Gaussian Processes for Ordinal Regression

By Wei Chu, Zoubin Ghahramani. Journal of machine learning research, 2005

502 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/724a2430522c7c398c01f90330f41e187c6a7243

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Citations in the corpus

Bayesian Gaussian Process Classification with the EM-EP Algorithm (2006, methodology): “This discretization actually creates a three-class ordinal variable, so ordinal regression methods may be more appropriate [30].”
Improving maximum margin matrix factorization (2008, result): “Strong generalization For the strong generalization setting, we compare our results to Gaussian Process Ordinal Regression (GPOR) (Chu and Ghahramani 2005) Gaussian Process Regression (GPR), the collaborative extensions (CPR, CGPOR) as well as the original MMMF implementation (Yu et al. 2006; Weimer”
SoftRank: optimizing non-smooth rank metrics (2008, methodology): “An example of this approach is PRank [8]; for a Bayesian treatment see [7].”
Alors: An algorithm recommender system (2017, methodology): “In the rank-based case for instance, a cumulative threshold model is built (Chu and Ghahramani, 2005), maintaining for each user the L − 1 thresholds used to segment the continuous latent ratings into L intervals of varying length, respectively mapped onto ranks 1 to L .”
Matchbox: large scale online bayesian recommendations (2009, unknown): “We relate the latent rating r to ranks l via a cumulative threshold model [4].”
The future of skills: employment in 2030 (2017, methodology): “standard deviation associated with each observation is an affine transformation of the chosen value: Our approach differs from the state-of-the-art (Chu and Ghahramani, 2005), firstly, in incorporating a heteroskedastic noise model.”
Ordinal Regression Methods: Survey and Experimental Study (2016, background): “…research [5]–[11], age estimation [12], brain computer interface [13], credit rating [14]–[17], econo-metric modelling [18], face recognition [19]–[21], facial beauty assessment [22], image classiﬁcation [23], wind speed prediction [24], social sciences [25], text classiﬁcation [26], and more.”
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (2005, unknown): “Chu and Ghahramani [2005] have described how to use GPs for the ordinal regression problem, where one is given ranked preference information as the target data.”
On the Consistency of Ordinal Regression Methods (2014, methodology): “e dierent datasets used are described in [5].”
Probabilistic Matrix Factorization with Non-random Missing Data (2014, methodology): “We use EP for the CDM as it performs well in ordinal regression (Chu & Ghahramani, 2005).”
Introduction to Semi-Supervised Learning (2009, methodology): “It can be implemented in Gaussian Processes with the null category noise model [106, 40], as information regularization [169, 44, 45], maximum entropy discrimination approach [87], or entropy minimization [79, 108, 122].”
Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014, methodology): “To capture the discrete nature and natural ordering of rating data, our model takes an ordinal regression approach (Chu & Ghahramani, 2005; Stern et al., 2009).”
General Latent Feature Models for Heterogeneous Datasets (2017, methodology): “Under the ordered probit model [5], the probability of each element xn taking value r ∈ {1, .”
Gaussian Process Preference Elicitation (2010, methodology): “Extensions of the Gaussian process formalism to model ordinal data and user preferences are given in [12] and [13].”