Upcoming Events

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Events Archive

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
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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
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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
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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
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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

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

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

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
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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
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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
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