Portrait de Antonio Gois

Antonio Gois

Doctorat - UdeM
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Causalité
Systèmes multi-agents
Théorie des jeux
Site web
Google Scholar
GitHub

Publications

The Role of Causal Features in Strategic Classification for Robustness and Alignment
Sophia Gunluk
Antonio Gois
Nir Rosenfeld
Nidhi Hegde
Simon Lacoste-Julien
Dhanya Sridhar
In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility i… (voir plus)n a classification task (e.g., loan repayment). Since a key challenge is the distribution shift induced by users, we turn to causal models, which have been shown to bound the worst-case out-of-distribution (OOD) risk, and establish several new results that link causality and strategic classification. First, we show that causal classification leads to optimal classification error after any sufficiently large adaptation, when the noise is bounded in a certain way. Second, when these assumptions do not hold, we show OOD cross-entropy risk of optimal classifiers decomposes into an OOD bias term and a term arising from not using all observable features, allowing us to determine when causal classifiers have an advantage. Finally, we show that causal classifiers can align long-term incentives between institutions and users, contrasting with previous work that highlights social costs of such approaches. We validate our theory empirically on synthetic data, finding that our results predict behavior in practice.
2026-02-02
Artificial Intelligence and Statistics (poster)
openreview.net
openreview.net
Performative Prediction on Games and Mechanism Design
Antonio Gois
Mehrnaz Mofakhami
Fernando P. Santos
Simon Lacoste-Julien
Gauthier Gidel
2025-04-22
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu
Antonio Gois
Chris Emezue
Mansi Rankawat
Simon Lacoste-Julien
Stefan Bauer
Yoshua Bengio
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian ne… (voir plus)tworks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling of discrete and composite objects, such as graphs. In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. Generating a sample DAG from this approximate distribution is viewed as a sequential decision problem, where the graph is constructed one edge at a time, based on learned transition probabilities. Through evaluation on both simulated and real data, we show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs, and it compares favorably against other methods based on MCMC or variational inference.
2022-05-19
auai.org/UAI/2022/Conference (poster)
doi.org
proceedings.mlr.press