Large margin rank boundaries for ordinal regression

By Ralf Herbrich. 2000

370 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/81b8bb14524665e3dbaa772f297cd5e5e79ba0d6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Dacheng Tao
3. Quoc V. Le
4. Mohit Bansal
5. Zhe Gan
6. Yi Yang
7. Kyunghyun Cho
8. Sergey Levine

Citations in the corpus

• Reducing Dueling Bandits to Cardinal Bandits (2014, background): “There is yet more work on bandit games where the algorithm plays two bandits (or more) in each iteration, e.g. Agarwal et al. Agarwal et al. (2010), although there the feedback is cardinal and not relative in each step.”
• Pranking with Ranking (2001, background): “One of the main results of [1℄ un-ders ores a omplexity gap between lassi ation learning and ranking learning.”
• A Discriminative Kernel-Based Approach to Rank Images from Text Queries (2008, background): “of margin is inspired from the definition introduced in [19]”
• How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation (2016, background): “Any problem in the evaluation set should be invisible to the people who design an automatic math problem solving system.”
• A support vector method for multivariate performance measures (2005, unknown): “, 1998), and SVMs (Herbrich et al., 2000; Rakotomamonjy, 2004).”
• Surrogate regret bounds for bipartite ranking via strongly proper losses (2012, unknown): “L G ]”
• IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (2017, methodology): “thm RankNet [3]. In addition to the logistic function log„1+exp„z””, it is possible to make use of other pairwise ranking loss functions [7], such as the hinge function „1 z”+ (as used in Ranking SVM [16]) and the exponential function exp„z”(as used in RankBoost [11]), to de•ne the probability D„hdu”
• Integer optimization methods for machine learning (2012, unknown): “, 2009], which is similar to ordinal regression [Herbrich et al., 2000].”
• Multi-task learning for boosting with application to web search ranking (2010, background): “Categories and Subject Descriptors I.2.6 [Artificial intelligence]: Learning; H.3.3 [Information storage and retrieval]: Information search and retrieval General Terms Algorithms”
• Learning to Rank for Information Retrieval and Natural Language Processing (2011, unknown): “;( wxf31 xx 32 xx 21 xx +1 -1 12 xx 23 xx 13 xx Positive Examples Negative Examples Redundant Ranking SVM • Pairwise classification on differences of feature vectors • Corresponding positive and negative examples • Negative examples are redundant and can be discarded • Hyper plane passes the o”
• An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain (2012, background): “The reason for this is that a user would rarely browse beyond the first page of search results containing the first nmax hits.”
• Adapting ranking SVM to document retrieval (2006, methodology): “[8], for instance, propose a method of learning to rank on the basis of SVM and apply their method to document retrieval.”
• L2R²: Leveraging Ranking for Abductive Reasoning (2020, methodology): “Hinge loss used in Ranking SVM and logistic (cross entropy) loss used in RankNet both have the following form: Lp (y, s) = ∑ yj>yk ϕ(sj − sk ), (2) where the ϕ functions are hinge function (ϕ(z) = max{0, 1−z}) and logistic function (ϕ(z) = log(1 + e−z )) respectively; yj > yk means that H j ranks hi”
• Bundle Methods for Regularized Risk Minimization (2010, unknown): “, 2006), ordinal regression (Herbrich et al., 2000), rank-”