Christopher Jung

csj93 at stanford.edu

I'm a postdoctoral researcher at Meta. Previously, I was a postdoctoral researcher at Stanford University, hosted by Omer Reingold. And I completed my PhD at UPenn where I was fortunate to be advised by Aaron Roth and Michael Kearns. I studied computer science at Cornell University (Summa Cum Laude with Honors) and worked as a software engineer at APT(Applied Predictive Technologies) which is now part of MasterCard.

Publications

Orthogonal Causal Calibration
With Justin Whitehouse, Justin Whitehouse, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu
Taking a Moment for Robustness
With Jabari Hastings, Charlotte Peale, Vasilis Syrgkanis
Oracle Efficient Online Multicalibration and Omniprediction
With Sumegha Garg, Omer Reingold, Aaron Roth
SODA 2024
Multicalibrated Regression for Downstream Fairness
With Ira Globus-Harris, Varun Gupta, Michael Kearns, Jamie Morgenstern, Aaron Roth
AIES 2023
Distributionally Robust Data Join
With Pranjal Awasthi, Jamie Morgenstern
FORC 2023
Batch Multivalid Conformal Prediction
With Georgy Noarov, Ramya Ramalingam, Aaron Roth
ICLR 2023
Practical Adversarial Multivalid Conformal Prediction
With Osbert Bastani, Varun Gupta, Georgy Noarov, Ramya Ramalingam, Aaron Roth
NeurIPS 2022 (oral presentation)
Online Multivalid Learning: Means, Moments, and Prediction Intervals
With Varun Gupta, Georgy Noarov, Mallesh M. Pai, Aaron Roth
ITCS 2022
Adaptive Machine Unlearning
With Varun Gupta, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
NeurIPS 2021
Moment Multicalibration for Uncertainty Estimation
With Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
COLT 2021
An Algorithmic Framework for Fairness Elicitation
With Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
FORC 2021
Fair Prediction with Endogenous Behavior
With Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
EC 2020
Metric-Free Individual Fairness in Online Learning
With Yahav Bechavod, Zhiwei Steven Wu
NeurIPS 2020 (oral presentation)
A New Analysis of Differential Privacy's Generalization Guarantees
With Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
ITCS 2020
Service in Your Neighborhood: Fairness in Center Location
With Sampath Kannan, Neil Lutz
FORC 2020
Quantifying the Burden of Exploration and the Unfairness of Free Riding
With Sampath Kannan, Neil Lutz
SODA 2020
Fair Algorithms for Learning in Allocation Problems
With Hadi Elzayn, Shahin Jabbari, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
FAT* 2019
Online Learning with an Unknown Fairness Metric
With Stephen Gillen, Michael Kearns, Aaron Roth
NeurIPS 2018