Search Results for author: Serena Wang

Found 15 papers, 5 papers with code

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

no code implementations24 May 2023 Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan

From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare.

Causal Inference counterfactual

Lost in Translation: Reimagining the Machine Learning Life Cycle in Education

no code implementations8 Sep 2022 Lydia T. Liu, Serena Wang, Tolani Britton, Rediet Abebe

We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions.


Robust Distillation for Worst-class Performance

no code implementations13 Jun 2022 Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon

We show empirically that our robust distillation techniques not only achieve better worst-class performance, but also lead to Pareto improvement in the tradeoff between overall performance and worst-class performance compared to other baseline methods.

Knowledge Distillation

Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence

no code implementations30 Jun 2021 Ghassen Jerfel, Serena Wang, Clara Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence.

Bayesian Inference Variational Inference

Multi-Source Causal Inference Using Control Variates

no code implementations30 Mar 2021 Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan

Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.

Causal Inference Epidemiology +2

Regularization Strategies for Quantile Regression

no code implementations9 Feb 2021 Taman Narayan, Serena Wang, Kevin Canini, Maya Gupta

We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile.

Fairness regression

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

no code implementations NeurIPS 2020 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.


Robust Optimization for Fairness with Noisy Protected Groups

1 code implementation NeurIPS 2020 Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael. I. Jordan

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.


Deontological Ethics By Monotonicity Shape Constraints

1 code implementation31 Jan 2020 Serena Wang, Maya Gupta

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes."

Ethics Fairness

Pairwise Fairness for Ranking and Regression

1 code implementation12 Jun 2019 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.

Fairness General Classification +1

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

1 code implementation11 Sep 2018 Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya Gupta, Seungil You, Karthik Sridharan

This new formulation leads to an algorithm that 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.


Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

1 code implementation29 Jun 2018 Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You

Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals.


Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization

no code implementations28 Jun 2018 Serena Wang, Maya Gupta, Seungil You

Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble are evaluated.

Combinatorial Optimization

Proxy Fairness

no code implementations28 Jun 2018 Maya Gupta, Andrew Cotter, Mahdi Milani Fard, Serena Wang

We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime.


Interpretable Set Functions

no code implementations31 May 2018 Andrew Cotter, Maya Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu

We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label.

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