75 citations · 820 across the 81 of their papers we have counts for
9 papers · 1 filter
On the Complexity of Representation Learning in Contextual Linear Bandits
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
In contextual linear bandits, the reward function is assumed to be a linear combination of an unknown reward vector and a given embedding of context-arm pairs. In practice, the emb…
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
Yifang Chen, Karthik Sankararaman, Alessandro Lazaric +6
Active learning with strong and weak labelers considers a practical setting where we have access to both costly but accurate strong labelers and inaccurate but cheap predictions pr…
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
Andrea Tirinzoni, Matteo Papini, Ahmed Touati +2
We study the problem of representation learning in stochastic contextual linear bandits. While the primary concern in this domain is usually to find realizable representations (i.e…
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta +1
We study the sample complexity of learning an -optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access…
Contextual bandits with concave rewards, and an application to fair ranking
Virginie Do, Elvis Dohmatob, Matteo Pirotta +2
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-objective bandit problem where the desired trade-off between the rewards is defined by a known concave objective…
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
Rui Yuan, Simon S. Du, Robert M. Gower +2
We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear pol…