Meta-Descent for Online, Continual Prediction. - Andrew Jacobsen; Matthew Schlegel; Cameron Linke; Thomas Degris; Adam White 001; Martha White (AAAI, 2019)

Continual Auxiliary Task Learning. - Matthew McLeod; Chunlok Lo; Matthew Schlegel; Andrew Jacobsen; Raksha Kumaraswamy; Martha White; Adam White 001 (NeurIPS, 2021)

Parameter-free Gradient Temporal Difference Learning. - Andrew Jacobsen; Alan Chan (CoRR, 2021)

General Value Function Networks. - Matthew Schlegel; Andrew Jacobsen; Zaheer Abbas; Andrew Patterson; Adam White 001; Martha White (J. Artif. Intell. Res., 2021)

Parameter-free Mirror Descent. - Andrew Jacobsen; Ashok Cutkosky (COLT, 2022)

Unconstrained Online Learning with Unbounded Losses. - Andrew Jacobsen; Ashok Cutkosky (ICML, 2023)

Online Linear Regression in Dynamic Environments via Discounting. - Andrew Jacobsen; Ashok Cutkosky (ICML, 2024)

An Equivalence Between Static and Dynamic Regret Minimization. - Andrew Jacobsen; Francesco Orabona (NeurIPS, 2024)

Dynamic Regret Reduces to Kernelized Static Regret. - Andrew Jacobsen; Alessandro Rudi; Francesco Orabona; Nicolò Cesa-Bianchi (CoRR, 2025)