Upcoming Events

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Thu, Nov 14, 2019, 4:30 pm

Abstract:

Location: B205 Engineering Quadrangle
Speaker(s):
Dmitriy Drusvyatskiy
University of Washington
Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Tags: Seminars

Can learning theory resist deep learning?

Can learning theory resist deep learning?
Fri, Nov 15, 2019, 12:30 pm

Abstract: 

Location: CS 105
Speaker(s):
Francis Bach
INRIA
Tags: Seminars

Diving into TensorFlow 2.0

Diving into TensorFlow 2.0
Fri, Nov 15, 2019, 2:00 pm

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: Seminars

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
Mon, Nov 18, 2019, 4:30 pm
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: Seminars
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Events Archive

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: Seminars

TensorFlow & PyTorch User Group Talks [Two 20-minute talks]

TensorFlow & PyTorch User Group Talks [Two 20-minute talks]

TensorFlow & PyTorch User Group Talks [Two 20-minute talks]

Location: 138 Lewis Science Library
Speaker(s):
Alex Beatson
Computer Science
Accelerating automated modeling and design with stochastic optimization and neural networks 
Mark Ho
Computational Cognitive Science Lab, Department of Psychology
Modeling Human Sequential Decision-Making
Tags: Seminars

Meisam Razaviyayn

Meisam Razaviyayn
Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-concave regime for which a global equilibrium solution can be computed efficiently. In this talk, we study the problem in the non-convex regime and show that an $\epsilon...
Location: Equad B205
Speaker(s):
Meisam Razaviyayn
University of Southern California
Tags: Seminars

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands
We study deep neural networks and their use in semiparametric inference. We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid...
Location: Sherrerd 101
Speaker(s):
Max H. Farrell, Associate Professor of Econometrics and Statistics
University of Chicago
Tags: Seminars

Control with Learning On the Fly: First Toy Problems

Control with Learning On the Fly: First Toy Problems
How can we control a system without knowing beforehand what the controls do? In particular, how should we balance the imperatives to "explore" (learn what the controls do) and "exploit" (use what we've learned so far to make the system do what we want)? We won't have enough data to apply deep learning. The talk poses several toy problems and...
Location: Sherrerd 101
Speaker(s):
Charles Fefferman
Math Department, Princeton University
Tags: Seminars

Provable Reinforcement Learning From Small Data

Provable Reinforcement Learning From Small Data
Recent years have witnessed increasing empirical successes in reinforcement learning (RL). However, many theoretical questions about RL were not well understood. For example, how many observations are necessary and sufficient for learning a good policy? How to learn to control using structural information with provable regret? In this talk, we...
Location: B205 Engineering Quadrangle
Speaker(s):
Mengdi Wang
Operations Research and Financial Engineering at Princeton University
Tags: Seminars

Beyond Supervised Learning for Biomedical Imaging

Beyond Supervised Learning for Biomedical Imaging
Many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are ill-posed inverse problems. In this talk, I will present a flexible machine learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will...
Location: B205 Engineering Quadrangle
Speaker(s):
Mert R. Sabuncu
Cornell University’s School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering
Tags: Seminars

Prediction with Confidence – General Framework for Predictive Inference

Prediction with Confidence – General Framework for Predictive Inference
We propose a general framework for prediction in which a prediction is in the form of a distribution function, called ‘predictive distribution function’. This predictive distribution function is well suited for prescribing the notion of confidence under the frequentist interpretation and  providing meaningful answers for prediction-related...
Location: Corwin Hall, Room 127
Speaker(s):
Regina Y. Liu
Rutgers, Department of Statistics
Tags: Seminars

AI Journey with Intel Workshop

AI Journey with Intel Workshop

AI Journey with Intel Workshop

Tuesday, September 17, 12:00-2:00 pm

399 Julis Romo Rabinowitz Building

[Lunch will be provided. RSVP now!]

Location: 399 Julis Romo Rabinowitz Building
Speaker(s):
Michael Zephyr
AI Developer Evangelist of Intel
AI Journey with Intel Workshop
Tags: Seminars

TensorFlow and PyTorch User Group

TensorFlow and PyTorch User Group

TensorFlow and PyTorch User Group

JAX: Accelerated machine-learning research via composable function transformations in Python
Thursday, September 12, 4:30-5:30 pm, 120 Lewis Science Library
Peter Hawkins, Google AI Princeton

Location: 120 Lewis Science Library
Speaker(s):
Peter Hawkins
Google AI Princeton
JAX: Accelerated machine-learning research via composable function transformations in Python
Tags: Seminars

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