Project on preference learning - ENSAE ParisTech

Survey of preference alignment algorithms

Simplified, modern implementation of Rating and Preference-based Reinforcement Learning.

An analysis of preference comparisons based on the Bayes factor

learning-to-rank

Bayesian Spatial Bradley--Terry

Preference Optimization made easy

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.

Preferences Learning JS app for visual images

(AISTATS 2024) "Looping in the Human: Collaborative and Explainable Bayesian Optimization"

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 our paper "Bandits with Preference Feedback: A Stackelberg Game Perspective"

UI for straightforward Bradley-Terry feedback loop

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.

Code for the paper "Reward Design for Justifiable Sequential Decision-Making"; ICLR 2024

[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".