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

142 papers, page 1 of 3

1. CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
2. ICLR 2023Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
3. ICLR 2023Learning the Positions in CountSketch
4. ICLR 2023Spatio-temporal point processes with deep non-stationary kernels
5. ICLR 2023Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams.
6. ICML 2023A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
7. ICML 2023Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
8. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
9. ICML 2023CLIPood: Generalizing CLIP to Out-of-Distributions
10. ICML 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
11. ICML 2023Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation
12. ICML 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
13. ICML 2023From Adaptive Query Release to Machine Unlearning
14. ICML 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
15. ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
16. ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
17. ICML 2023LEVER: Learning to Verify Language-to-Code Generation with Execution
18. ICML 2023MALTS: Matching After Learning to Stretch
19. ICML 2023Multi-User Reinforcement Learning with Low Rank Rewards
20. ICML 2023OCD: Learning to Overfit with Conditional Diffusion Models
21. ICML 2023Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
22. ICML 2023Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
23. ICML 2023Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
24. ICML 2023The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
25. ICML 2023Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming
26. ICML 2023Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations
27. ICML 2023Towards Learning to Imitate from a Single Video Demonstration
28. ICML 2023VA-learning as a more efficient alternative to Q-learning
29. ICML 2023VectorMapNet: End-to-end Vectorized HD Map Learning
30. ICML 2023Which Tricks are Important for Learning to Rank?
31. NeurIPS 2023A Batch-to-Online Transformation under Random-Order Model
32. NeurIPS 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
33. NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
34. NeurIPS 2023A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment
35. NeurIPS 2023A Path to Simpler Models Starts With Noise
36. NeurIPS 2023A Randomized Approach to Tight Privacy Accounting
37. NeurIPS 2023A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
38. NeurIPS 2023A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
39. NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
40. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
41. NeurIPS 2023A Unified Framework for Rank-based Loss Minimization
42. NeurIPS 2023A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
43. NeurIPS 2023Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
44. NeurIPS 2023Adversarial Attacks on Online Learning to Rank with Click Feedback
45. NeurIPS 2023Adversarially Robust Learning with Uncertain Perturbation Sets
46. NeurIPS 2023Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
47. NeurIPS 2023An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
48. NeurIPS 2023An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
49. NeurIPS 2023ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training
50. NeurIPS 2023Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors

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.