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

26 papers, page 1 of 1

1. ICML 2026AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
2. ICML 2026Approximating f -Divergences with Rank Statistics
3. ICML 2026Breaking the Computational Barrier: Provably Efficient Actor–Critic for Low-Rank MDPs
4. ICML 2026Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
5. ICML 2026De-attribute to Forget for LLM Unlearning
6. ICML 2026Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
7. ICML 2026Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training
8. ICML 2026From Drift to Coherence: Stabilizing Beliefs in LLMs
9. ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
10. ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
11. ICML 2026Learning to Correct: Reinforcement Learning for Multi-Attempt Chain-of-Thought
12. ICML 2026Learning to Evict from Key-Value Cache
13. ICML 2026Learning to Rank by Directly Optimizing Full-Order Probabilities
14. ICML 2026Learning to Rank from Incomplete Rankings
15. ICML 2026Learning to Route Languages for Multilingual Policy Optimization
16. ICML 2026Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
17. ICML 2026Mixtures Closest To A Given Measure: A Semidefinite Programming Approach
18. ICML 2026On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models
19. ICML 2026Refining Dual Spectral Sparsity in Transformed Tensor Singular Values
20. ICML 2026Rethinking Federated Prompt Learning for Medical Images: From Textual Tuning to Visual Manifold Anchoring
21. ICML 2026Riemannian Networks over Full-Rank Correlation Matrices
22. ICML 2026Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
23. ICML 2026Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
24. ICML 2026Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression
25. ICML 2026The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
26. ICML 2026Transport Clustering: Solving Low-Rank Optimal Transport via Clustering

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.