The Many Faces of Exponential Weights in Online Learning. - Dirk van der Hoeven; Tim van Erven; Wojciech Kotlowski (COLT, 2018)

User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning. - Dirk van der Hoeven (NeurIPS, 2019)

Open Problem: Fast and Optimal Online Portfolio Selection. - Tim van Erven; Dirk van der Hoeven; Wojciech Kotlowski; Wouter M. Koolen (COLT, 2020)

Exploiting the Surrogate Gap in Online Multiclass Classification. - Dirk van der Hoeven (NeurIPS, 2020)

Comparator-Adaptive Convex Bandits. - Dirk van der Hoeven; Ashok Cutkosky; Haipeng Luo (NeurIPS, 2020)

Beyond Bandit Feedback in Online Multiclass Classification. - Dirk van der Hoeven; Federico Fusco; Nicolò Cesa-Bianchi (NeurIPS, 2021)

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. - Tim van Erven; Wouter M. Koolen; Dirk van der Hoeven (J. Mach. Learn. Res., 2021)

Nonstochastic Bandits and Experts with Arm-Dependent Delays. - Dirk van der Hoeven; Nicolò Cesa-Bianchi (AISTATS, 2022)

Distributed Online Learning for Joint Regret with Communication Constraints. - Dirk van der Hoeven; Hédi Hadiji; Tim van Erven (ALT, 2022)

Learning on the Edge: Online Learning with Stochastic Feedback Graphs. - Emmanuel Esposito; Federico Fusco; Dirk van der Hoeven; Nicolò Cesa-Bianchi (NeurIPS, 2022)

A Regret-Variance Trade-Off in Online Learning. - Dirk van der Hoeven; Nikita Zhivotovskiy; Nicolò Cesa-Bianchi (NeurIPS, 2022)

A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs. - Chloé Rouyer; Dirk van der Hoeven; Nicolò Cesa-Bianchi; Yevgeny Seldin (NeurIPS, 2022)

Nonstochastic Contextual Combinatorial Bandits. - Lukas Zierahn; Dirk van der Hoeven; Nicolò Cesa-Bianchi; Gergely Neu (AISTATS, 2023)

A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs. - Dirk van der Hoeven; Lukas Zierahn; Tal Lancewicki; Aviv Rosenberg 002; Nicolò Cesa-Bianchi (COLT, 2023)

Delayed Bandits: When Do Intermediate Observations Help? - Emmanuel Esposito; Saeed Masoudian; Hao Qiu; Dirk van der Hoeven; Nicolò Cesa-Bianchi; Yevgeny Seldin (ICML, 2023)

Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts. - Dirk van der Hoeven; Ciara Pike-Burke; Hao Qiu; Nicolò Cesa-Bianchi (ICML, 2023)

High-Probability Risk Bounds via Sequential Predictors. - Dirk van der Hoeven; Nikita Zhivotovskiy; Nicolò Cesa-Bianchi (CoRR, 2023)

Online Newton Method for Bandit Convex Optimisation Extended Abstract. - Hidde Fokkema; Dirk van der Hoeven; Tor Lattimore; Jack J. Mayo (COLT, 2024)

Online Newton Method for Bandit Convex Optimisation. - Hidde Fokkema; Dirk van der Hoeven; Tor Lattimore; Jack J. Mayo (CoRR, 2024)

When Lower-Order Terms Dominate: Adaptive Expert Algorithms for Heavy-Tailed Losses. - Antoine Moulin; Emmanuel Esposito; Dirk van der Hoeven (CoRR, 2025)

Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability. - Dirk van der Hoeven; Julia Olkhovskaia; Tim van Erven (CoRR, 2025)