Independently Controllable Features. - Emmanuel Bengio; Valentin Thomas; Joelle Pineau; Doina Precup; Yoshua Bengio (CoRR, 2017)

Independently Controllable Factors. - Valentin Thomas; Jules Pondard; Emmanuel Bengio; Marc Sarfati; Philippe Beaudoin; Marie-Jean Meurs; Joelle Pineau; Doina Precup; Yoshua Bengio (CoRR, 2017)

Disentangling the independently controllable factors of variation by interacting with the world. - Valentin Thomas; Emmanuel Bengio; William Fedus; Jules Pondard; Philippe Beaudoin; Hugo Larochelle; Joelle Pineau; Doina Precup; Yoshua Bengio (CoRR, 2018)

Probabilistic Planning with Sequential Monte Carlo methods. - Alexandre Piché; Valentin Thomas; Cyril Ibrahim; Yoshua Bengio; Chris Pal (ICLR (Poster), 2019)

Information matrices and generalization. - Valentin Thomas; Fabian Pedregosa; Bart van Merriënboer; Pierre-Antoine Manzagol; Yoshua Bengio; Nicolas Le Roux (CoRR, 2019)

On the interplay between noise and curvature and its effect on optimization and generalization. - Valentin Thomas; Fabian Pedregosa; Bart van Merriënboer; Pierre-Antoine Manzagol; Yoshua Bengio; Nicolas Le Roux (AISTATS, 2020)

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. - Wesley Chung; Valentin Thomas; Marlos C. Machado; Nicolas Le Roux (ICML, 2021)

The Role of Baselines in Policy Gradient Optimization. - Jincheng Mei; Wesley Chung; Valentin Thomas; Bo Dai 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2022)

On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation. - Valentin Thomas (NeurIPS, 2022)

Bridging the Gap Between Target Networks and Functional Regularization. - Alexandre Piché; Valentin Thomas; Joseph Marino; Rafael Pardinas; Gian Maria Marconi; Christopher Pal; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

Retrieval & Fine-Tuning for In-Context Tabular Models. - Valentin Thomas; Junwei Ma; Rasa Hosseinzadeh; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (NeurIPS, 2024)

In-Context Data Distillation with TabPFN. - Junwei Ma; Valentin Thomas; Guangwei Yu; Anthony L. Caterini (CoRR, 2024)

TabDPT: Scaling Tabular Foundation Models. - Junwei Ma; Valentin Thomas; Rasa Hosseinzadeh; Hamidreza Kamkari; Alex Labach; Jesse C. Cresswell; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (CoRR, 2024)

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning. - Vahid Balazadeh Meresht; Hamidreza Kamkari; Valentin Thomas; Benson Li; Junwei Ma; Jesse C. Cresswell; Rahul G. Krishnan (CoRR, 2025)