78 citations · 165 across the 14 of their papers we have counts for
19 papers
Convex Dataset Valuation for Post-Training
Siqi Zeng, Christopher Jung, Rui Li +7
Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post-training. In practice, however, developers face constraints on compute, l…
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
Meitong Liu, Christopher Jung, Rui Li +2
In transfer learning, the learner leverages auxiliary data to improve generalization on a main task. However, the precise theoretical understanding of when and how auxiliary data h…
Cross-attention Secretly Performs Orthogonal Alignment in Recommendation Models
Hyunin Lee, Yong Zhang, Hoang Vu Nguyen +8
Cross-domain sequential recommendation (CDSR) aims to align heterogeneous user behavior sequences collected from different domains. While cross-attention is widely used to enhance…
Oracle Efficient Online Multicalibration and Omniprediction
Sumegha Garg, Christopher Jung, Omer Reingold +1
A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous…
Batch Multivalid Conformal Prediction
Christopher Jung, Georgy Noarov, Ramya Ramalingam +1
We develop fast distribution-free conformal prediction algorithms for obtaining multivalid coverage on exchangeable data in the batch setting. Multivalid coverage guarantees are st…
Multicalibrated Regression for Downstream Fairness
Ira Globus-Harris, Varun Gupta, Christopher Jung +3
We show how to take a regression function that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier sati…
Practical Adversarial Multivalid Conformal Prediction
Osbert Bastani, Varun Gupta, Christopher Jung +3
We give a simple, generic conformal prediction method for sequential prediction that achieves target empirical coverage guarantees against adversarially chosen data. It is computat…
Distributionally Robust Data Join
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset. What is the most principle…
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for seq…
Online Multivalid Learning: Means, Moments, and Prediction Intervals
Varun Gupta, Christopher Jung, Georgy Noarov +2
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples…
Moment Multicalibration for Uncertainty Estimation
Christopher Jung, Changhwa Lee, Mallesh M. Pai +2
We show how to achieve the notion of "multicalibration" from Hébert-Johnson et al. [2018] not just for means, but also for variances and other higher moments. Informally, it means…
Fair Prediction with Endogenous Behavior
Christopher Jung, Sampath Kannan, Changhwa Lee +3
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fair…
Metric-Free Individual Fairness in Online Learning
Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu
We study an online learning problem subject to the constraint of individual fairness, which requires that similar individuals are treated similarly. Unlike prior work on individual…
A New Analysis of Differential Privacy's Generalization Guarantees
Christopher Jung, Katrina Ligett, Seth Neel +3
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially priva…
A Center in Your Neighborhood: Fairness in Facility Location
Christopher Jung, Sampath Kannan, Neil Lutz
When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more com…
Quantifying the Burden of Exploration and the Unfairness of Free Riding
Christopher Jung, Sampath Kannan, Neil Lutz
We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have different decision makers…
Fair Algorithms for Learning in Allocation Problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung +4
Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objec…
Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns +1
We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown simil…