Timezone: »
Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e.g. for fairness, churn, or custom losses. Despite their utility, the inherent randomness of stochastic classifiers may cause them to be problematic to use in practice for a variety of practical reasons. In this paper, we attempt to answer the theoretical question of how well a stochastic classifier can be approximated by a deterministic one, and compare several different approaches, proving lower and upper bounds. We also experimentally investigate the pros and cons of these methods, not only in regard to how successfully each deterministic classifier approximates the original stochastic classifier, but also in terms of how well each addresses the other issues that can make stochastic classifiers undesirable.
Author Information
Andrew Cotter (Google)
Maya Gupta (Google)
Harikrishna Narasimhan (Google Research)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Oral: On Making Stochastic Classifiers Deterministic »
Thu. Dec 12th 12:25 -- 12:40 AM Room West Exhibition Hall C + B3
More from the Same Authors
-
2023 Poster: When Does Confidence-Based Cascade Deferral Suffice? »
Wittawat Jitkrittum · Neha Gupta · Aditya Menon · Harikrishna Narasimhan · Ankit Rawat · Sanjiv Kumar -
2022 Poster: Post-hoc estimators for learning to defer to an expert »
Harikrishna Narasimhan · Wittawat Jitkrittum · Aditya Menon · Ankit Rawat · Sanjiv Kumar -
2021 Poster: Training Over-parameterized Models with Non-decomposable Objectives »
Harikrishna Narasimhan · Aditya Menon -
2020 Poster: Approximate Heavily-Constrained Learning with Lagrange Multiplier Models »
Harikrishna Narasimhan · Andrew Cotter · Yichen Zhou · Serena Wang · Wenshuo Guo -
2020 Poster: Fair Performance Metric Elicitation »
Gaurush Hiranandani · Harikrishna Narasimhan · Sanmi Koyejo -
2020 Poster: Consistent Plug-in Classifiers for Complex Objectives and Constraints »
Shiv Kumar Tavker · Harish Guruprasad Ramaswamy · Harikrishna Narasimhan -
2020 Poster: Robust Optimization for Fairness with Noisy Protected Groups »
Serena Wang · Wenshuo Guo · Harikrishna Narasimhan · Andrew Cotter · Maya Gupta · Michael Jordan -
2019 Poster: Optimizing Generalized Rate Metrics with Three Players »
Harikrishna Narasimhan · Andrew Cotter · Maya Gupta -
2019 Oral: Optimizing Generalized Rate Metrics with Three Players »
Harikrishna Narasimhan · Andrew Cotter · Maya Gupta -
2018 Poster: Diminishing Returns Shape Constraints for Interpretability and Regularization »
Maya Gupta · Dara Bahri · Andrew Cotter · Kevin Canini -
2018 Poster: To Trust Or Not To Trust A Classifier »
Heinrich Jiang · Been Kim · Melody Guan · Maya Gupta -
2017 Poster: Deep Lattice Networks and Partial Monotonic Functions »
Seungil You · David Ding · Kevin Canini · Jan Pfeifer · Maya Gupta -
2016 Poster: Launch and Iterate: Reducing Prediction Churn »
Mahdi Milani Fard · Quentin Cormier · Kevin Canini · Maya Gupta -
2016 Poster: Fast and Flexible Monotonic Functions with Ensembles of Lattices »
Mahdi Milani Fard · Kevin Canini · Andrew Cotter · Jan Pfeifer · Maya Gupta -
2016 Poster: Satisfying Real-world Goals with Dataset Constraints »
Gabriel Goh · Andrew Cotter · Maya Gupta · Michael P Friedlander