In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to...
Abstract. In this paper, we identify issues and present solutions developed – both theoretical and experimental – during the course of developing a data stream management syste...
Abstract. This paper is concerned with the question of quantifying gradient degrees of acceptability by introducing the notion of Density in the context of constructional constrain...