Upcoming Events

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Events Archive

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):

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
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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):

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
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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
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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
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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
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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
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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

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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