Sample Efficient Learning of Predictors that Complement Humans

• URL: http://arxiv.org/abs/2207.09584v1
• Date: Tue, 19 Jul 2022 23:19:25 GMT
• Title: Sample Efficient Learning of Predictors that Complement Humans
• Authors: Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi
• Abstract summary: We provide the first theoretical analysis of the benefit of learning complementary predictors in expert deferral. We design active learning schemes that require minimal amount of data of human expert predictions.
• Score: 5.830619388189559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the goals of learning algorithms is to complement and reduce the burden on human decision makers. The expert deferral setting wherein an algorithm can either predict on its own or defer the decision to a downstream expert helps accomplish this goal. A fundamental aspect of this setting is the need to learn complementary predictors that improve on the human's weaknesses rather than learning predictors optimized for average error. In this work, we provide the first theoretical analysis of the benefit of learning complementary predictors in expert deferral. To enable efficiently learning such predictors, we consider a family of consistent surrogate loss functions for expert deferral and analyze their theoretical properties. Finally, we design active learning schemes that require minimal amount of data of human expert predictions in order to learn accurate deferral systems.

Related papers

• Learning-Augmented Algorithms with Explicit Predictors [67.02156211760415]
  Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. Prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box. In this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge.
  arXiv  Detail & Related papers  (2024-03-12T08:40:21Z)
• Modeling of learning curves with applications to pos tagging [0.27624021966289597]
  We introduce an algorithm to estimate the evolution of learning curves on the whole of a training data base. We approximate iteratively the sought value at the desired time, independently of the learning technique used. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition.
  arXiv  Detail & Related papers  (2024-02-04T15:00:52Z)
• Learning to Defer with Limited Expert Predictions [0.0]
  We propose a three-step approach to reduce the number of expert predictions required to train learning to defer algorithms. Our experiments show that the approach allows the training of various learning to defer algorithms with a minimal number of human expert predictions.
  arXiv  Detail & Related papers  (2023-04-14T09:22:34Z)
• Understanding Self-Predictive Learning for Reinforcement Learning [61.62067048348786]
  We study the learning dynamics of self-predictive learning for reinforcement learning. We propose a novel self-predictive algorithm that learns two representations simultaneously.
  arXiv  Detail & Related papers  (2022-12-06T20:43:37Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Learning Predictions for Algorithms with Predictions [49.341241064279714]
  We introduce a general design approach for algorithms that learn predictors. We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
  arXiv  Detail & Related papers  (2022-02-18T17:25:43Z)
• Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness [92.26039686430204]
  We show that even in carefully-designed systems, complementary performance can be elusive. First, we provide a theoretical framework for modeling simple human-algorithm systems. Next, we use this model to prove conditions where complementarity is impossible.
  arXiv  Detail & Related papers  (2022-02-17T18:44:41Z)
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [91.3755431537592]
  We analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression. We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice.
  arXiv  Detail & Related papers  (2021-01-26T17:11:40Z)
• Consistent Estimators for Learning to Defer to an Expert [5.076419064097734]
  We show how to learn predictors that can either predict or choose to defer the decision to a downstream expert. We show the effectiveness of our approach on a variety of experimental tasks.
  arXiv  Detail & Related papers  (2020-06-02T18:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.