We propose a decision-theoretic sparsification method for Gaussian process preference learning. This method overcomes the loss-insensitive nature of popular sparsification approaches such as the Informative Vector Machine (IVM). Instead of selecting a su
Abstract. In this paper we propose a fast online preference learning algorithm capable of utilizing ...
In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm fo...
We propose an efficient optimization algorithm for selecting a subset of training data to induce spa...
We propose a decision-theoretic sparsification method for Gaussian process preference learning. This...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian...
Gaussian Process Preference Learning (GPPL) is considered to be the state-of-the-art algorithm for l...
Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive due to thei...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
We present a new model based on Gaussian processes (GPs) for learning pair-wise preferences expresse...
Information theoretic active learning has been widely studied for prob-abilistic models. For simple ...
We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric cl...
Human preferences can effectively be elicited using pairwise comparisons and in this paper current s...
We revisit widely used preferential Gaussian processes (PGP) by Chu and Ghahramani [2005] and challe...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
Abstract. In this paper we propose a fast online preference learning algorithm capable of utilizing ...
In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm fo...
We propose an efficient optimization algorithm for selecting a subset of training data to induce spa...
We propose a decision-theoretic sparsification method for Gaussian process preference learning. This...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian...
Gaussian Process Preference Learning (GPPL) is considered to be the state-of-the-art algorithm for l...
Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive due to thei...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
We present a new model based on Gaussian processes (GPs) for learning pair-wise preferences expresse...
Information theoretic active learning has been widely studied for prob-abilistic models. For simple ...
We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric cl...
Human preferences can effectively be elicited using pairwise comparisons and in this paper current s...
We revisit widely used preferential Gaussian processes (PGP) by Chu and Ghahramani [2005] and challe...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
Abstract. In this paper we propose a fast online preference learning algorithm capable of utilizing ...
In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm fo...
We propose an efficient optimization algorithm for selecting a subset of training data to induce spa...
DOI
Add to ORKG