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stat.ML2016★ 12 cited
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…
stat.ML2016★ 14 cited
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…