Events at the Center for Statistics and Machine Learning are currently being scheduled. Please check back here later this month for events.

In this talk, recent results on various aspects of the Empirical Risk Minimization (ERM) problem with Relative Entropy Regularization (ERM-RER) are presented. The regularization is with respect to a sigma-finite measure, instead of a probability measure, which provides a larger flexibility for including prior knowledge on the models. Special cases of this general formulation include the ERM problem with (discrete or differential) entropy regularization and the information-risk minimization problem.

This online workshop provides an interdisciplinary venue for diagnosing and addressing reproducibility failures in ML-based science. We especially welcome researchers outside traditional ML fields who are interested in applying ML methods in their own fields. Participants will learn to identify reproducibility failures in their own fields and ensure that their research is reproducible.

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

A good learning algorithm is characterized primarily by its ability to predict beyond the training data, i.e., its ability to generalize. What makes a learning algorithm have the ability to generalize? And can we predict when a learning algorithm will generalize well? We believe that a clear answer to these questions is still elusive. In this talk, we will share some perspectives based on our work to understand generalization in deep learning algorithms.

The school will run in person June 13 to June 17, 2022 and is aimed at PhD students interested in machine learning theory. The primary goal is to showcase, through four main courses, a range of exciting recent developments in the subject. The primary focus this year is on theoretical advances in deep learning. An…

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).