Andrew Jacobsen

According to our database, Andrew Jacobsen authored at least 13 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Gradient-Variation Regret Bounds for Unconstrained Online Learning.

Yuheng Zhao, Andrew Jacobsen, Nicolò Cesa-Bianchi, Peng Zhao

CoRR, April, 2026

A Perturbation Approach to Unconstrained Linear Bandits.

Andrew Jacobsen, Dorian Baudry, Shinji Ito, Nicolò Cesa-Bianchi

CoRR, March, 2026

Parameter-Free Dynamic Regret for Unconstrained Linear Bandits.

Alberto Rumi, Andrew Jacobsen, Nicolò Cesa-Bianchi, Fabio Vitale

CoRR, March, 2026

Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory.

Emmanuel Esposito, Andrew Jacobsen, Hao Qiu, Mengxiao Zhang

CoRR, February, 2026

2025

Dynamic Regret Reduces to Kernelized Static Regret.

Andrew Jacobsen, Alessandro Rudi, Francesco Orabona, Nicolò Cesa-Bianchi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

An Equivalence Between Static and Dynamic Regret Minimization.

Andrew Jacobsen, Francesco Orabona

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Online Linear Regression in Dynamic Environments via Discounting.

Andrew Jacobsen, Ashok Cutkosky

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Unconstrained Online Learning with Unbounded Losses.

Andrew Jacobsen, Ashok Cutkosky

Proceedings of the International Conference on Machine Learning, 2023

2022

Parameter-free Mirror Descent.

Andrew Jacobsen, Ashok Cutkosky

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

General Value Function Networks.

Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White

J. Artif. Intell. Res., 2021

Parameter-free Gradient Temporal Difference Learning.

Andrew Jacobsen, Alan Chan

CoRR, 2021

Continual Auxiliary Task Learning.

Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

Meta-Descent for Online, Continual Prediction.

Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019