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

112 papers, page 1 of 3

1. NeurIPS 2023A Batch-to-Online Transformation under Random-Order Model
2. NeurIPS 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
3. NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
4. NeurIPS 2023A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment
5. NeurIPS 2023A Path to Simpler Models Starts With Noise
6. NeurIPS 2023A Randomized Approach to Tight Privacy Accounting
7. NeurIPS 2023A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
8. NeurIPS 2023A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
9. NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
10. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
11. NeurIPS 2023A Unified Framework for Rank-based Loss Minimization
12. NeurIPS 2023A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
13. NeurIPS 2023Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
14. NeurIPS 2023Adversarial Attacks on Online Learning to Rank with Click Feedback
15. NeurIPS 2023Adversarially Robust Learning with Uncertain Perturbation Sets
16. NeurIPS 2023Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
17. NeurIPS 2023An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
18. NeurIPS 2023An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
19. NeurIPS 2023ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training
20. NeurIPS 2023Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
21. NeurIPS 2023Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Bone Shape Reconstruction
22. NeurIPS 2023Benchmarking Robustness to Adversarial Image Obfuscations
23. NeurIPS 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
24. NeurIPS 2023Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
25. NeurIPS 2023CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
26. NeurIPS 2023Combinatorial Group Testing with Selfish Agents
27. NeurIPS 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
28. NeurIPS 2023Context-lumpable stochastic bandits
29. NeurIPS 2023DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
30. NeurIPS 2023Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
31. NeurIPS 2023Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
32. NeurIPS 2023Does progress on ImageNet transfer to real-world datasets?
33. NeurIPS 2023DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
34. NeurIPS 2023Efficient Low-rank Backpropagation for Vision Transformer Adaptation
35. NeurIPS 2023Efficient Model-Free Exploration in Low-Rank MDPs
36. NeurIPS 2023Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation
37. NeurIPS 2023Estimating Koopman operators with sketching to provably learn large scale dynamical systems
38. NeurIPS 2023Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
39. NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
40. NeurIPS 2023Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
41. NeurIPS 2023FedL2P: Federated Learning to Personalize
42. NeurIPS 2023From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
43. NeurIPS 2023Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
44. NeurIPS 2023How many samples are needed to leverage smoothness?
45. NeurIPS 2023How to Scale Your EMA
46. NeurIPS 2023Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
47. NeurIPS 2023Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
48. NeurIPS 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
49. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
50. NeurIPS 2023Kissing to Find a Match: Efficient Low-Rank Permutation Representation

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.