Paternain, Santiago

8 publications

TMLR 2026 Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples Weiqin Chen, Xinjie Zhang, Sandipan Mishra, Santiago Paternain

NeurIPS 2025 DISC: Dynamic Decomposition Improves LLM Inference Scaling Jonathan Light, Wei Cheng, Benjamin Riviere, Yue Wu, Masafumi Oyamada, Mengdi Wang, Yisong Yue, Santiago Paternain, Haifeng Chen

ICLRW 2025 DISC: Dynamic Decomposition Improves LLM Inference Scaling Jonathan Light, Wei Cheng, Yue Wu, Masafumi Oyamada, Mengdi Wang, Santiago Paternain, Haifeng Chen

TMLR 2025 Random Policy Enables In-Context Reinforcement Learning Within Trust Horizons Weiqin Chen, Santiago Paternain

L4DC 2024 Generalized Constraint for Probabilistic Safe Reinforcement Learning Weiqin Chen, Santiago Paternain

L4DC 2024 Multi-Agent Assignment via State Augmented Reinforcement Learning Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andrés Bazerque

L4DC 2020 Counterfactual Programming for Optimal Control Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro

NeurIPS 2019 Constrained Reinforcement Learning Has Zero Duality Gap Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro