Learning to Rank papers, 2023–2026

Key findings

• Learning to Rank fell from 142 papers in 2023 to 55 in 2026, so interest is cooling.
• NeurIPS publishes the most Learning to Rank work: 154 papers from 2023 to 2026, 60% of the four-venue total.
• The fastest-growing overlap is with Low-Rank Adaptation: papers tagged with both rose from 39 in 2023 to 43 in 2026.
• Among the 5 topics under Information Retrieval, it ranks #4 by 2026 paper count; the fastest grower in the group, Cross-Encoder Reranking, is up 3.0x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
• ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
• ICLR 2026Designing Rules to Pick a Rule: Aggregation by Consistency
• CVPR 2026LRDUN A Low-Rank Deep Unfolding Network for Efficient Spectral Compressive
• NeurIPS 2025From Condensation to Rank Collapse: A Two-Stage Analysis of Transformer Training Dynamics
• ICML 2025Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
• ICLR 2025Palu: KV-Cache Compression with Low-Rank Projection
• CVPR 2025Breaking the Low-Rank Dilemma of Linear Attention
• NeurIPS 2024Efficient LLM Scheduling by Learning to Rank
• ICML 2024Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
• ICLR 2024LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
• CVPR 2024Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning
• NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
• ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
• ICLR 2023Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
• CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
• NeurIPS 2026Matrix Recovery Via Symmetric Rank-one Measurements With Random Unit-modulus Vectors
• ICML 2026The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
• ICLR 2026Revisiting Weight Regularization for Low-Rank Continual Learning
• CVPR 2026Low-Rank Residual Diffusion Models

All Learning to Rank papers

28 papers, page 1 of 1

1. CVPR 2024LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
2. CVPR 2024PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
3. CVPR 2024Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning
4. ICLR 2024LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
5. ICLR 2024Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming
6. ICML 2024Efficient Pareto Manifold Learning with Low-Rank Structure
7. ICML 2024Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
8. ICML 2024FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees
9. ICML 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressors
10. ICML 2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
11. ICML 2024High-Dimensional Geometric Streaming for Nearly Low Rank Data
12. ICML 2024Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
13. ICML 2024Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
14. ICML 2024LQER: Low-Rank Quantization Error Reconstruction for LLMs
15. ICML 2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
16. ICML 2024Operator SVD with Neural Networks via Nested Low-Rank Approximation
17. ICML 2024Reweighted Solutions for Weighted Low Rank Approximation
18. NeurIPS 2024Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
19. NeurIPS 2024Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
20. NeurIPS 2024Efficient LLM Scheduling by Learning to Rank
21. NeurIPS 2024Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
22. NeurIPS 2024Entrywise error bounds for low-rank approximations of kernel matrices
23. NeurIPS 2024Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
24. NeurIPS 2024Loki: Low-rank Keys for Efficient Sparse Attention
25. NeurIPS 2024Neural Embeddings Rank: Aligning 3D latent dynamics with movements
26. NeurIPS 2024SGD vs GD: Rank Deficiency in Linear Networks
27. NeurIPS 2024Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
28. NeurIPS 2024Weight decay induces low-rank attention layers

FAQ

How many Learning to Rank papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

257 in total: 142 in 2023, 28 in 2024, 32 in 2025, 55 in 2026.

How many Learning to Rank papers are at NeurIPS 2026?

NeurIPS 2026 has 18 Learning to Rank papers, versus 13 the year before.

How many Learning to Rank papers are at ICLR 2026?

ICLR 2026 has 9 Learning to Rank papers, versus 8 the year before.

How many Learning to Rank papers are at ICML 2026?

ICML 2026 has 26 Learning to Rank papers, versus 10 the year before.

How many Learning to Rank papers are at CVPR 2026?

CVPR 2026 has 2 Learning to Rank papers, versus 1 the year before.

Which conference publishes the most Learning to Rank papers?

NeurIPS, with 154 papers from 2023 to 2026.