Information

  • Location: NIPS 2017 Workshop, Long Beach, CA, USA
  • Date: 9 Dec, 2017
  • Paper Submission Site: See more information below in CFP.
  • Paper Submission Deadline (2nd round): 7 November 2017, 5:00:00 PM PST
  • Author Notification (2nd round): 12 November 2017, 5:00:00 PM PST
  • Registration status: NIPS registration is sold out for the general audience. However, NIPS has held a number of tickets in reserve. If your paper is accepted to the workshop, you would be able to register for the workshop (until this reserve lasts).
  • Contact: For any questions, please email nips17teaching AT gmail.com

Overview

This workshop focuses on "machine teaching", the inverse problem of machine learning, in which the goal is to find an optimal training set given a machine learning algorithm and a target model. The study of machine teaching began in the early 1990s, primarily coming out of computational learning theory. Recently, there has been a surge of interest in machine teaching as several different communities within machine learning have found connections to this problem; these connections have included the following:

  • machine teaching has close connections to newly introduced models of interaction in machine learning community, such as curriculum learning, self-paced learning, and knowledge distillation. [Hinton et al. 2015; Bengio et al. 2009]
  • there are strong theoretical connections between the Teaching-dimension (the sample complexity of teaching) and the VC-dimension (the sample complexity of learning from randomly chosen examples). [Doliwa et al. 2014]
  • machine teaching problem formulation has been recently studied in the context of diverse applications including personalized educational systems, cyber-security problems, robotics, program synthesis, human-in-the-loop systems, and crowdsourcing. [Jha et al. 2016; Zhu 2015; Mei & Zhu 2015; Ba & Caruana 2014; Patil et al. 2014; Singla et al. 2014; Cakmak & Thomaz 2014]

In this workshop, we draw attention to machine teaching by emphasizing how the area of machine teaching interacts with emerging research trends and application domains relevant to the NIPS community. The goal of this workshop is to foster these ideas by bringing together researchers with expertise/interest in the inter-related areas of machine teaching, interactive machine learning, robotics, cyber-security problems, generative adversarial networks, educational technologies, and cognitive science.

Talks

Topics of Interest

Topics of interests in the workshop include (but are not limited to):
Theoretical Foundations of Machine Teaching
  • using tools from information theory to develop better mathematical models of teaching;
  • characterizing the complexity of teaching when a teacher has limited power, or incomplete knowledge of student's model, or a mismatch in feature representations;
  • algorithms for adaptive teaching by interactively inferring the learner's state;
  • new notions of Teaching-dimension for generic teaching settings.
Connections to Machine Learning Models
  • the information complexity of teaching and query complexity;
  • machine teaching vs. curriculum learning and other models of interactive machine learning;
  • teaching reinforcement learning agents
Applications to Educational Technologies
  • using the machine teaching formulation to enable more rigorous and generalizable approaches for developing intelligent tutoring systems;
  • behavioral experiments to identify good cognitive models of human learning processes.
Applications to Adversarial Machine Learning
  • Applications of machine teaching to adversarial attacks, including cyber-security problems, generative adversarial networks, attacks on machine learning algorithms, etc.
Novel Applications
  • Novel applications for machine teaching such as program synthesis, human-robot interactions, social robotics, etc.

Call for Papers

Submissions should follow the NIPS 2017 format and are encouraged to be up to eight pages, excluding references and additional appendices. Papers submitted for review do not need to be anonymized. There will be no official proceedings, but the accepted papers will be made available on the workshop website. Accepted papers will be either presented as a talk or poster.

We welcome submissions on (i) novel research work (previously unpublished), (ii) work recently published or under review in another conference/journal (please clearly state the venue in the Abstract). In the interest of spurring the discussion, we particularly encourage submission of visionary position papers on the emerging trends in the field.

Please submit papers at: https://cmt3.research.microsoft.com/TMRH2017/

Registration status: NIPS registration is sold out for the general audience. However, NIPS has held a number of tickets in reserve. If your paper is accepted to the workshop, you would be able to register for the workshop (until this reserve lasts).

Accepted Papers (to be updated)

Accepted papers from the 1st round of submissions are listed below. This list will be updated after the 2nd round of submissions.
  • "Generative Adversarial Active Learning". Jia-Jie Zhu; Jose Bento.
  • "Machine Education - The Way Forward for Achieving Trust-Enabled Machine Agents". George Leu; Erandi Lakshika; Jiangjun Tang; Kathryn Merrick; Michael Barlow.
  • "Optimizing Human Learning". Behzad Tabibian; Utkarsh Upadhyay; Abir De; Ali Zarezadeh; Bernhard Scholkopf; Manuel Gomez Rodriguez.
  • "Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification". Chong Wang; Xipeng Lan.
  • "Interpretable Teaching of Visual Categories to Humans Learners". Shihan Su; Yuxin Chen; Oisin Mac Aodha; Pietro Perona; Yisong Yue.
  • "Pedagogical Learning". Long Ouyang; Michael Frank.
  • "Interpretable and Pedagogical Examples". Smitha L Milli; Igor Mordatch; Pieter Abbeel.

Organizers