6 citations · 9 across the 6 of their papers we have counts for
6 papers
The Benefits of Diversity: Combining Comparisons and Ratings for Efficient Scoring
Julien Fageot, Matthias Grossglauser, Lê-Nguyên Hoang +2
Should humans be asked to evaluate entities individually or comparatively? This question has been the subject of long debates. In this work, we show that, interestingly, combining…
Ranking Items from Discrete Ratings: The Cost of Unknown User Thresholds
Oscar Villemaud, Suryanarayana Sankagiri, Matthias Grossglauser
Ranking items is a central task in many information retrieval and recommender systems. User input for the ranking task often comes in the form of ratings on a coarse discrete scale…
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons
Julien Fageot, Sadegh Farhadkhani, Lê Nguyên Hoang +1
Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model a…
Solidago: A Modular Collaborative Scoring Pipeline
Lê Nguyên Hoang, Romain Beylerian, Bérangère Colbois +6
This paper presents Solidago, an end-to-end modular pipeline to allow any community of users to collaboratively score any number of entities. Solidago proposes a six-module decompo…
Robust Sparse Voting
Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang +1
Many applications, such as content moderation and recommendation, require reviewing and scoring a large number of alternatives. Doing so robustly is however very challenging. Indee…
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang +1
To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Wh…