Teaching to Learn: Sequential Teaching of Agents with Inner States

• URL: http://arxiv.org/abs/2009.06227v1
• Date: Mon, 14 Sep 2020 07:03:15 GMT
• Title: Teaching to Learn: Sequential Teaching of Agents with Inner States
• Authors: Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski
• Abstract summary: We introduce a multi-agent formulation in which learners' inner state may change with the teaching interaction. In order to teach such learners, we propose an optimal control approach that takes the future performance of the learner after teaching into account.
• Score: 20.556373950863247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this paper we extend this setting from current static one-data-set analyses to learners which change their learning algorithm or latent state to improve during learning, and to generalize to new datasets. We introduce a multi-agent formulation in which learners' inner state may change with the teaching interaction, which affects the learning performance in future tasks. In order to teach such learners, we propose an optimal control approach that takes the future performance of the learner after teaching into account. This provides tools for modelling learners having inner states, and machine teaching of meta-learning algorithms. Furthermore, we distinguish manipulative teaching, which can be done by effectively hiding data and also used for indoctrination, from more general education which aims to help the learner become better at generalization and learning in new datasets in the absence of a teacher.

Related papers

• A General Model for Detecting Learner Engagement: Implementation and Evaluation [0.0]
  This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels. We analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement. The suggested model achieves an accuracy of 68.57% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels.
  arXiv  Detail & Related papers  (2024-05-07T12:11:15Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis [0.9187505256430948]
  The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. Students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor.
  arXiv  Detail & Related papers  (2023-12-17T21:50:02Z)
• Reinforcement Teaching [43.80089037901853]
  We propose Reinforcement Teaching: a framework for meta-learning in which a teaching policy is learned, through reinforcement, to control a student's learning process. The student's learning process is modelled as a Markov reward process and the teacher, with its action-space, interacts with the induced Markov decision process. We show that, for many learning processes, the student's learnable parameters form a Markov state. To avoid having the teacher learn directly from parameters, we propose the Embedder that learns a representation of a student's state from its input/output behaviour.
  arXiv  Detail & Related papers  (2022-04-25T18:04:17Z)
• 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)
• Teaching with Commentaries [108.62722733649542]
  We propose a flexible teaching framework using commentaries and learned meta-information. We find that commentaries can improve training speed and/or performance. commentaries can be reused when training new models to obtain performance benefits.
  arXiv  Detail & Related papers  (2020-11-05T18:52:46Z)
• Learning to Reweight with Deep Interactions [104.68509759134878]
  We propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
  arXiv  Detail & Related papers  (2020-07-09T09:06:31Z)
• Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
  We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
  arXiv  Detail & Related papers  (2020-02-03T06:13:01Z)

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.