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Taman Narayan

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4 papers

2 author rows

TMLR Journal 2024 Journal Article

Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation

Taman Narayan
Serena Lutong Wang
Kevin Robert Canini
Maya Gupta

We analyze and improve a recent strategy to train a quantile regression model by minimizing an expected pinball loss over all quantiles. Through an asymptotic convergence analysis, we show that minimizing the expected pinball loss can be more efficient at estimating single quantiles than training with the standard pinball loss for that quantile, an insight that generalizes the known deficiencies of the sample quantile in the unconditioned setting. Then, to guarantee a legitimate inverse CDF, we propose using flexible deep lattice networks with a monotonicity constraint on the quantile input to guarantee non-crossing quantiles, and show lattice models can be regularized to the same location-scale family. Our analysis and experiments on simulated and real datasets show that the proposed method produces state-of-the-art legitimate inverse CDF estimates that are likely to be as good or better for specific target quantiles.

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ICML Conference 2020 Conference Paper

Multidimensional Shape Constraints

Maya R. Gupta
Erez Louidor
Oleksandr Mangylov
Nobu Morioka
Taman Narayan
Sen Zhao

We propose new multi-input shape constraints across four intuitive categories: complements, diminishers, dominance, and unimodality constraints. We show these shape constraints can be checked and even enforced when training machine-learned models for linear models, generalized additive models, and the nonlinear function class of multi-layer lattice models. Real-world experiments illustrate how the different shape constraints can be used to increase explainability and improve regularization, especially for non-IID train-test distribution shift.

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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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ICML Conference 2019 Conference Paper

Shape Constraints for Set Functions

Andrew Cotter
Maya R. Gupta
Heinrich Jiang
Erez Louidor
James Muller
Taman Narayan
Serena Wang 0001
Tao Zhu 0005

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, and then 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 nonlinear 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 on the model behavior, which makes it easier to explain and debug.

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