relations between, on the one hand, the Langevin Monte Carlo for sampling and, on the other hand, the gradient descent for optimization. I will also present non-asymptotic guarantees for the accuracy…

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Variable selection using presence-only data with applications to biochemistry

February 9, 2018 @ 11:00 am - 12:00 pm

Garvesh Raskutti (University of Wisconsin)

MIT Building E18, Room 304

Abstract: In a number of problems, we are presented with positive and unlabelled data, referred to as presence-only responses. The application I present today involves studying the relationship between protein sequence and function and presence-only data arises since for many experiments it is impossible to obtain a large set of negative (non-functional) sequences. Furthermore, if the number of variables is large and the goal is variable selection (as in this case), a number of statistical and computational challenges arise due…

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Connections between structured estimation and weak submodularity

February 2, 2018 @ 11:00 am - 12:00 pm

Sahand Negahban (Yale University)

MIT Building E18, Room 304

Abstract:  Many modern statistical estimation problems rely on imposing additional structure in order to reduce the statistical complexity and provide interpretability. Unfortunately, these structures often are combinatorial in nature and result in computationally challenging problems. In parallel, the combinatorial optimization community has placed significant effort in developing algorithms that can approximately solve such optimization problems in a computationally efficient manner. The focus of this talk is to expand upon ideas that arise in combinatorial optimization and connect those algorithms and…

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December 2017

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

December 1, 2017 @ 11:00 am - 12:00 pm

Susan Murphy (Harvard)

MIT Building E18, Room 304

Abstract:  A formidable challenge in designing sequential treatments is to  determine when and in which context it is best to deliver treatments.  Consider treatment for individuals struggling with chronic health conditions.  Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment.   That is, the treatment is adapted to the individual’s context; the context may include  current health status, current level of social support and current level of…

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November 2017

Generative Models and Compressed Sensing

November 17, 2017 @ 11:00 am - 12:00 pm

Alex Dimakis (University of Texas at Austin)

MIT Building E18, Room 304

Abstract:  The goal of compressed sensing is to estimate a vector from an under-determined system of noisy linear measurements, by making use of prior knowledge in the relevant domain. For most results in the literature, the structure is represented by sparsity in a well-chosen basis. We show how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all. Instead, we assume that the unknown vectors lie near the range of a generative model, e.g. a GAN…

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SES Admissions Info Session

November 6, 2017 @ 4:00 pm - 6:00 pm

Munther Dahleh, Stephen Graves, Ali Jadbabaie, Jessika Trancik (IDSS)

MIT Building E18, Room 304

Join us for pizza and an Admissions Information Session on the Social and Engineering Systems Doctoral Program.

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Unbiased Markov chain Monte Carlo with couplings

November 1, 2017 @ 11:00 am - 12:00 pm

Pierre Jacob (Harvard)

MIT Building E18, Room 304

Abstract: Markov chain Monte Carlo methods provide consistent approximations of integrals as the number of iterations goes to infinity. However, these estimators are generally biased after any fixed number of iterations, which complicates both parallel computation. In this talk I will explain how to remove this burn-in  bias by using couplings of Markov chains and a telescopic sum argument, inspired by Glynn & Rhee (2014). The resulting unbiased estimators can be computed independently in parallel, and averaged. I will present coupling…

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October 2017

Stochastics and Statistics Seminar – Amit Daniely (Google)

October 27, 2017 @ 11:00 am - 12:00 pm

MIT Building E18, Room 304

Abstract:  Can learning theory, as we know it today, form a theoretical basis for neural networks. I will try to discuss this question in light of two new results — one positive and one negative. Based on joint work with Roy Frostig, Vineet Gupta and Yoram Singer, and with Vitaly Feldman Biography: Amit Daniely is an Assistant Professor at the Hebrew University in Jerusalem, and a research scientist at Google Research, Tel-Aviv. Prior to that, he was a research scientist at Google Research,…

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Inference in dynamical systems and the geometry of learning group actions – Sayan Mukherjee (Duke)

October 20, 2017 @ 11:00 am - 12:00 pm

MIT Building E18, Room 304

Inference in dynamical systems and the geometry of learning group actions – Sayan Mukherjee (Duke)

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Special Stochastics and Statistics Seminar – John Cunningham (Columbia)

October 19, 2017 @ 4:30 pm - 5:30 pm

MIT Building E18, Room 304

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Additivity of Information in Deep Generative Network: The I-MMSE Transform Method – Galen Reeves (Duke University)

October 13, 2017 @ 11:00 am - 12:00 pm

MIT Building E18, Room 304

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Transport maps for Bayesian computation – Youssef Marzouk (MIT)

October 6, 2017 @ 11:00 am - 12:00 pm

MIT Building E18, Room 304

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September 2017

The BLOSSOMS – Augmented World Project – Dr. Miri Barak (Israel Institute of Technology)

September 19, 2017 @ 3:00 pm - 4:00 pm

MIT Building E18, Room 304

Technology-enhanced project-based pedagogy for the promotion of scientific thinking

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New Provable Techniques for Learning and Inference in Probabilistic Graphical Models

September 8, 2017 @ 11:00 am - 12:00 pm

MIT Building E18, Room 304

Speaker: Andrej Risteski (Princeton University) A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the “best fit” model parameters from raw data, while inference is the task of answering probabilistic queries for a model with known parameters (e.g. what is the marginal distribution of a…

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