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Previous Seminars

Massively Parallel Evolutionary Computation for Empowering Electoral Reform

Fri, Feb 21, 2020, 12:15 pm
Important insights into redistricting can be gained by formulating and analyzing the problem with a Markov Chain Monte Carlo framework that utilizes optimization heuristics to inform transition proposals.

Wearable Brain-Machine Interface Architectures for Neurocognitive Stress

Mon, Feb 10, 2020, 4:30 pm
The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse...

Scalable Semidefinite Programming

Mon, Feb 10, 2020, 4:00 pm
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This talk develops new provably correct algorithms for solving large SDP problems by economizing on both the storage and the arithmetic costs. We present two methods: one based on sketching, and the other on...

Targeted Machine Learning for Causal Inference

Fri, Feb 7, 2020, 12:00 pm
We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for infinite dimensional models.

Recent Advances in Non-Convex Distributed Optimization and Learning

Mon, Nov 18, 2019, 4:30 pm
We consider a class of distributed non-convex optimization problems, in which a number of agents are connected by a communication network, and they collectively optimize a sum of (possibly non-convex and non-smooth) local objective functions. This type of problem has gained some recent popularities, especially in the application of distributed...

Can learning theory resist deep learning?

Fri, Nov 15, 2019, 12:30 pm
Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world...

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Thu, Nov 14, 2019, 4:30 pm
Stochastic iterative methods lie at the core of large-scale optimization and its modern applications to data science. Though such algorithms are routinely and successfully used in practice on highly irregular problems (e.g. deep neural networks), few performance guarantees are available outside of smooth or convex settings. In this talk, I will...

Exploration by Optimization in Partial Monitoring

Tue, Nov 12, 2019, 4:30 pm

In many real-world problems learners cannot directly observe their own rewards but can still infer whether some particular action is successful. How should a learner take actions to balance its need of information while maximizing their reward in this setting? Partial monitoring is a framework introduced a few decades ago to model learning...


Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning

Mon, Nov 11, 2019, 4:00 pm
Tensor decomposition discovers latent structure in higher-order data sets and is the higher-order analogue of the matrix decomposition.

Algorithm and Statistical Inference for Recovery of Discrete Structure

Fri, Nov 8, 2019, 12:30 pm
Discrete structure recovery is an important topic in modern high-dimensional inference. Examples of discrete structure include clustering labels, ranks of players, and signs of variables in a regression model.