[Sds-seminars] [Sds-announce] FDS Colloquium, Yo Joong Choe, 1/29/25, KT 13th Floor, Rm. 1327, 12pm-1pm, "Topics in Sequential Anytime-Valid Inference: Comparing Forecasters & Combining Evidence"

[elizavette.torres at yale.edu]

FDS Colloquium

 

Yo Joong "YJ" Choe, University of Chicago



Date: Wednesday, January 29, 2025

Time: 12:00PM to 1:00PM

Location: Kline Tower, 13th Floor, Rm. 1327 See map
<http://maps.google.com/?q=219+Prospect+Street%2C+New+Haven%2C+CT%2C+06511%2
C+us>  

219 Prospect Street

New Haven, CT 06511

 

Webcast:
https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8ec2f9a0-1062-4
bf1-9c92-b259013b41fb 

 

 

Topics in Sequential Anytime-Valid Inference: Comparing Forecasters &
Combining Evidence

 

Information and Abstract: 

Given sequentially observed data, anytime-valid methods guarantee valid
inference at arbitrary stopping times, as opposed to pre-specified sample
sizes, thereby allowing the experimenter to stop experiments early. In this
talk, I will present two recent advances in the emerging field of sequential
anytime-valid inference (SAVI).

 

First, consider two forecasters, each making a prediction for a sequence of
events over time. How can we rigorously compare these forecasters as the
events unfold, while avoiding restrictive assumptions such as stationarity?
I will address this question by designing a novel inference procedure for
estimating the time-varying difference in mean forecast scores. The
procedure utilizes confidence sequences, which are sequences of confidence
intervals that are anytime-valid and can be continuously monitored over
time. I will demonstrate applications of this approach to real-world sports
and weather forecasters.

 

Next, given a composite null hypothesis over sequentially observed data
(e.g., whether high-volatility days are random in a financial time series),
consider two or more testing procedures that are powerful against different
alternatives. How can we combine these procedures so that we can leverage
their collective statistical power, particularly when the procedures are
valid under different information sets (i.e., filtrations)? This general
question arises in various sequential inference problems, such as randomness
testing and multi-step forecast evaluation. I will introduce a simple
solution that allows us to combine arbitrary sequential tests that are based
on e-processes-the SAVI notion of statistical evidence-across different
filtrations. 

 

Speaker bio: Dr. Yo Joong "YJ" Choe is a Postdoctoral Scholar at the
University of Chicago's Data Science Institute. He received a joint Ph.D. in
Statistics and Machine Learning (ML) from Carnegie Mellon University in
2023. His research interests include: (1) reliable evaluation of black-box
forecasters, with a focus on sequential anytime-valid inference (SAVI)
methods, and (2) causal approaches to enhancing the transparency of large
language models (LLMs). His work is published in top journals, such as
Operations Research, as well as major ML conferences, such as NeurIPS, ICML,
and ICLR. Previously, Dr. Choe was a Research Scientist at Kakao and Kakao
Brain, where he developed models and resources for practical natural
language processing (NLP) tasks.

 

Lunch at 11:30am in room 1307
Talk from 12:00-1:00pm in room 1327A

 

For more details and upcoming events visit our website at
<https://statistics.yale.edu/calendar> https://statistics.yale.edu/calendar.


 

Department of Statistics and Data Science

Yale University
Kline Tower

219 Prospect Street
New Haven, CT 06511

 <https://statistics.yale.edu/> https://statistics.yale.edu/

 

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