The Representation Power of Neural Networks: Breaking the Curse of Dimensionality. - Moïse Blanchard; M. Amine Bennouna (CoRR, 2020)

On the Length of Monotone Paths in Polyhedra. - Moïse Blanchard; Jesús A. De Loera; Quentin Louveaux (SIAM J. Discret. Math., 2021)

Universal Online Learning with Unbounded Losses: Memory Is All You Need. - Moïse Blanchard; Romain Cosson; Steve Hanneke (ALT, 2022)

Universal Online Learning: an Optimistically Universal Learning Rule. - Moïse Blanchard (COLT, 2022)

Universal Online Learning with Bounded Loss: Reduction to Binary Classification. - Moïse Blanchard; Romain Cosson (COLT, 2022)

Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions. - Moïse Blanchard; Mohammed Amine Bennouna (ICLR, 2022)

Universal Regression with Adversarial Responses. - Moïse Blanchard; Patrick Jaillet (CoRR, 2022)

Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. - Moïse Blanchard; Alexandre Jacquillat; Patrick Jaillet (CoRR, 2022)

Additional Results and Extensions for the paper "Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem". - Moïse Blanchard; Alexandre Jacquillat; Patrick Jaillet (CoRR, 2022)

Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. - Moïse Blanchard; Junhui Zhang; Patrick Jaillet (COLT, 2023)

Memory-Constrained Algorithms for Convex Optimization. - Moïse Blanchard; Junhui Zhang; Patrick Jaillet (NeurIPS, 2023)

Contextual Bandits and Optimistically Universal Learning. - Moïse Blanchard; Steve Hanneke; Patrick Jaillet (CoRR, 2023)

Non-stationary Contextual Bandits and Universal Learning. - Moïse Blanchard; Steve Hanneke; Patrick Jaillet (CoRR, 2023)

Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes. - Moïse Blanchard; Junhui Zhang; Patrick Jaillet (CoRR, 2023)

Tight Bounds for Local Glivenko-Cantelli. - Moïse Blanchard; Václav Vorácek (ALT, 2024)

Correlated Binomial Process. - Moïse Blanchard; Doron Cohen 002; Aryeh Kontorovich (COLT, 2024)

Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems. - Moïse Blanchard (FOCS, 2024)

Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers. - Moïse Blanchard; Patrick Jaillet (CoRR, 2024)

Probabilistic Bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. - Moïse Blanchard; Alexandre Jacquillat; Patrick Jaillet (Math. Oper. Res., 2024)

Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract). - Yan Dai 002; Moïse Blanchard; Patrick Jaillet (COLT, 2025)

Agnostic Smoothed Online Learning. - Moïse Blanchard (STOC, 2025)

Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely. - Yan Dai 002; Moïse Blanchard; Patrick Jaillet (CoRR, 2025)

GUARD: Constructing Realistic Two-Player Matrix and Security Games for Benchmarking Game-Theoretic Algorithms. - Noah Krever; Jakub Cerný; Moïse Blanchard; Christian Kroer (CoRR, 2025)

Distributionally-Constrained Adversaries in Online Learning. - Moïse Blanchard; Samory Kpotufe (CoRR, 2025)

Collaborative Min-Max Regret in Grouped Multi-Armed Bandits. - Moïse Blanchard; Vineet Goyal (CoRR, 2025)