Publications associées à Filtrage collaboratif

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Recommender systems: Trends and frontiers

Pearl Pu Faltings

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RS ...

AMER ASSOC ARTIFICIAL INTELL2022

Positive and Negative Critiquing for VAE-based Recommenders

Boi Faltings, Diego Matteo Antognini

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

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TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Nevena Dresevic

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

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Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Victor Constantin, Sina Honari

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

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p Recommender systems: Past, present, future

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The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly ...

AMER ASSOC ARTIFICIAL INTELL2021

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Boi Faltings, Diego Matteo Antognini

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

ASSOC COMPUTING MACHINERY2021

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Filtrage collaboratif

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Robust Collaborative Learning with Linear Gradient Overhead

KNNs of Semantic Encodings for Rating Prediction

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Recommender systems: Trends and frontiers

Positive and Negative Critiquing for VAE-based Recommenders

TEE-based decentralized recommender systems: The raw data sharing redemption

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

p Recommender systems: Past, present, future

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Graph Chatbot

Chattez avec Graph Search

p Recommender systems: Past, present, future

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Recommender systems: Trends and frontiers

Robust Collaborative Learning with Linear Gradient Overhead

KNNs of Semantic Encodings for Rating Prediction

Positive and Negative Critiquing for VAE-based Recommenders

TEE-based decentralized recommender systems: The raw data sharing redemption

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Fast Multi-Step Critiquing for VAE-based Recommender Systems