| 2024 | COLT | Online Newton Method for Bandit Convex Optimisation Extended Abstract. | Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo |
| 2023 | AISTATS | Nonstochastic Contextual Combinatorial Bandits. | Lukas Zierahn, Dirk van der Hoeven, Nicol Cesa-Bianchi, Gergely Neu |
| 2023 | COLT | 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, Nicol Cesa-Bianchi |
| 2023 | ICML | Delayed Bandits: When Do Intermediate Observations Help? | Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicol Cesa-Bianchi, Yevgeny Seldin |
| 2023 | ICML | Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts. | Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicol Cesa-Bianchi |
| 2022 | AISTATS | Nonstochastic Bandits and Experts with Arm-Dependent Delays. | Dirk van der Hoeven, Nicol Cesa-Bianchi |
| 2022 | ALT | Distributed Online Learning for Joint Regret with Communication Constraints. | Dirk van der Hoeven, Hdi Hadiji, Tim van Erven |
| 2020 | COLT | Open Problem: Fast and Optimal Online Portfolio Selection. | Tim van Erven, Dirk van der Hoeven, Wojciech Kotlowski, Wouter M. Koolen |
| 2018 | COLT | The Many Faces of Exponential Weights in Online Learning. | Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski |