arXiv AI
Jul 28
D3O: Dynamic Distribution Distillation for Ordinal Regression
arXiv:2607. 23575v1 Announce Type: cross Abstract: Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered.
Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
arXiv:2607. 08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption.
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arXiv Machine Learning
Sep 25
MORE-PLR: multi-output regression employed for partial label ranking
The paper introduces MORE-PLR, a method that tackles the partial label ranking problem by employing multi-output regression. It uses an encoder to transform incomplete rankings with ties into regression targets during training, and applies post‑hoc layers during inference to convert regression outputs into bucket orders. Experiments show that this framework competes with state‑of‑the‑art partial label ranking methods.
Hugging Face Trending Papers
Jul 9
Contrastive Order Learning: A General Framework for Ordinal Regression
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among rank labels.
arXiv Machine Learning
Jul 10
Contrastive Order Learning: A General Framework for Ordinal Regression
arXiv:2607. 08109v1 Announce Type: new Abstract: We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning.
arXiv AI
Jun 8
Bounded-Abstention Pairwise Learning to Rank
arXiv:2505. 23437v2 Announce Type: replace-cross Abstract: Ranking systems influence decision-making in high-stakes domains like health, education, and employment, where they can have substantial economic and social impacts.
arXiv Machine Learning
Jun 25
Deep Neural Networks with Ordinal Loss for Medical Applications
arXiv:2606. 25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meaningful severity levels.
arXiv Machine Learning
Aug 11
A Control Function Framework for Mitigating Position Bias in Learning to Rank Systems
arXiv:2506. 06989v3 Announce Type: replace-cross Abstract: Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences.
arXiv AI
Sep 15
Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores
arXiv:2601.13885v2 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluatio...
arXiv Machine Learning
Jun 5
A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth
arXiv:2601. 21817v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.
arXiv Machine Learning
Jun 5
Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference
arXiv:2606. 05308v1 Announce Type: new Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set.
arXiv Statistics ML
Aug 25
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach
The paper tackles a ranking and selection problem where a company learns from choice-based feedback presented in dynamic assortments. It introduces two efficient algorithms—Nested Elimination for best-item identification and Nested Partition for full-ranking identification—each with instance-specific, non-asymptotic sample-complexity guarantees that are asymptotically worst-case optimal. The authors analyze the algorithms via multi-dimensional random walks, extend the framework to capacity-constrained displays, and validate their results with synthetic and real data experiments.
arXiv Machine Learning
Jul 3
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
arXiv:2607. 02182v1 Announce Type: new Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy deployment.