255 citations · 2.4k across the 127 of their papers we have counts for
5 papers · 2 filters
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits
Tor Lattimore, Csaba Szepesvari
Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing te…
Multiclass Classification Calibration Functions
Bernardo Ávila Pires, Csaba Szepesvári
In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for eas…
Chaining Bounds for Empirical Risk Minimization
Gábor Balázs, András György, Csaba Szepesvári
This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise…
Conservative Bandits
Yifan Wu, Roshan Shariff, Tor Lattimore +1
We study a novel multi-armed bandit problem that models the challenge faced by a company wishing to explore new strategies to maximize revenue whilst simultaneously maintaining the…
Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models
Bernardo Ávila Pires, Csaba Szepesvári
In this paper we study a model-based approach to calculating approximately optimal policies in Markovian Decision Processes. In particular, we derive novel bounds on the loss of us…