Christopher Jung
csj93 at stanford.edu
I'm a postdoctoral researcher at Stanford University, hosted by Omer Reingold.
Previously, I completed my PhD at UPenn where I was fortunate to be advised by Aaron Roth and Michael Kearns. Before Penn, 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.
I'm on the 2023-2024 industry job market.
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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