Seminars Sorted by Series

Workshop on Mean Curvature and Regularity

Nov
09
2018

Workshop on Mean Curvature and Regularity

Progress in the theory of CMC surfaces in locally homgeneous 3-manifolds X
William Meeks
11:30am|Simonyi Hall 101

Abstract: I will go over some recent work that I have been involved in on surface geometry in complete locally homogeneous 3-manifolds X. In joint work with Mira, Perez and Ros, we have been able to finish a long term project related to the Hopf...

Workshop on Motives, Galois Representations and Cohomology around the Langlands Program

Nov
06
2017

Workshop on Motives, Galois Representations and Cohomology around the Langlands Program

The mod $p$ derived spherical Hecke algebra: structure and applications
Niccolò Ronchetti
2:30pm|S-101

Abstract: I will introduce the mod p derived spherical Hecke algebra of a p-adic group, and discuss its structure via a derived version of the Satake homomorphism. Then, I will survey some speculations about its action on the cohomology of...

Nov
07
2017

Workshop on Motives, Galois Representations and Cohomology around the Langlands Program

Modularity lifting theorems for non-regular symplectic representations
George Boxer
11:30am|S-101

Abstract: We prove an ordinary modularity lifting theorem for certain non-regular 4-dimensional symplectic representations over totally real fields. The argument uses both higher Hida theory and the Calegari-Geraghty version of the Taylor-Wiles...

Nov
08
2017

Workshop on Motives, Galois Representations and Cohomology around the Langlands Program

Topological and arithmetic intersection numbers attached to real quadratic cycles
Henri Darmon
10:00am|S-101

Abstract: I will discuss a recent conjecture formulated in an ongoing project with Jan Vonk relating the intersection numbers of one-dimensional topological cycles on certain Shimura curves to the arithmetic intersections of associated real...

Nov
08
2017

Workshop on Motives, Galois Representations and Cohomology around the Langlands Program

A derived Hecke algebra in the context of the mod $p$ Langlands program
Rachel Ollivier
2:30pm|S-101

Abstract: Given a p-adic reductive group G and its (pro-p) Iwahori-Hecke algebra H, we are interested in the link between the category of smooth representations of G and the category of H-modules. When the field of coefficients has characteristic...

Workshop on New Directions in Optimization, Statistics and Machine Learning

Apr
15
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Towards Robust Artificial Intelligence
Pushmeet Kohli
9:00am|Virtual

Deep learning has led to rapid progress being made in the field of machine learning and artificial intelligence, leading to dramatically improved solutions of many challenging problems such as image understanding, speech recognition, and control...

Apr
15
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Iterative Random Forests (iRF) with applications to genomics and precision medicine
Bin Yu
11:30am|Virtual

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as...

Apr
15
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Generative Modeling by Estimating Gradients of the Data Distribution
Stefano Ermon
2:00pm|Virtual

Existing generative models are typically based on explicit representations of probability distributions (e.g., autoregressive or VAEs) or implicit sampling procedures (e.g., GANs). We propose an alternative approach based on modeling directly the...

Apr
15
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Interpretability for everyone
Been Kim
3:15pm|Virtual

In this talk, I would like to share some of my reflections on the progress made in the field of interpretable machine learning. We will reflect on where we are going as a field, and what are the things that we need to be aware of to make progress...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Do Simpler Models Exist and How Can We Find Them?
Cynthia Rudin
10:00am|Virtual

While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Deep equilibrium models via monotone operators
Zico Kolter
11:15am|Virtual

In this talk, I will first introduce our recent work on the Deep Equilibrium Model (DEQ). Instead of stacking nonlinear layers, as is common in deep learning, this approach finds the equilibrium point of the repeated iteration of a single non-linear...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

The Peculiar Optimization and Regularization Challenges in Multi-Task Learning and Meta-Learning
Chelsea Finn
12:30pm|Virtual

Despite the success of deep learning, much of its success has existed in settings where the goal is to learn one, single-purpose function from data. However, in many contexts, we hope to optimize neural networks for multiple, distinct tasks (i.e...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Modularity, Attention and Credit Assignment: Efficient information dispatching in neural computations
Anirudh Goyal
2:00pm|Virtual

Physical processes in the world often have a modular structure, with complexity emerging through combinations of simpler subsystems. Machine learning seeks to uncover and use regularities in the physical world. Although these regularities manifest...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Tradeoffs between Robustness and Accuracy
Percy Liang
3:15pm|Virtual

Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. While one can train robust models, this often comes at the expense of...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Steps towards more human-like learning in machines
Josh Tenenbaum
4:30pm|Virtual

There are several broad insights we can draw from computational models of human cognition in order to build more human-like forms of machine learning. (1) The brain has a great deal of built-in structure, yet still tremendous need and potential for...

Workshop on New Directions in Reinforcement Learning and Control

Workshop on New Ideas and Tools in Turbulence

Mar
01
2019

Workshop on New Ideas and Tools in Turbulence

Emergence of Multiscaling in a Flow Driven by a Random Force
10:10am|Simonyi Hall 101

We are interested in moments of velocity increments and derivatives, characterized by scaling exponents overline{(v(x + r) − v(x))n} ∝ r^ζn and overline{(∂xvx)n} ∝ Re^ρn , respectively. In high Reynolds number flows, the moments of different orders...

Mar
01
2019

Workshop on New Ideas and Tools in Turbulence

For What It's Worth: An Analyst's Hunt for Asymptotic Heat Transport in Rayleigh-Bénard Convection
Charlie Doering
1:30pm|Simonyi Hall 101

Abstract: The confounding question of asymptotically high Rayleigh number heat transport in Rayleigh-Bénard convection modeled by the Boussineq approximation to the Navier-Stokes equations is reviewed from viewpoints of theory (models of the model)...

Mar
01
2019

Workshop on New Ideas and Tools in Turbulence

Application of machine learning to turbulence modeling
2:10pm|Simonyi Hall 101

Abstract: I will discuss some preliminary work on using machine learning
to produce turbulence models that can be used in large eddy simulation.
I will discuss how better models can be constructed and in general,
how one can use machine learning to...