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Dimitrios Stamos

2 accepted papers

2020

Online Parameter-Free Learning of Multiple Low Variance Tasks

UAI 2020poster

We propose a method to learn a common bias vector for a growing sequence of low-variance tasks. Unlike state-of-the-art approaches, our method does not require tuning any hyper-parameter. Our approach is presented in the non-statistical setting and can be of two variants. The “aggressive” one update…

2019

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

ICML 2019oral

We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs. We propose an efficient algorithm based on trace norm regularization which, differently from previous methods, does not requ…