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Serena Wang

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5 papers

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NeurIPS Conference 2025 Conference Paper

Metritocracy: Representative Metrics for Lite Benchmarks

Ariel Procaccia
Ben Schiffer
Serena Wang
Shirley Zhang

A common problem in LLM evaluation is how to choose a subset of metrics from a full suite of possible metrics. Subset selection is usually done for efficiency or interpretability reasons, and the goal is often to select a "representative" subset of metrics. However, "representative" is rarely clearly defined. In this work, we use ideas from social choice theory to formalize two notions of representation for the selection of a subset of evaluation metrics. We first introduce positional representation, which guarantees every alternative is sufficiently represented at every position cutoff. We then introduce positional proportionality, which guarantees no alternative is proportionally over- or under-represented by more than a small error at any position. We prove upper and lower bounds on the smallest number of metrics needed to guarantee either of these properties in the worst case. We also study a generalized form of each property that allows for additional input on groups of metrics that must be represented. Finally, we tie theory to practice through real-world case studies on both LLM evaluation and hospital quality evaluation.

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NeurIPS Conference 2020 Conference Paper

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

Harikrishna Narasimhan
Andrew Cotter
Yichen Zhou
Serena Wang
Wenshuo Guo

In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints. In particular, with ranking fairness tasks, there may even be a variable number of constraints, e. g. one for each query in the training set. In these cases, the standard approach of optimizing a Lagrangian while maintaining one Lagrange multiplier per constraint may no longer be practical. Our proposal is to associate a feature vector with each constraint, and to learn a ``multiplier model’’ that maps each such vector to the corresponding Lagrange multiplier. We prove optimality, approximate feasibility and generalization guarantees under assumptions on the flexibility of the multiplier model, and empirically demonstrate that our method is effective on real-world case studies.

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AAAI Conference 2020 Conference Paper

Pairwise Fairness for Ranking and Regression

Harikrishna Narasimhan
Andrew Cotter
Maya Gupta
Serena Wang

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efﬁciently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classiﬁcation. Experiments illustrate the broad applicability and trade-offs of these methods.

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NeurIPS Conference 2020 Conference Paper

Robust Optimization for Fairness with Noisy Protected Groups

Serena Wang
Wenshuo Guo
Harikrishna Narasimhan
Andrew Cotter
Maya Gupta
Michael Jordan

Many existing fairness criteria for machine learning involve equalizing some metric across protected groups such as race or gender. However, practitioners trying to audit or enforce such group-based criteria can easily face the problem of noisy or biased protected group information. First, we study the consequences of naively relying on noisy protected group labels: we provide an upper bound on the fairness violations on the true groups $G$ when the fairness criteria are satisfied on noisy groups $\hat{G}$. Second, we introduce two new approaches using robust optimization that, unlike the naive approach of only relying on $\hat{G}$, are guaranteed to satisfy fairness criteria on the true protected groups $G$ while minimizing a training objective. We provide theoretical guarantees that one such approach converges to an optimal feasible solution. Using two case studies, we show empirically that the robust approaches achieve better true group fairness guarantees than the naive approach.

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JMLR Journal 2019 Journal Article

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

Andrew Cotter
Heinrich Jiang
Maya Gupta
Serena Wang
Taman Narayan
Seungil You
Karthik Sridharan

We show that many machine learning goals can be expressed as “rate constraints” on a model's predictions. We study the problem of training non-convex models subject to these rate constraints (or other non-convex or non-differentiable constraints). In the non-convex setting, the standard approach of Lagrange multipliers may fail. Furthermore, if the constraints are non-differentiable, then one cannot optimize the Lagrangian with gradient-based methods. To solve these issues, we introduce a new “proxy-Lagrangian” formulation. This leads to an algorithm that, assuming access to an optimization oracle, produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem. We then give a procedure that shrinks the randomized solution down to a mixture of at most $m+1$ deterministic solutions, given $m$ constraints. This culminates in a procedure that can solve non-convex constrained optimization problems with possibly non-differentiable and non-convex constraints, and enjoys theoretical guarantees. We provide extensive experimental results covering a broad range of policy goals, including various fairness metrics, accuracy, coverage, recall, and churn. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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