1 citations · 1 across the 9 of their papers we have counts for
12 papers
Rethinking GFlowNet Training: Negative Reinforce Dynamics
Pedro Dall'Antonia, Daniel Csillag, Diego Mesquita
Generative Flow Networks (GFlowNets) are models that learn to sample discrete objects proportional to a target reward. They are trained by minimizing balance errors over sampled tr…
Particle GFlowNets: Rethinking Generative Marginalization Models
Tiago da Silva, Diego Mesquita, Salem Lahlou
Generative Marginalization Models (MaMs) have been recently introduced as efficient neural sampling models for any-order autoregressive modelling of discrete distributions. By lear…
Gaussian Sheaf Neural Networks
André Ribeiro, Ana Luiza Tenório, Tiago da Silva +1
Graph Neural Networks (GNNs) have become the de facto standard for learning on relational data. While traditional GNNs' message passing is well suited for vector-valued node featur…
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
Pedro Dall'Antonia, Tiago da Silva, Daniel Csillag +2
Generative Flow Networks (GFlowNets) are a flexible family of amortized samplers trained to generate discrete and compositional objects with probability proportional to a reward fu…
Boosted GFlowNets: Improving Exploration via Sequential Learning
Pedro Dall'Antonia, Tiago da Silva, Daniel Augusto de Souza +2
Generative Flow Networks (GFlowNets) are powerful samplers for compositional objects that, by design, sample proportionally to a given non-negative reward. Nonetheless, in practice…
Differentially Private E-Values
Daniel Csillag, Diego Mesquita
E-values have gained prominence as flexible tools for statistical inference and risk control, enabling anytime- and post-hoc-valid procedures under minimal assumptions. However, ma…
Infinite Neural Operators: Gaussian processes on functions
Daniel Augusto de Souza, Yuchen Zhu, Harry Jake Cunningham +3
A variety of infinitely wide neural architectures (e.g., dense NNs, CNNs, and transformers) induce Gaussian process (GP) priors over their outputs. These relationships provide both…
On the Identifiability of Tensor Ranks via Prior Predictive Matching
Eliezer da Silva, Arto Klami, Diego Mesquita +1
Selecting the latent dimensions (ranks) in tensor factorization is a central challenge that often relies on heuristic methods. This paper introduces a rigorous approach to determin…
Differentially Private Selection using Smooth Sensitivity
Iago Chaves, Victor Farias, Amanda Perez +2
Differentially private selection mechanisms offer strong privacy guarantees for queries aiming to identify the top-scoring element r from a finite set R, based on a dataset-depende…
Streaming Bayes GFlowNets
Tiago da Silva, Daniel Augusto de Souza, Diego Mesquita
Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch whenever new data arrives. In principle, Bayesia…
On Divergence Measures for Training GFlowNets
Tiago da Silva, Eliezer de Souza da Silva, Diego Mesquita
Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative mode…
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…