Free and open source code of the https://tournesol.app platform. Meet the community on Discord https://discord.gg/WvcSG55Bf3
-
Updated
May 16, 2024 - Python
Free and open source code of the https://tournesol.app platform. Meet the community on Discord https://discord.gg/WvcSG55Bf3
RewardBench: the first evaluation tool for reward models.
The MAGICAL benchmark suite for robust imitation learning (NeurIPS 2020)
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.
Python-based GUI to collect Feedback of Chemist in Molecules
Code for "Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model" as published at CVPR 2021.
Aligning LLM Agents by Learning Latent Preference from User Edits
Data and models for the paper "Configurable Safety Tuning of Language Models with Synthetic Preference Data"
Preference Learning with Gaussian Processes and Bayesian Optimization
Java framework for Preference Learning
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.
A paper under AAAI-20 review
[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".
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.
Constructive Preference Elicitation for Social Choice With Setwise max-margin Learning.
Python library for preference based 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.
(AISTATS 2024) "Looping in the Human: Collaborative and Explainable Bayesian Optimization"
Add a description, image, and links to the preference-learning topic page so that developers can more easily learn about it.
To associate your repository with the preference-learning topic, visit your repo's landing page and select "manage topics."