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Inherent Trade-Offs in Algorithmic Fairness

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...
Speaker(s):
Jon Kleinberg
Departments of Computer Science and Information Science at Cornell University

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

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...
Speaker(s):
Adrian Albert
Research Scientist, Lawrence Berkeley National Laboratory

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

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,...
Speaker(s):
Boris Leistedt
Center of Cosmology and Particle Physics at New York University

Fitting Convex Sets to Data

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...
Speaker(s):
Venkat Chandrasekaran, Professor of Computing and Mathematical Sciences and Electrical Engineering
CalTech

The Complexities of Astronomical Data

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...
Speaker(s):
Peter Melchior
Department of Astrophysical Sciences

Astrophysical Inference with Complex, Stochastic Time Series

Astrophysical Inference with Complex, Stochastic Time Series
Tue, Feb 26, 2019, 3:00 pm to 4:00 pm
Astronomical time series—measurements of a celestial source’s brightness as a function of time—are key to probing the physical processes governing the behaviour of these sources on a large range of scales, from small asteroids in our solar system, to supermassive black holes at the centres of galaxies. Modern time domain surveys like the Zwicky...
Speaker(s):
Daniela Huppenkothen
DIRAC Institute and eScience Institute, University of Washington

Binary star science with sparse, noisy, and missing data

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...
Speaker(s):
Adrian Price-Whelan
Lyman Spitzer, Jr. Postdoctoral Fellow

Machine Learning and Causal Inference for Heterogeneous Treatment Effects

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.
Speaker(s):
Susan Athey
The Economics of Technology Professor, Stanford Graduate School of Business

Decoding the Milky Way Galaxy

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...
Speaker(s):
Ana Bonaca
Institute for Theory and Computation, Harvard-Smithsonian Center for Astrophysics
Decoding the Milky Way Galaxy

Sparse matrices in sparse analysis

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.
Speaker(s):
Anna Gilbert
University of Michigan
Sparse matrices in sparse analysis

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