Proposes a deep learning method for robust ordinal regression under label noise.
problem Label noise in real-world data constrains ordinal regression algorithms.
method Develops a deep learning approach that is robust to label noise and rank consistent.
result Demonstrates robustness to label noise and rank consistency on real data.
Binary feedback outperforms ordinal comparisons in ranking recovery.
problem Challenges the conventional wisdom that ordinal comparisons offer richer information.
method Proposes a parametric framework for modeling ordinal paired comparisons, binarizing ordinal data, and proving faster convergence rates for binary comparisons.
result Binarizing ordinal data significantly improves ranking recovery accuracy and exhibits a substantial performance gap.
Proposes a new method for rank-consistent ordinal regression without weight-sharing constraints.
problem Ordinal response variables in real-world prediction problems are often ignored by conventional classification losses.
method CORN framework using conditional training sets and the chain rule for conditional probability distributions.
result Improves performance substantially compared to the CORAL reference approach without weight-sharing restrictions.
Proposes CORAL framework for consistent ordinal regression in neural networks.
problem Inconsistencies in ordinal regression with neural networks.
method Transforms ordinal targets into binary classification subtasks and applies CORAL framework for rank-monotonicity and consistent confidence scores.
result Reduction of prediction error in age prediction tasks.
Improved image ranking model using ordinal distance metric learning and multidimensional scaling.
problem Ranking images based on known ranked images.
method Proposes an improved linear ordinal distance metric learning approach using multidimensional scaling.
result Demonstrates improved ranking performance and speed over the linear distance metric learning model.
Lognormal distribution used for predicting team rankings in an orienteering relay race.
problem Predicting final team rankings in an orienteering relay race.
method Used lognormal distribution and Fenton-Wilkinson approximations for order statistics.
result Accurate predictions of team rankings using order statistics.
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…
SurvCORN predicts survival curves using conditional ordinal ranking networks.
problem Challenges in survival analysis with censored data.
method SurvCORN: Conditional Ordinal Ranking Neural Network.
result SurvCORN improves accuracy in predicting time-to-event outcomes.
New method estimates and completes tensors from ordinal data, improving accuracy and efficiency.
problem Estimating and completing tensors from incomplete, ordinal observations.
method Multi-linear cumulative link model with rank-constrained M-estimator.
result The proposed estimator achieves faster convergence and is minimax optimal.
A new scoring function improves ordinal regression by combining binary predictions.
problem Improving ordinal regression prediction accuracy.
method Introduces a cumulative sum scoring function to combine binary predictions.
result Simplified formulation and two online learning algorithms converge under rank separability condition.
Framework benchmarks optimizers on multiple criteria.
problem Benchmarking optimizers across diverse test functions.
method Union-free generic depth function for partial orders/rankings.
result Identifies central and outlying rankings of optimizers.
Unified framework for robust ordinal embedding from contaminated comparisons.
problem Inconsistent relative comparisons in ordinal data.
method Jointly identifies and corrects contaminated comparisons, learning embeddings directly.
result Unified framework alleviates sub-optimality and provides a robust solution.
Study on rank 2 Higgs bundles on 5-punctured sphere, proving P=W conjecture in lowest degree.
problem Proving the P=W conjecture for rank 2 Higgs bundles on a 5-punctured sphere. method Abelianization of Higgs bundles, fiducial solutions, and analysis of Fenchel--Nielsen co-ordinates.
result Proved the lowest degree weighted pieces of the P=W conjecture. Study fairness in ordinal regression using threshold models.
problem Fairness in ordinal regression predictions.
method Adapted fairness notions from fair ranking; use threshold model with scoring function and thresholds; apply binary classification for scoring function and local search for thresholds.
result Generalization guarantees on predictor error and fairness violation; effectiveness demonstrated in experiments.
This study introduces balanced DRPS and OrderedLogitNN for better QDE of discrete-level questions.
problem Lack of ordinal regression methods and fair evaluation metrics for discrete-level QDE.
method Introduces balanced DRPS and OrderedLogitNN, fine-tunes BERT on RACE++ and ARC datasets.
result OrderedLogitNN outperforms other models on complex QDE tasks.
New method improves matrix factorization accuracy and speed.
problem Improving matrix factorization for large, noisy data.
method Introducing generalized round-rank (GRR) for ordinal-valued matrices.
result GRR-based matrices cannot be well approximated by low-rank linear factorization.
A scalable algorithm improves AUC optimization for semi-supervised ordinal regression.
problem Optimizing AUC for semi-supervised ordinal regression with limited labeled data.
method Proposes QS3ORAO using quadruply stochastic gradients for scalable kernelized learning. result Converges to optimal solution at O(1/t) rate, demonstrating efficiency and effectiveness. Efficiently trains large-scale ordinal regression models using DCD.
problem Efficiently training large-scale ordinal regression models.
method Dual coordinate descent method (DCD) for training and a new prediction function.
result Extensive experiments show the DCD method is suitable for large-scale data.
In applications such as recommendation systems and revenue management, it is important to predict preferences on items that have not been seen by a user or predict outcomes of comparisons among those that have never been compared. A popular discrete choice model of multinomial logit model captures the structure of the …
The goal of ordinal embedding is to represent items as points in a low-dimensional Euclidean space given a set of constraints in the form of distance comparisons like "item i is closer to item j than item k". Ordinal constraints like this often come from human judgments. To account for errors and variation in jud…
Python package for ordinal regression using gradient boosting.
problem Handling ordinal variables in machine learning.
method Gradient boosting with latent variable framework.
result Performs joint optimization of latent function and threshold vector.
Aims to learn optimal behavior from ranked experts in MDPs.
problem Learning optimal behavior without a specified reward function from ranked experts.
method Uses ordinal regression to find a reward function maximizing the margin between ranks.
result Shows important differences in reward function hidden from existing algorithms.
The paper explores learning metrics in low dimensions with bounds and complexities.
problem Learning metrics in low dimensions with bounds and complexities.
method Develops upper and lower bounds on generalization error, quantifies sample complexity, and bounds accuracy relative to the true metric.
result Novel mathematical approaches to metric learning and insights into ordinal embedding.
Paper proposes a method to improve deep neural networks' confidence estimates.
problem Overconfident predictions limit practical use of deep neural networks in safety-critical applications.
method Proposes a novel loss function, Correctness Ranking Loss, to regularize class probabilities.
result The method produces well-ranked confidence estimates and is effective for out-of-distribution detection and active learning.
NIPS 2016 analyzed its review process to improve future conferences.
problem Growth in submissions, reviewers, and attendees requires better quality assessment.
method Analyzed data from the review process, including ordinal rankings experiments.
result Investigated the efficacy of collecting ordinal rankings from reviewers.
Adapts deep reinforcement learning to ordinal rewards.
problem Using numerical rewards in reinforcement learning has drawbacks; ordinal rewards offer an alternative.
method Develops a general approach to converting reinforcement learning algorithms to ordinal reward systems, including Ordinal Deep Q-Networks.
result Ordinal Deep Q-Networks perform comparably to numerical variants on engineered problems and better on simpler reward signals.
Paper challenges the usefulness of cardinal scores without simplifying assumptions on miscalibration.
problem Handling arbitrary miscalibrations in ratings.
method Designing estimators for cardinal scores with arbitrary miscalibrations, consistent with induced ranking.
result Strict and uniform outperformance of estimators over all possible ranking-based estimators.
Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied t…
Proposes models to better represent ordinal data with non-unimodal distributions.
problem Real-world ordinal data often have non-unimodal conditional probability distributions.
method Develops approximately unimodal likelihood models to better represent non-unimodal CPDs.
result Proposed models can effectively represent both unimodal and nearly unimodal CPDs.
Develops multi-task learning models for ordinal regression with heterogeneous data.
problem Tackling ordinal regression for heterogeneous, non-IID data.
method Sparse and deep multi-task learning approaches.
result Proposed MTOR models improve prediction performance.
A new framework estimates causal effects for ordinal variables.
problem Existing causal inference methods fail for ordinal data.
method Presumes a latent Gaussian DAG model with constrained covariance matrix.
result Closed-form function for ordinal causal effects in latent space.
A game-theoretic approach to multi-criteria ranking from ordinal data.
problem Ranking objects from ordinal data with multiple criteria.
method Generalizing von Neumann winner to multi-criteria setting using Blackwell's approachability.
result The Blackwell winner can be computed as a convex optimization problem and achieves near-optimal sample complexity.
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in da…
New method uniquely identifies causal structure from ordinal data.
problem Challenges in causal discovery for categorical data, especially direction of relationships.
method Exploits ordinal information to uniquely identify causal structure.
result Favorable and robust performance compared to state-of-the-art methods.
A new framework for semi-supervised ordinal regression.
problem Lack of evaluation metrics and theoretical guarantees in existing semi-supervised ordinal regression.
method Empirical risk minimization principle, flexible model choices, and estimation error bound.
result Consistent risk estimator and improved performance across various metrics.
A new R package for ordinal classification and preprocessing.
problem Lack of proper methods for ordinal data in machine learning.
method Developed an R package named ocapis in Scala.
result Improves classification and preprocessing of ordinal data.
New methods for ordinal classification of interval-valued data and functional data.
problem Ordinal classification of interval-valued data and functional data.
method Six ordinal classifiers are proposed, including parametric, binary decomposition, logistic regression, distance-based, k-nearest-neighbor, kernel PCA, and random forest methods.
result Considering ordering and interval-valued information improves the accuracy of ordinal classification.
Novel variational autoencoder for generative and classification tasks.
problem Developing a robust generative model for various tasks.
method A novel variational autoencoder with specific latent variables and ordinality enforcement.
result Comparable performance in generative and classification tasks compared to baselines.
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 …
The paper tackles feature selection for ordinal regression, considering feature redundancies and privileged information.
problem Discovering relevant factors in ranked data with potentially redundant features and privileged information.
method Develops feature relevance bounds for linear ordinal regression, considering feature redundancies and privileged information.
result Identifies all strongly and weakly relevant features and their type of relevance.
Optimal bounds proven for ordinal embedding convergence rate.
problem Optimal bounds for ordinal embedding convergence rate in 1D.
method Utilized results from additive number theory and conducted computational experiments.
result Proved optimal bounds for convergence rate in 1D.
Deep neural networks for ordinal outcomes combining image and tabular data.
problem Lack of interpretable models for ordinal outcomes in mixed data types.
method Ordinal Neural Network Transformation Models (ONTRAMs) integrating DL and classical ordinal regression.
result ONTRAMs achieve performance equivalent to standard multi-class DL models but are faster and more interpretable.
The paper proposes a framework for structured prediction using projection oracles.
problem Structured prediction with improved loss functions.
method A general framework for deriving loss functions using convex sets and projection oracles.
result Projections onto the marginal polytope can make the loss smaller and are computationally efficient.
Ordinal embedding methods estimate perceptual scales from relative judgments.
problem Measuring subjective sensation using relative judgments.
method Ordinal embedding from machine learning applied to method of triads.
result Ordinal embedding allows estimating perceptual scales from few judgments, non-monotonous functions, and multi-dimensional scales.
Study homeomorphism groups of ordinals, proving strong distortion and normal generators.
problem Understanding algebraic and geometric properties of homeomorphism groups of ordinals.
method Analyzing successor ordinals with connections to permutation groups and manifolds.
result Proves strong distortion and normal generators for homeomorphism groups of ordinals.
Identifies feature relevance bounds for ordinal regression models.
problem Interpreting ordinal regression models is challenging due to variable dependencies.
method Identifies feature relevance bounds explicitly differentiating between strongly and weakly relevant features.
result Identification of feature relevance bounds for ordinal regression models.
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
problem Measuring representation similarity in large datasets.
method Triplet and Quadruplet Similarity Indices.
result Demonstrates inherent interpretability, robustness to outliers, and computational efficiency.
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…