Serena Lutong Wang
Serena Wang joined Google Research in 2016, where she works with Maya Gupta on building controllable and interpretable machine learning systems. She is also currently a PhD student in EECS at UC Berkeley advised by Michael Jordan, supported by the NSF Graduate Research Fellowship. She received a B.A. in Computer Science from Harvard University, where she worked in the intersection of machine learning and Operating Systems. Serena has presented workshops at Grace Hopper 2018 and ODSC West 2017.
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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 and feasibility 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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Pairwise Fairness for Ranking and Regression
Andy Cotter
Maya Gupta
33rd AAAI Conference on Artificial Intelligence (2020)
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Improving fairness for ranking and regression models has less mature algorithmic tooling than classifiers. Here, we present pairwise formulations of fairness for ranking and regression models that can express analogues of statistical fairness notions like equal opportunity or equal accuracy, as well as statistical parity. The proposed framework supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using constrained optimization or robust optimization algorithms. Experiments illustrate the broad applicability and trade-offs of these methods.
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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." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.
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Shape Constraints for Set Functions
Andrew Cotter
Maya R. Gupta
Heinrich Jiang
Erez Louidor
Jim Muller
Taman Narayan
Tao Zhu
International Conference on Machine Learning (2019) (to appear)
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Set functions predict a label from a permutation-invariant variable-size collection of feature vectors. We propose making set functions more understandable and regularized by capturing domain knowledge through shape constraints. We show how prior work in monotonic constraints can be adapted to set functions. Then we propose two new shape constraints designed to generalize the conditioning role of weights in a weighted mean. We show how one can train standard functions and set functions that satisfy these shape constraints with a deep lattice network. We propose a non-linear estimation strategy we call the semantic feature engine that uses set functions with the proposed shape constraints to estimate labels for compound sparse categorical features. Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, but provides guarantees of the model behavior and is thus easier to explain and debug.
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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter
Heinrich Jiang
Taman Narayan
Seungil You
Karthik Sridharan
Maya R. Gupta
Journal of Machine Learning Research (2019) (to appear)
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Machine learning models often need to satisfy many real-world policy goals and capture some
kinds of side information. We show that many such goals can be mathematically expressed as
constraints on the model’s predictions on the data, which we call rate constraints. In this paper, we
study the specific problem of training non-convex models subject to these rate constraints (which
are non-convex and non-differentiable), and the general problem of constrained optimization of
possibly non-convex objectives with possibly non-convex and non-differentiable constraints. In the
non-convex setting, the Lagrangian may not have an equilibrium to converge to, and thus using the
standard approach of Lagrange multipliers may fail as a deterministic solution may not even exist.
Furthermore, if the constraints are non-differentiable, then one cannot optimize the Lagrangian
with gradient-based methods by definition. To solve these issues, we present the proxy-Lagrangian,
which leads to an algorithm that produces a stochastic classifier with theoretical guarantees by
playing a two-player non-zero-sum game. The first player minimizes external regret in terms of a
differentiable relaxation of the constraints and the second player enforces the original constraints
by minimizing swap-regret: this leads to finding a solution concept which we call a semi-coarse
correlated equilibrium which we interestingly show corresponds to an approximately optimal and
feasible solution to the constrained optimization problem. We then give a procedure which shrinks
the randomized solution down to one that is a mixture of at most m + 1 deterministic solutions.
This culminates into end-to-end procedures which can provably solve non-convex constrained
optimization problems with possibly non-differentiable and non-convex constraints. We provide
extensive experimental results on benchmark and real-world problems, enforcing a broad range of
policy goals including different fairness metrics, and other goals on accuracy, coverage, recall, and
churn.
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Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter
Maya Gupta
Heinrich Jiang
Nathan Srebro
Karthik Sridharan
Blake Woodworth
Seungil You
International Conference on Machine Learning (2019) (to appear)
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Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the generalization performance for such constrained optimization problems, in terms of how well the constraints are satisfied at evaluation time, given that they are satisfied at training time. To improve generalization, we frame the problem as a two-player game where one player optimizes the model parameters on a training dataset, and the other player enforces the constraints on an independent validation dataset. We build on recent work in two-player constrained optimization to show that if one uses this two-dataset approach, then constraint generalization can be significantly improved. As we illustrate experimentally, this approach works not only in theory, but also in practice.
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