Seminars

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Previous Seminars

Binary star science with sparse, noisy, and missing data

Fri, Feb 22, 2019, 2:00 pm to 3:00 pm
Many open questions in astrophysics hinge on a better understanding of binary star populations (their occurrence rate, orbital parameter distributions, and how these vary with environment).Yet, most of what we know about binary star statistics comes from just a few hundred stars nearest to the sun. Contemporary astronomical surveys have the...
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Machine Learning and Causal Inference for Heterogeneous Treatment Effects

Fri, Feb 22, 2019, 12:00 pm to 1:00 pm
This talk will review recently developed methods to apply machine learning methods to causal inference problems, including the problems of estimating heterogeneous treatment effects, for example, in A/B testing, as well as in estimating optimal treatment assignment policies.
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Decoding the Milky Way Galaxy

Fri, Feb 15, 2019, 2:00 pm to 3:00 pm
Stars residing in the Milky Way halo hold the key to the origin of our Galaxy. The stellar halo has traditionally been inaccessible as it contains only one percent of stars in the Galaxy. However, novel data sets have enabled us to confidently identify thousands of halo stars in a high-dimensional space of stellar positions, velocities and...

Sparse matrices in sparse analysis

Wed, Feb 13, 2019, 4:30 pm
In this talk, I will give two vignettes on the theme of sparse matrices in sparse analysis. The first vignette covers work from compressive sensing in which we want to design sparse matrices (i.e., matrices with few non-zero entries) that we use to (linearly) sense or measure compressible signals.
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Rethinking the Role of Optimization in Learning

Mon, Feb 11, 2019, 4:30 pm
In this talk, I will overview our recent progress towards understanding how we learn large capacity machine learning models, especially deep neural networks. In the modern practice of deep learning, many successful models have far more trainable parameters compared to the number of training examples.
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Astrophysics as a Testbed for Statistical Method Development

Wed, Feb 6, 2019, 11:00 am

There have been many efforts to apply methods from machine learning and statistics to make discoveries in astrophysics and throughout the physical sciences. While it is clear that the use of these methods has advanced our science goals, I will argue that these collaborations can also advance research in machine learning.

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The Many Faces of Regularization: from Signal Recovery to Online Algorithms

Wed, Jan 16, 2019, 4:30 pm

In optimization, regularization plays several distinct roles. In the first part of the talk, we consider sample-efficient recovery of signals with low-dimensional structure, which is ill-posed without regularization.

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Machine Learning and the Physical World

Mon, Dec 10, 2018, 4:00 pm

Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling).

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Seminar: Latent statistical structure in large-scale neural data: how to find it, and when to believe it

Tue, Oct 16, 2018, 4:30 pm

One central challenge in neuroscience is to understand how neural populations represent and produce the remarkable computational abilities of our brains.  Indeed, neuroscientists increasingly form scientific hypotheses that can only be studied at the level of the neural population, and exciting new large-scale datasets have followed. ...

Deep Learning and Cognition

Thu, Nov 16, 2017, 12:30 pm

Co-sponsored Computer Science and CSML Seminars

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