Upcoming Seminars

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Previous Seminars

Control with Learning On the Fly: First Toy Problems

Thu, Oct 10, 2019, 4:30 pm
How can we control a system without knowing beforehand what the controls do? In particular, how should we balance the imperatives to "explore" (learn what the controls do) and "exploit" (use what we've learned so far to make the system do what we want)? We won't have enough data to apply deep learning. The talk poses several toy problems and...

Provable Reinforcement Learning From Small Data

Fri, Oct 4, 2019, 4:30 pm
Recent years have witnessed increasing empirical successes in reinforcement learning (RL). However, many theoretical questions about RL were not well understood. For example, how many observations are necessary and sufficient for learning a good policy? How to learn to control using structural information with provable regret? In this talk, we...

Beyond Supervised Learning for Biomedical Imaging

Wed, Oct 2, 2019, 4:30 pm
Many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are ill-posed inverse problems. In this talk, I will present a flexible machine learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will...

Prediction with Confidence – General Framework for Predictive Inference

Fri, Sep 27, 2019, 12:00 pm
We propose a general framework for prediction in which a prediction is in the form of a distribution function, called ‘predictive distribution function’. This predictive distribution function is well suited for prescribing the notion of confidence under the frequentist interpretation and  providing meaningful answers for prediction-related...

Multiscale Model Reduction in Physics with Deep Networks

Mon, May 13, 2019, 4:15 pm
Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning and physics. This talk shows deep convolutional neural network architectures take advantage of scale separation, symmetries and sparse representations. We introduce simplified architectures which can be analyzed mathematically. Scale...

Inherent Trade-Offs in Algorithmic Fairness

Fri, Mar 29, 2019, 12:00 pm to 1:00 pm
Recent discussion in the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs...

Physics-informed Machine Learning For Climate, Urban, And Environmental Sciences

Thu, Mar 14, 2019, 12:30 pm to 1:30 pm
At the confluence of scientific simulation and modern machine learning there exists an opportunity to develop a “middle path” that leverages the strengths of both approaches to build machine-learning emulators of numerical simulation models. From the perspective of machine learning, incorporating simulation data may significantly reduce the need...

Cosmology with galaxy surveys: from precision to accuracy with data-driven models

Tue, Mar 12, 2019, 3:00 pm to 4:00 pm
Immense surveys of the night sky are being conducted to test the origins and evolutions of the Universe as a whole, using distant galaxies and quasars (bright galaxies with active supermassive black holes at their center). This requires modeling the spatial and spectral statistics of those sources in detail using millions of noisy observations,...

Fitting Convex Sets to Data

Mon, Mar 11, 2019, 4:30 pm to 5:30 pm
A number of problems in signal processing may be viewed conceptually as fitting a convex set to data.  In vision and learning, the task of identifying a collection of features or atoms that provide a concise description of a dataset has been widely studied under the title of dictionary learning or sparse coding.  In convex-geometric terms, this...

The Complexities of Astronomical Data

Fri, Mar 8, 2019, 2:00 pm to 3:00 pm
The analysis of astronomical data set reveals an astonishing diversity of astrophysical processes but also a dizzying array of observational complications. I will introduce several approaches to deal with complications like missing data, non-linear error propagation, and overlapping objects. I will concentrate on proximal optimization algorithms...