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September 2019

Automated Data Summarization for Scalability in Bayesian Inference

Tamara Broderick (MIT)

September 11 @ 4:00 pm - 5:00 pm

IDS.190 - Topics in Bayesian Modeling and Computation Abstract: Many algorithms take prohibitively long to run on modern, large datasets. But even in complex data sets, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a "coreset") that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time, but…

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Probabilistic Modeling meets Deep Learning using TensorFlow Probability

Brian Patton (Google AI)

September 18 @ 4:00 pm - 5:00 pm

IDS.190 - Topics in Bayesian Modeling and Computation Speaker: Brian Patton (Google AI) Abstract: TensorFlow Probability provides a toolkit to enable researchers and practitioners to integrate uncertainty with gradient-based deep learning on modern accelerators. In this talk we'll walk through some practical problems addressed using TFP; discuss the high-level interfaces, goals, and principles of the library; and touch on some recent innovations in describing probabilistic graphical models. Time-permitting, we may touch on a couple areas of research interest for the…

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Some New Insights On Transfer Learning

Samory Kpotufe (Columbia)

September 20 @ 11:00 am - 12:00 pm

Abstract: The problem of transfer and domain adaptation is ubiquitous in machine learning and concerns situations where predictive technologies, trained on a given source dataset, have to be transferred to a new target domain that is somewhat related. For example, transferring voice recognition trained on American English accents to apply to Scottish accents, with minimal retraining. A first challenge is to understand how to properly model the ‘distance’ between source and target domains, viewed as probability distributions over a feature…

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Frontiers of Efficient Neural-Network Learnability

Adam Klivans (UT Austin)

September 27 @ 11:00 am - 12:00 pm

Abstract: What are the most expressive classes of neural networks that can be learned, provably, in polynomial-time in a distribution-free setting? In this talk we give the first efficient algorithm for learning neural networks with two nonlinear layers using tools for solving isotonic regression, a nonconvex (but tractable) optimization problem. If we further assume the distribution is symmetric, we obtain the first efficient algorithm for recovering the parameters of a one-layer convolutional network. These results implicitly make use of a…

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Data Science and Big Data Analytics: Making Data-Driven Decisions

September 30

The seven-week course launches September 30, 2019. This course was developed by over ten MIT faculty members at IDSS. It is specially designed for data scientists, business analysts, engineers, and technical managers looking to learn the latest theories and strategies to harness data.

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October 2019

Behavior of the Gibbs Sampler in the Imbalanced Case/Bias Correction from Daily Min and Max Temperature Measurements

Natesh Pillai (Harvard)

October 2 @ 4:00 pm - 5:00 pm

IDS.190 Topics in Bayesian Modeling and Computation *Note:  The speaker this week will give two shorter talks within the usual session Title:   Behavior of the Gibbs sampler in the imbalanced case Abstract:   Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty. However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and…

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Probabilistic Programming and Artificial Intelligence

Vikash Mansinghka (MIT)

October 9 @ 4:00 pm - 5:00 pm

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: Probabilistic programming is an emerging field at the intersection of programming languages, probability theory, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data that is too small and/or messy for standard approaches from machine learning and…

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The Planted Matching Problem

Cristopher Moore (Santa Fe Institute)

October 11 @ 11:00 am - 12:00 pm

Abstract: What happens when an optimization problem has a good solution built into it, but which is partly obscured by randomness? Here we revisit a classic polynomial-time problem, the minimum perfect matching problem on bipartite graphs. If the edges have random weights in , Mézard and Parisi — and then Aldous, rigorously — showed that the minimum matching has expected weight zeta(2) = pi^2/6. We consider a “planted” version where a particular matching has weights drawn from an exponential distribution…

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Markov Chain Monte Carlo Methods and Some Attempts at Parallelizing Them

Pierre E. Jacob (Harvard University)

October 16 @ 4:00 pm - 5:00 pm

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: MCMC methods yield approximations that converge to quantities of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal: it stands at odds with current trends in computing hardware. Namely, it would often be computationally preferable to run many short chains in parallel, but such an approach is flawed because of the so-called "burn-in" bias.  This talk will first describe that issue and some known…

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Towards Robust Statistical Learning Theory

Stanislav Minsker (USC)

October 18 @ 11:00 am - 12:00 pm

Abstract:  Real-world data typically do not fit statistical models or satisfy assumptions underlying the theory exactly, hence reducing the number and strictness of these assumptions helps to lessen the gap between the “mathematical” world and the “real” world. The concept of robustness, in particular, robustness to outliers, plays the central role in understanding this gap. The goal of the talk is to introduce the principles and robust algorithms based on these principles that can be applied in the general framework of statistical…

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