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2020 – today
- 2024
[j2]Moïse Blanchard
, Alexandre Jacquillat, Patrick Jaillet:
Probabilistic Bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. Math. Oper. Res. 49(2): 1169-1191 (2024)- [c8]Moïse Blanchard, Václav Vorácek:
Tight Bounds for Local Glivenko-Cantelli. ALT 2024: 179-220 - [c7]Moïse Blanchard, Doron Cohen, Aryeh Kontorovich:
Correlated Binomial Process. COLT 2024: 551-595 - [i14]Moïse Blanchard:
Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems. CoRR abs/2404.06720 (2024) - [i13]Moïse Blanchard, Patrick Jaillet:
Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers. CoRR abs/2408.10066 (2024) - [i12]Moïse Blanchard:
Agnostic Smoothed Online Learning. CoRR abs/2410.05124 (2024) - 2023
- [c6]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. COLT 2023: 4696-4736 - [c5]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Memory-Constrained Algorithms for Convex Optimization. NeurIPS 2023 - [i11]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Contextual Bandits and Optimistically Universal Learning. CoRR abs/2301.00241 (2023) - [i10]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. CoRR abs/2302.04963 (2023) - [i9]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Non-stationary Contextual Bandits and Universal Learning. CoRR abs/2302.07186 (2023) - [i8]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes. CoRR abs/2306.10096 (2023) - 2022
- [c4]Moïse Blanchard, Romain Cosson, Steve Hanneke:
Universal Online Learning with Unbounded Losses: Memory Is All You Need. ALT 2022: 107-127 - [c3]Moïse Blanchard, Romain Cosson:
Universal Online Learning with Bounded Loss: Reduction to Binary Classification. COLT 2022: 479-495 - [c2]Moïse Blanchard:
Universal Online Learning: an Optimistically Universal Learning Rule. COLT 2022: 1077-1125 - [c1]Moïse Blanchard, Mohammed Amine Bennouna:
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions. ICLR 2022 - [i7]Moïse Blanchard:
Universal Online Learning: an Optimistically Universal Learning Rule. CoRR abs/2201.05947 (2022) - [i6]Moïse Blanchard, Romain Cosson, Steve Hanneke:
Universal Online Learning with Unbounded Losses: Memory Is All You Need. CoRR abs/2201.08903 (2022) - [i5]Moïse Blanchard, Patrick Jaillet:
Universal Regression with Adversarial Responses. CoRR abs/2203.05067 (2022) - [i4]Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet:
Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. CoRR abs/2211.11063 (2022) - [i3]Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet:
Additional Results and Extensions for the paper "Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem". CoRR abs/2211.11065 (2022) - 2021
- [j1]Moïse Blanchard, Jesús A. De Loera, Quentin Louveaux:
On the Length of Monotone Paths in Polyhedra. SIAM J. Discret. Math. 35(3): 1746-1768 (2021) - [i2]Moïse Blanchard, Romain Cosson:
Universal Online Learning with Bounded Loss: Reduction to Binary Classification. CoRR abs/2112.14638 (2021) - 2020
- [i1]Moïse Blanchard, M. Amine Bennouna:
The Representation Power of Neural Networks: Breaking the Curse of Dimensionality. CoRR abs/2012.05451 (2020)
Coauthor Index
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