PHYSTAT: 2 Sample Testing and Goodness of Fit

Contribution List

1. Introduction

Louis Lyons (Imperial College and Oxford)

01/06/2023, 09:00

Session 1a

2. Range of Machine Learning methods in Particle Physics

Ben Nachman (Lawrence Berkeley National Lab)

01/06/2023, 09:15

Session 1a

3. Discussion time

01/06/2023, 09:45

Session 1a

4. Goodness of fit by Neyman-Pearson testing

Gaia Grosso (Universita e INFN, Padova (IT))

01/06/2023, 10:00

Session 1a

5. Discussion time

01/06/2023, 10:30

Session 1a

6. Applications to deep generative models

Raghav Kansal (Univ. of California San Diego (US))

01/06/2023, 11:15

Session 1b

7. Discussion time

01/06/2023, 11:45

Session 1b

8. Optimal Transport for Goodness of Fit and 2-sample testing

Larry Wasserman (Carnegie Mellon University)

01/06/2023, 12:00

Session 1b

9. Final discussion time on day 1

01/06/2023, 12:30

Session 1b

10. Multivariate model assessment without chi-squared

Sara Algeri (University of Minnesota)

02/06/2023, 09:00

Session 2a

11. Discussion time

02/06/2023, 09:30

Session 2a

12. Kernel methods for 2-sample and goodness of fit testing

Arthur Gretton (UCL)

02/06/2023, 09:45

Session 2a

13. Discussion time

02/06/2023, 10:15

Session 2a

14. Comparing distributions of high dimensional complex data

Ann B Lee (Carnegie Mellon University)

02/06/2023, 11:00

Session 2b

15. Discussion time

02/06/2023, 11:30

Session 2b

16. Classifier-based 2 sample testing for model independent searches for New Physics

Mikael Kuusela (Carnegie Mellon University)

02/06/2023, 11:45

Session 2b

17. Final discussion time on Day 2

02/06/2023, 12:15

Session 2b

18. Closing of workshop

02/06/2023, 12:45

Session 2b