# Upcoming Events

*Princeton University is actively monitoring the situation around coronavirus (COVID-19) and the evolving guidance from government and health authorities. The latest guidance for Princeton members and visitors is available on the University’s Emergency Management website. *

No upcoming events found.

## 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):

Chao Gao

Assistant Professor, Statistics University of Chicago

Tags:
CSML Seminar Series

### 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):

Elisa Celis

Assistant Professor of Statistics and Data Science, Yale University

Tags:
CSML Seminar Series

### Machine Learning for the Sciences

*Taking place every other Friday. Lunch will be provided.*

Location:
26 Prospect Ave, Auditorium 103

Tags:
ML for the Sciences

### 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):

Arthur Spirling

New York University

Tags:
CSML Seminar Series

### Machine Learning for the Sciences

*Taking place every other Friday. Lunch will be provided.*

Location:
26 Prospect Ave, Auditorium 103

Tags:
ML for the Sciences

### 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):

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

### 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

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

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

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