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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: CSML Seminar Series

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: CSML Seminar Series

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: CSML Seminar Series

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: CSML Seminar Series

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: CSML Seminar Series

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: CSML Seminar Series

AI Journey with Intel Workshop

AI Journey with Intel Workshop
Michael Zephyr,  AI Developer Evangelist of Intel, will offer a survey of the company's AI tools including Intel® Optimized Tensorflow* (CPU-only), Intel OpenVino™ Toolkit for computer vision, and Intel® AI libraries. The workshop is open to all members of the campus research community. Please see the attached poster for speaker bio and more...
Location: 399 Julis Romo Rabinowitz Building
Speaker(s):
Michael Zephyr
AI Developer Evangelist of Intel
AI Journey with Intel Workshop
Tags: Research Computing Workshop

TensorFlow and PyTorch User Group

TensorFlow and PyTorch User Group
JAX is a system for high-performance machine learning research. It offers the familiarity of Python+NumPy and the speed of hardware accelerators, and it enables the definition and the composition of function transformations useful for machine-learning programs. In particular, these transformations include automatic differentiation, automatic...
Location: 120 Lewis Science Library
Speaker(s):
Peter Hawkins
Google AI Princeton
JAX: Accelerated machine-learning research via composable function transformations in Python
Tags: Research Computing Workshop

DataX Research Funding Information Session

DataX Research Funding Information Session

Thinking of applying for DataX research funding but would like more information? Join us for an information session!

The session aims to:

DataX Research Funding Information Session

DataX Research Funding Information Session

Thinking of applying for DataX research funding but would like more information? Join us for an information session!

The session aims to:

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