19 citations · 31 across the 12 of their papers we have counts for
12 papers
The price of multi-group transductive learning
Noah Bergam, Samuel Deng, Daniel Hsu
We show every multi-group learner in the leave-one-out transductive setting may incur a surprisingly large multiplicative penalty in its error rate on some group relative to the er…
A One-Inclusion Graph Approach to Multi-Group Learning
Noah Bergam, Samuel Deng, Daniel Hsu
We prove the tightest-known upper bounds on the sample complexity of multi-group learning. Our algorithm extends the one-inclusion graph prediction strategy using a generalization…
Group-realizable multi-group learning by minimizing empirical risk
Navid Ardeshir, Samuel Deng, Daniel Hsu +1
The sample complexity of multi-group learning is shown to improve in the group-realizable setting over the agnostic setting, even when the family of groups is infinite so long as i…
Group-wise oracle-efficient algorithms for online multi-group learning
Samuel Deng, Daniel Hsu, Jingwen Liu
We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possi…
Multi-group Learning for Hierarchical Groups
Samuel Deng, Daniel Hsu
The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend…
Group conditional validity via multi-group learning
Samuel Deng, Navid Ardeshir, Daniel Hsu
We consider the problem of distribution-free conformal prediction and the criterion of group conditional validity. This criterion is motivated by many practical scenarios including…
Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu +1
We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume th…
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng +3
Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from 400 AI engineers, each of wh…
Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg +6
A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algori…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks
Samuel Deng, Sanjam Garg, Somesh Jha +3
Poisoning attacks have emerged as a significant security threat to machine learning algorithms. It has been demonstrated that adversaries who make small changes to the training set…
Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
Samuel Deng, Achille Varzi
In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions. The purp…