(AISTATS 2024) "Looping in the Human: Collaborative and Explainable Bayesian Optimization"
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Updated
Mar 4, 2024 - Jupyter Notebook
(AISTATS 2024) "Looping in the Human: Collaborative and Explainable Bayesian Optimization"
Project on preference learning - ENSAE ParisTech
Survey of preference alignment algorithms
An analysis of preference comparisons based on the Bayes factor
Bayesian Spatial Bradley--Terry
Project about experiments of the use of ILASP as a post-hoc method over black-box models, in which we also study and approach technical issues like exponential time execution.
learning-to-rank
Preferences Learning JS app for visual images
Constructive Preference Elicitation for Social Choice With Setwise max-margin Learning.
APReL: Active preference-based reward learning for human-robot interaction. Utilizing "Mountain Car" environment, learn from human preferences to reach the goal state. Applications in robotics and adaptability to other learning methods.
Python library for preference based learning
Code for the paper "Reward Design for Justifiable Sequential Decision-Making"; ICLR 2024
In this project, we design a recurrent neural network to simulate a cognitive model of decision-making called Multi Alternative Decision Field Theory (MDFT). We train this RNN to learn the parameters of MDFT.
[P]reference and [R]ule [L]earning algorithm implementation for Python 3 (https://arxiv.org/abs/1812.07895)
Code for the project: "Analysis of Recommendation-systems based on User Preferences".
Java framework for Preference Learning
A paper under AAAI-20 review
This repository contains the source code for our paper: "Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation", accepted to IROS-2022. For more details, please refer to our project website at https://sites.google.com/view/san-fapl.
Preference Learning with Gaussian Processes and Bayesian Optimization
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