no code implementations • 18 Nov 2020 • Luis Haug, Ivan Ovinnikov, Eugene Bykovets
Given an optimality profile and a small amount of additional supervision, our algorithm fits a reward function, modeled as a neural network, by essentially minimizing the Wasserstein distance between the corresponding induced distribution and the optimality profile.
no code implementations • 10 Sep 2020 • Jérémy Scheurer, Claudio Ferrari, Luis Berenguer Todo Bom, Michaela Beer, Werner Kempf, Luis Haug
Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema.
no code implementations • 21 Mar 2020 • Rati Devidze, Farnam Mansouri, Luis Haug, Yuxin Chen, Adish Singla
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task.
no code implementations • NeurIPS 2019 • Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.
no code implementations • NeurIPS 2018 • Luis Haug, Sebastian Tschiatschek, Adish Singla
In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i. e., where there is a mismatch between the worldviews of the learner and the expert.