107 citations · 122 across the 7 of their papers we have counts for
8 papers
Encoded but Exposed: Language Embeddings can Reveal More Than They Should in Recommender Systems
Linda Beková, Rodrigo Alves
Language embeddings are increasingly used in recommenders to represent item content, but their privacy risks remain poorly understood. We study whether item embeddings can reveal i…
Learning Minimally Rigid Graphs with High Realization Counts
Oleksandr Slyvka, Jan Rubeš, Rodrigo Alves +1
For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptionally many realizations is an…
Generalization Bounds for Rank-sparse Neural Networks
Antoine Ledent, Rodrigo Alves, Yunwen Lei
It has been recently observed in much of the literature that neural networks exhibit a bottleneck rank property: for larger depths, the activation and weights of neural networks tr…
Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
Antoine Ledent, Petr Kasalický, Rodrigo Alves +1
We introduce a new convolutional AutoEncoder architecture for user modelling and recommendation tasks with several improvements over the state of the art. Firstly, our model has th…
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
Petr Kasalický, Rodrigo Alves, Pavel Kordík
The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority o…
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings
Antoine Ledent, Rodrigo Alves, Yunwen Lei +2
We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entr…
Orthogonal Inductive Matrix Completion
Antoine Ledent, Rodrigo Alves, Marius Kloft
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together wit…
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay +6
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibr…