Christopher Jung

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 34

Top Topics

Conformal Prediction

Differential Privacy

Facility Location

Allocation Problems

Top Venues

Publications

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