Optimal Transport for Correctional Learning

• URL: http://arxiv.org/abs/2304.01701v1
• Date: Tue, 4 Apr 2023 10:55:32 GMT
• Title: Optimal Transport for Correctional Learning
• Authors: Rebecka Winqvist, In\^es Lourenco, Francesco Quinzan, Cristian R. Rojas, Bo Wahlberg
• Abstract summary: correctional learning is a framework developed to enhance the accuracy of parameter estimation processes. In this framework, an expert agent, referred to as the teacher, modifies the data used by a learning agent, known as the student, to improve its estimation process. The objective of the teacher is to alter the data such that the student's estimation error is minimized, subject to a fixed intervention budget.
• Score: 9.25190738506728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to another. Correctional learning is a framework developed to enhance the accuracy of parameter estimation processes by means of a teacher-student approach. In this framework, an expert agent, referred to as the teacher, modifies the data used by a learning agent, known as the student, to improve its estimation process. The objective of the teacher is to alter the data such that the student's estimation error is minimized, subject to a fixed intervention budget. Compared to existing formulations of correctional learning, our novel optimal transport approach provides several benefits. It allows for the estimation of more complex characteristics as well as the consideration of multiple intervention policies for the teacher. We evaluate our approach on two theoretical examples, and on a human-robot interaction application in which the teacher's role is to improve the robots performance in an inverse reinforcement learning setting.

Related papers

• Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation [23.048841953423846]
  We focus on the problem of learning to reward, which is fundamental to reinforcement learning. Previous approaches either introduce additional procedures for learning to reward, thereby increasing the complexity of optimization. We propose a novel batch inverse reinforcement learning paradigm that achieves the desired properties.
  arXiv  Detail & Related papers  (2023-10-30T13:43:20Z)
• "You might think about slightly revising the title": identifying hedges in peer-tutoring interactions [1.0466434989449724]
  Hedges play an important role in the management of conversational interaction. We use a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations.
  arXiv  Detail & Related papers  (2023-06-18T12:47:54Z)
• Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble [56.705249154629264]
  Self-training teacher-student frameworks are proposed to improve the robustness of NER models. In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks. Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
  arXiv  Detail & Related papers  (2022-12-13T12:14:09Z)
• Explainable Action Advising for Multi-Agent Reinforcement Learning [32.49380192781649]
  Action advising is a knowledge transfer technique for reinforcement learning based on the teacher-student paradigm. We introduce Explainable Action Advising, in which the teacher provides action advice as well as associated explanations indicating why the action was chosen. This allows the student to self-reflect on what it has learned, enabling generalization advice and leading to improved sample efficiency and learning performance.
  arXiv  Detail & Related papers  (2022-11-15T04:15:03Z)
• Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation [67.52229938775294]
  In past years, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, we propose to conduct further exploration to imitate the human learning process from different aspects.
  arXiv  Detail & Related papers  (2021-11-28T01:14:29Z)
• A teacher-student framework for online correctional learning [12.980296933051509]
  We show that the variance of the estimate of the student is reduced with the help of the teacher. We formulate the online problem - where the teacher has to decide at each time instant whether or not to change the observations. We validate the framework in numerical experiments, and compare the optimal online policy with the one from the batch setting.
  arXiv  Detail & Related papers  (2021-11-15T15:01:00Z)
• Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction [73.77593805292194]
  We train a separate exploration policy to maximize an approximate upper confidence bound of the critics in an off-policy actor-critic framework. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy actor-critic training.
  arXiv  Detail & Related papers  (2021-10-22T22:07:51Z)
• Iterative Teacher-Aware Learning [136.05341445369265]
  In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
  arXiv  Detail & Related papers  (2021-10-01T00:27:47Z)
• Distribution Matching for Machine Teaching [64.39292542263286]
  Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis. Previous studies on machine teaching focused on balancing the teaching risk and cost to find those best teaching examples. This paper presents a distribution matching-based machine teaching strategy.
  arXiv  Detail & Related papers  (2021-05-06T09:32:57Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)

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.