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

55 papers, page 1 of 2

1. CVPR 2026Low-Rank Residual Diffusion Models
2. CVPR 2026LRDUN A Low-Rank Deep Unfolding Network for Efficient Spectral Compressive
3. ICLR 2026Consistent Low-Rank Approximation
4. ICLR 2026Designing Rules to Pick a Rule: Aggregation by Consistency
5. ICLR 2026GlowQ: Group-Shared LOw-Rank Approximation for Quantized LLMs
6. ICLR 2026Revisiting Weight Regularization for Low-Rank Continual Learning
7. ICLR 2026Sequences of Logits Reveal the Low Rank Structure of Language Models
8. ICLR 2026SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization
9. ICLR 2026Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
10. ICLR 2026The Power of Small Initialization in Noisy Low-Tubal-Rank Tensor Recovery
11. ICLR 2026Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
12. ICML 2026AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
13. ICML 2026Approximating f -Divergences with Rank Statistics
14. ICML 2026Breaking the Computational Barrier: Provably Efficient Actor–Critic for Low-Rank MDPs
15. ICML 2026Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
16. ICML 2026De-attribute to Forget for LLM Unlearning
17. ICML 2026Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
18. ICML 2026Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training
19. ICML 2026From Drift to Coherence: Stabilizing Beliefs in LLMs
20. ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
21. ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
22. ICML 2026Learning to Correct: Reinforcement Learning for Multi-Attempt Chain-of-Thought
23. ICML 2026Learning to Evict from Key-Value Cache
24. ICML 2026Learning to Rank by Directly Optimizing Full-Order Probabilities
25. ICML 2026Learning to Rank from Incomplete Rankings
26. ICML 2026Learning to Route Languages for Multilingual Policy Optimization
27. ICML 2026Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
28. ICML 2026Mixtures Closest To A Given Measure: A Semidefinite Programming Approach
29. ICML 2026On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models
30. ICML 2026Refining Dual Spectral Sparsity in Transformed Tensor Singular Values
31. ICML 2026Rethinking Federated Prompt Learning for Medical Images: From Textual Tuning to Visual Manifold Anchoring
32. ICML 2026Riemannian Networks over Full-Rank Correlation Matrices
33. ICML 2026Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
34. ICML 2026Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
35. ICML 2026Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression
36. ICML 2026The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
37. ICML 2026Transport Clustering: Solving Low-Rank Optimal Transport via Clustering
38. NeurIPS 2026A Sparse Low-Rank Biclique Decomposition for Graphs
39. NeurIPS 2026Asymmetric Factorization for Low-Rank PSD Learning: When Is the Relaxation Exact ?
40. NeurIPS 2026BiLoCo: Binary Low-Rank Corrections for LLM FP4 Decode
41. NeurIPS 2026BQ-LoRA: Binary-Quantized Low-Rank Adapters as Implicit Regularizers for Parameter-Efficient Fine-Tuning
42. NeurIPS 2026Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
43. NeurIPS 2026Learnable Low-Rank Polynomial Sketch for Effective Linear Attention
44. NeurIPS 2026Learning from Ranking Feedback: Improved Regret Bounds via Independence Preserving Rank Breaking
45. NeurIPS 2026Learning Reveals Invisible Structure in Low-Rank RNNs
46. NeurIPS 2026Locking Pretrained Weights via Deep Low-Rank Residual Distillation
47. NeurIPS 2026Matrix Recovery Via Symmetric Rank-one Measurements With Random Unit-modulus Vectors
48. NeurIPS 2026Overcoming Rank Collapse in Feedback Alignment
49. NeurIPS 2026Provably Efficient Representation Learning for Low-Rank CMDPs
50. NeurIPS 2026RankVQ: Low-Rank Parameterized Commutative Vector Quantization for KV Cache Compression

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.