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Publications

A curated list of publications across probabilistic machine learning, Bayesian modeling, GFlowNets, causal discovery, and trustworthy AI.

Use categories and topics to filter by venue and theme.

2026

2025

When do GFlowNets Learn the Right Distribution?

·274 words
Tiago Da Silva Rodrigo Barreto Alves Eliezer De Souza Da Silva Amauri H Souza Vikas Garg Samuel Kaski Diego Mesquita Publication Spotlight Paper Conference ICLR Generative Flow Networks Generative Models Theoretical Machine Learning

Analysis of the limitations and stability of GFlowNets under balance violations, showing how these affect accuracy. We introduce a novel metric for assessing correctness, improving evaluation beyond existing protocols.

ICLR 2025 (Spotlight, ~top 5% 🎉)

2024

On Divergence Measures for Training GFlowNets

·277 words
Tiago Da Silva Eliezer De Souza Da Silva Diego Mesquita Publication Conference NeurIPS Deep Generative Models Statistical Divergences Variational Inference Generative Flow Networks

Novel approach to training Generative Flow Networks (GFlowNets) by minimizing divergence measures such as Renyi-$\alpha$, Tsallis-$\alpha$, and Kullback-Leibler (KL) divergences. Stochastic gradient estimators using variance reduction techniques leads to faster and stabler training.

NeurIPS 2024 (Poster)

Analyzing GFlowNets: Stability, Expressiveness, and Assessment

·288 words
Tiago Da Silva Eliezer De Souza Da Silva Rodrigo Barreto Alves Luiz Max Carvalho Amauri H Souza Samuel Kaski Vikas Garg Diego Mesquita Publication Preprint Workshop SPIGM@ICML Deep Generative Models Theoretical ML Generative Flow Networks

How balance violations impact the learned distribution, motivating an weighted balance loss to improve training. For graph distributions, there are scenarios where balance is unattainable, and richer embeddings of children’s states is needed enhance expressiveness. To measure of distributional correctness in GFN we introduce a provable correct novel assessment metric.

2023

Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

·360 words
Eliezer De Souza Da Silva Tomasz Kuśmierczyk Marcelo Hartmann Arto Klami Publication Journal JMLR Bayesian Matrix Factorization Theoretical Prior Predictive Analysis

A method for prior specification by optimizing hyperparameters via the prior predictive distribution. This approach matches virtual statistics generated by the prior to certain target values. We apply it to Bayesian matrix factorization models, obtaining a close-formula for the rank of the latent variables, and analytically determine the matching hyperparameters, and extend it to general models through stochastic optimization.

JMLR 2023

ICML 2024 (Poster, Journal Track)

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets

·288 words
Tiago Da Silva Eliezer De Souza Da Silva Adèle Ribeiro António Góis Dominik Heider Samuel Kaski Diego Mesquita Publication Preprint Generative Flow Networks Causal Structure Learning Causal ML

We introduce a causal discovery method that estimates uncertainty and refines results with expert feedback. Using generative flow networks, we sample belief-based ancestral graphs that captures latent-confounding, and iteratively reduce uncertainty through human input, with a human-in-the-loop approach.

2019

Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions

·275 words
Bjørnar Vassøy Massimiliano Ruocco Eliezer De Souza Da Silva Erlend Aune Publication Conference WSDM Point Processes Recurrent Neural Networks Recommender Systems

A joint model combining a Hierarchical RNN for session-based recommendations and a Point Process model for predicting return times. This approach improves both recommendation accuracy and return-time predictions over strong baselines.

WSDM 2019 (Poster)

2017

Content-Based Social Recommendation with Poisson Matrix Factorization

·269 words
Eliezer De Souza Da Silva Helge Langseth Heri Ramampiaro Publication Conference ECML Bayesian Matrix Factorization Recommender Systems Variational Inference

A latent variable probabilistic model for recommender systems that combines social trust, item content, and user preferences into a unified Poisson matrix factorization framework. This model jointly factorizes the user–item interaction matrix and item–content matrix, accounting for social relationships and content information to enhance recommendation accuracy. ECML 2017