Preference Learning with Gaussian Processes and Bayesian Optimization
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Updated
Aug 10, 2017 - Python
Preference Learning with Gaussian Processes and Bayesian Optimization
Java framework for Preference Learning
Code for the project: "Analysis of Recommendation-systems based on User Preferences".
[P]reference and [R]ule [L]earning algorithm implementation for Python 3 (https://arxiv.org/abs/1812.07895)
Preferences Learning JS app for visual images
A paper under AAAI-20 review
learning-to-rank
Python library for preference based learning
Constructive Preference Elicitation for Social Choice With Setwise max-margin Learning.
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.
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.
An analysis of preference comparisons based on the Bayes factor
Project on preference learning - ENSAE ParisTech
This repository contains the source code for our paper: "NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning". For more details, please refer to our project website at https://sites.google.com/view/san-navistar.
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.
Bayesian Spatial Bradley--Terry
The MAGICAL benchmark suite for robust imitation learning (NeurIPS 2020)
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.
Code for "Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model" as published at CVPR 2021.
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