Search Results for author:
Found 14 papers, 6 papers with code
End-to-end Supervised Prediction of Arbitrary-size Graphs with Partially-Masked Fused Gromov-Wasserstein Matching
We present a novel end-to-end deep learning-based approach for Supervised Graph Prediction (SGP).
An investigation of structures responsible for gender bias in BERT and DistilBERT
In this paper, we propose an empirical exploration of this problem by formalizing two questions: (1) Can we identify the neural mechanism(s) responsible for gender bias in BERT (and by extension DistilBERT)?
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Mixture of segmentation for heterogeneous functional data
In this paper we consider functional data with heterogeneity in time and in population.
A Survey on Fairness for Machine Learning on Graphs
In that context, algorithmic contributions for graph mining are not spared by the problem of fairness and present some specific challenges related to the intrinsic nature of graphs: (1) graph data is non-IID, and this assumption may invalidate many existing studies in fair machine learning, (2) suited metric definitions to assess the different types of fairness with relational data and (3) algorithmic challenge on the difficulty of finding a good trade-off between model accuracy and fairness.
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
All of the Fairness for Edge Prediction with Optimal Transport
Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security.
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
The theoretical analysis of deep neural networks (DNN) is arguably among the most challenging research directions in machine learning (ML) right now, as it requires from scientists to lay novel statistical learning foundations to explain their behaviour in practice.
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector that is further used to derive the final clustering partition.
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature.
Co-clustering through Optimal Transport
The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix.
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
