Upcoming Events

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Events Archive

Scalable Semidefinite Programming

Scalable Semidefinite Programming
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...
Location: 214 Fine Hall
Speaker(s):
Madeleine Udell
Cornell University
Tags: CSML Seminar Series

Targeted Machine Learning for Causal Inference

Targeted Machine Learning for Causal Inference
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.
Location: 399 Julis Romo Rabinowitz
Speaker(s):
Mark van der Laan
UC Berkeley, Biostatistics/Statistics
Tags: CSML Seminar Series

Generalized Lagrangian Networks

Generalized Lagrangian Networks

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? Recent work (Greydanus et al. 2019) proposed Hamiltonian Neural Networks (HNNs) which can learn the Hamiltonian of a physical system from data using a neural network.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Sam Greydanus
Google AI
Tags: ML for the Sciences

Solving Inverse Problems with Data-driven Priors

Solving Inverse Problems with Data-driven Priors

I will present a Bayesian machine learning architecture that combines a physically motivated parameterization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Peter Melchior
Princeton University
Tags: ML for the Sciences

Barks, Bubbles and Brownies!

Barks, Bubbles and Brownies!

We will have therapy dogs available to help calm you, bubble tea to hydrate you and brownies to assist with that chocolate fix.

RSVP not necessary but first come, first serve for the bubble tea!

Location: 26 Prospect Ave

Machine Learning for the Sciences

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103
Tags: ML for the Sciences

Grad Students: Interested in Data Science?

Grad Students: Interested in Data Science?

Center for Statistics and Machine Learning is holding an informal graduate information session about its certificate program.

Lunch will be served!

Location: 26 Prospect Ave

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
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...
Location: B205 Engineering Quadrangle
Speaker(s):
Mingyi Hong
University of Minnesota
Tags: CSML Seminar Series

Diving into TensorFlow 2.0

Diving into TensorFlow 2.0

Description: Please join us for this 90-minute workshop, taught at an intermediate level. We will briefly introduce TensorFlow 2.0, then dive in to writing a few flavors of neural networks. Attendees will need a laptop and an internet connection.

Location: Lewis Science Library 138
Speaker(s):
Josh Gordon
Google
Diving in to TensorFlow 2.0
Tags: Research Computing Workshop

Can learning theory resist deep learning?

Can learning theory resist deep learning?
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...
Location: CS 105
Speaker(s):
Francis Bach
INRIA
Tags: CSML Seminar Series

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