Upcoming Events

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Events Archive

Algorithm and Statistical Inference for Recovery of Discrete Structure

Discrete structure recovery is an important topic in modern high-dimensional inference. Examples of discrete structure include clustering labels, ranks of players, and signs of variables in a regression model.
Location: Sherrerd 101
Speaker(s):

Optimizing for Fairness in ML

Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be.
Location: Sherrerd 101
Speaker(s):

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research

We consider the properties and performance of word embeddings techniques in the context of political science research. In particular, we explore key parameter choices—including context window length, embedding vector dimensions and the use of pre-trained vs locally fit variants—with respect to efficiency and quality of inferences possible with...
Location: Corwin Hall, Room 127
Speaker(s):

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

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

Accelerating automated modeling and design with stochastic optimization and neural networks (20-minute talk) Modeling Human Sequential Decision-Making (20-minute talk)
Location: 138 Lewis Science Library
Speaker(s):

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
Speaker(s):

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
Speaker(s):

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