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.
The paper proposes a new method for learning reward models from ordinal feedback, improving upon binary feedback.
problem Learning reward models from human preferences using binary feedback discards useful samples and loses fine-grained information.
method The paper introduces a framework for learning reward models under ordinal feedback, generalizing the Bradley-Terry model.
result Ordinal feedback reduces the Rademacher complexity compared to binary feedback, leading to better reward learning.
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.
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.
We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator,…
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.
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.
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.
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.
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.
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 …
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.
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.
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.
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.
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.
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.
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.
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…
A new kernel measures brain network similarities, improving disease classification.
problem Lack of edge weight information in existing graph kernels for brain connectivity networks.
method Ordinal pattern kernel for weighted brain connectivity networks.
result The ordinal pattern kernel achieves better classification performance than state-of-the-art graph kernels.
Landmark Ordinal Embedding improves scalability of ordinal embedding.
problem Learning low-dimensional Euclidean representations from ordinal constraints.
method Landmark-based strategy (LOE) that trades statistical efficiency for computational efficiency.
result LOE is significantly more efficient than conventional methods as the number of items grows.
This paper applies deep learning to ordinal regression, modeling it as a binary search.
problem Ordinal regression with deep learning models.
method Formulated ordinal regression as a binary search problem, using recurrent neural networks.
result Deep learning model shows comparable or better predictive power compared to traditional methods.
XOFM explains attribute effects in ordinal regression using piece-wise linear functions.
problem Lack of detailed attribute contributions in existing ordinal regression models.
method XOFM uses piece-wise linear functions to approximate attribute contributions and introduces ordinal transformation.
result XOFM provides superior explainability and state-of-the-art prediction accuracy.
Algorithm finds real line mapping from points under ordinal constraints.
problem Finding a mapping from points to real line under ordinal constraints.
method Approximation algorithm for dense case in O(n7)+(1/ε)O(1/ε1/8)n time. result Computes a solution satisfying (1−O(ε1/8))-fraction of all constraints. 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.
The paper identifies and critiques problems with risk matrices using ordinal scales.
problem Problems with risk matrices using ordinal scales.
method Overview of risk assessment process, explanation of fallacies, and suggestions for improvement.
result The paper proposes avoiding risk matrices and using fully quantitative methods instead.
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.
problem Controlling risk in ordinal classification tasks.
method Formulated ordinal classification in conformal risk control framework, proposed loss functions and algorithms.
result Demonstrated effectiveness and analyzed differences in risk control methods.
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 d(A,B)<d(C,D). For many problems in machine learning and statist…
Transforms ordinal regression to structured classification for healthcare applications.
problem Improper handling of label ordering in machine learning applications.
method Applies conditional random fields to solve a structured classification problem derived from ordinal regression.
result Proposed method outperforms baseline approaches on various datasets.
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
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.
problem Disentangling factors in VAEs requires an inductive bias.
method Robust Ordinal VAE (ROVAE) incorporates noisy pairwise ordinal comparisons to disentangle factors.
result ROVAE outperforms existing methods and is more robust to noisy comparisons.
Proposes a deep ordinal regression framework using optimal transport loss and unimodal output probabilities.
problem Lack of unimodal output probabilities in recent ordinal regression models.
method Introduces a deep learning framework based on optimal transport loss and unimodal output distribution, inspired by the Proportional Odds model.
result Demonstrates improved performance and unimodal output probabilities on real-world datasets compared to existing methods.
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 …
A new neural network model for ordinal regression.
problem Ordinal regression with non-proportional odds.
method Interpretable neural network for both continuous and discrete responses, training a non-linear neural network as a coefficient function.
result N3POM preserves interpretability while offering flexibility. 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 …
We address the task of simultaneous feature fusion and modeling of discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the…
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. The study explores how to infer the geometry of space forms from similarity comparisons.
problem Inferring the geometry of space forms from unreliable similarity measurements.
method Introducing ordinal capacity and spread, proving their relation to space form properties, and using statistical analysis of similarity measurements.
result The statistical behavior of ordinal spread variables can identify the underlying space form.
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal…
The paper develops distribution-free methods for ordinal classification.
problem Constructing valid prediction sets for ordinal classification problems.
method Leveraging conformal prediction and multiple testing with FWER control.
result The proposed methods achieve satisfactory levels of marginal and class-specific conditional coverages.
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.