Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Contributo in Atti di convegno
Data di Pubblicazione:
2023
Citazione:
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning / P. Giuseppe Sessa, P. Laforgue, N. Cesa Bianchi, A. Krause (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS). - In: Advances in Neural Information Processing Systems 36 / [a cura di] A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, S. Levine. - [s.l] : Curran Associates, 2023. - pp. 6770-6781 (( Intervento presentato al 37. convegno Neural Information Processing Systems nel 2023.
Abstract:
Multitask learning is a powerful framework that enables one to simultaneously
learn multiple related tasks by sharing information between them. Quantifying
uncertainty in the estimated tasks is of pivotal importance for many downstream
applications, such as online or active learning. In this work, we provide novel
confidence intervals for multitask regression in the challenging agnostic setting, i.e.,
when neither the similarity between tasks nor the tasks’ features are available to the
learner. The obtained intervals do not require i.i.d. data and can be directly applied
to bound the regret in online learning. Through a refined analysis of the multitask
information gain, we obtain new regret guarantees that, depending on a task
similarity parameter, can significantly improve over treating tasks independently.
We further propose a novel online learning algorithm that achieves such improved
regret without knowing this parameter in advance, i.e., automatically adapting
to task similarity. As a second key application of our results, we introduce a
novel multitask active learning setup where several tasks must be simultaneously
optimized, but only one of them can be queried for feedback by the learner at each
round. For this problem, we design a no-regret algorithm that uses our confidence
intervals to decide which task should be queried. Finally, we empirically validate
our bounds and algorithms on synthetic and real-world (drug discovery) data.
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
P. Giuseppe Sessa, P. Laforgue, N. Cesa Bianchi, A. Krause
Link alla scheda completa:
Titolo del libro:
Advances in Neural Information Processing Systems 36