A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
The study explores how to infer the geometry of space forms from similarity comparisons.
A new kernel measures brain network similarities, improving disease classification.
Paper tackles clustering with ordinal comparisons, achieving near-optimal results.
This paper applies deep learning to ordinal regression, modeling it as a binary search.
A scalable algorithm improves AUC optimization for semi-supervised ordinal regression.
In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: "Is stimulus S_i more similar to stimulus S_j or to stimulus S_k?". …
We consider the problem of embedding unweighted, directed k-nearest neighbor graphs in low-dimensional Euclidean space. The k-nearest neighbors of each vertex provides ordinal information on the distances between points, but not the distances themselves. We use this ordinal information along with the low-dimensionality…
In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, colle…
Paper introduces novel distances for clustering ordinal time series.
We address the problem of general supervised learning when data can only be accessed through an (indefinite) similarity function between data points. Existing work on learning with indefinite kernels has concentrated solely on binary/multi-class classification problems. We propose a model that is generic enough to hand…
A new scoring function improves ordinal regression by combining binary predictions.
Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapt…
Proposes models to better represent ordinal data with non-unimodal distributions.
Proposes a deep learning method for robust ordinal regression under label noise.
A new algorithm reduces the time for ordinal embedding, making it faster and more scalable.
Study shows 'Ordinal Neural Collapse' in deep OR tasks, revealing simple geometric relationships.
A new framework estimates causal effects for ordinal variables.
New method uniquely identifies causal structure from ordinal data.
Binary feedback outperforms ordinal comparisons in ranking recovery.
New methods for ordinal classification of interval-valued data and functional data.
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate …
Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and id…
Deep neural networks for ordinal outcomes combining image and tabular data.
This study introduces balanced DRPS and OrderedLogitNN for better QDE of discrete-level questions.
Study homeomorphism groups of ordinals, proving strong distortion and normal generators.
Ordinal regression is aimed at predicting an ordinal class label. In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absol…
Proposes a new method for rank-consistent ordinal regression without weight-sharing constraints.
Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework i…
When eliciting judgements from humans for an unknown quantity, one often has the choice of making direct-scoring (cardinal) or comparative (ordinal) measurements. In this paper we study the relative merits of either choice, providing empirical and theoretical guidelines for the selection of a measurement scheme. We pro…
Landmark Ordinal Embedding improves scalability of ordinal embedding.
XOFM explains attribute effects in ordinal regression using piece-wise linear functions.
Algorithm finds real line mapping from points under ordinal constraints.
Lognormal distribution used for predicting team rankings in an orienteering relay race.
The paper identifies and critiques problems with risk matrices using ordinal scales.
Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nomi…
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra…
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into…
Develops methods to control risk in ordinal classification tasks.
In recent years it has become popular to study machine learning problems in a setting of ordinal distance information rather than numerical distance measurements. By ordinal distance information we refer to binary answers to distance comparisons such as . For many problems in machine learning and statist…
New framework learns complex AI attitudes from heterogeneous data.
We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learnin…
ROVAE uses noisy pairwise comparisons to disentangle factors in VAEs.
Proposes a deep ordinal regression framework using optimal transport loss and unimodal output probabilities.
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and …
The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through multidimensional scaling (MDS), which converts human dissimilarity ratings for a fixed set of stimuli into a spatial representation. One can d…
A new neural network model for ordinal regression.
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …