Free and open source code of the https://tournesol.app platform. Meet the community on Discord https://discord.gg/WvcSG55Bf3
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Updated
May 30, 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.
Python-based GUI to collect Feedback of Chemist in Molecules
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"
(AISTATS 2024) "Looping in the Human: Collaborative and Explainable Bayesian Optimization"
Code for the paper "Reward Design for Justifiable Sequential Decision-Making"; ICLR 2024
Survey of preference alignment algorithms
Code for "Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model" as published at CVPR 2021.
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.
The MAGICAL benchmark suite for robust imitation learning (NeurIPS 2020)
Bayesian Spatial Bradley--Terry
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.
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.
Project on preference learning - ENSAE ParisTech
An analysis of preference comparisons based on the Bayes factor
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.
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
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