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

257 papers, page 2 of 6

51. NeurIPS 2026RECIPE: Learning to Rank Complete Precursor Sets for Inorganic Retrosynthesis
52. NeurIPS 2026SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
53. NeurIPS 2026Stabilizing the Dynamic Low-Rank Training
54. NeurIPS 2026Towards Identifying Dominant Low-Rank Subspaces in Zeroth-Order Fine-Tuning
55. NeurIPS 2026VALOR: Vector-Aware Low-Rank Restructuring of Neural Networks for RISC-V Inference
56. CVPR 2025Breaking the Low-Rank Dilemma of Linear Attention
57. ICLR 2025Efficient Learning with Sine-Activated Low-Rank Matrices
58. ICLR 2025Ensembles of Low-Rank Expert Adapters
59. ICLR 2025Lambda-Skip Connections: the architectural component that prevents Rank Collapse
60. ICLR 2025LoLCATs: On Low-Rank Linearizing of Large Language Models
61. ICLR 2025LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
62. ICLR 2025Palu: KV-Cache Compression with Low-Rank Projection
63. ICLR 2025SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process
64. ICLR 2025SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
65. ICML 2025Approximation to Smooth Functions by Low-Rank Swish Networks
66. ICML 2025GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
67. ICML 2025Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
68. ICML 2025Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression
69. ICML 2025Low-Rank Thinning
70. ICML 2025Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion
71. ICML 2025ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
72. ICML 2025Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision
73. ICML 2025Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank
74. ICML 2025The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport
75. NeurIPS 2025A geometric framework for momentum-based optimizers for low-rank training
76. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
77. NeurIPS 2025Demystifying Language Model Forgetting with Low-rank Example Associations
78. NeurIPS 2025Efficient Low Rank Attention for Long-Context Inference in Large Language Models
79. NeurIPS 2025Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
80. NeurIPS 2025From Condensation to Rank Collapse: A Two-Stage Analysis of Transformer Training Dynamics
81. NeurIPS 2025Generalization Bounds for Rank-sparse Neural Networks
82. NeurIPS 2025LittleBit: Ultra Low-Bit Quantization via Latent Factorization
83. NeurIPS 2025Low-Rank Graphon Learning for Networks
84. NeurIPS 2025Low-Rank Head Avatar Personalization with Registers
85. NeurIPS 2025On the Role of Hidden States of Modern Hopfield Network in Transformer
86. NeurIPS 2025Perturbation Bounds for Low-Rank Inverse Approximations under Noise
87. NeurIPS 2025Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
88. CVPR 2024LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
89. CVPR 2024PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
90. CVPR 2024Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning
91. ICLR 2024LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
92. ICLR 2024Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming
93. ICML 2024Efficient Pareto Manifold Learning with Low-Rank Structure
94. ICML 2024Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
95. ICML 2024FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees
96. ICML 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressors
97. ICML 2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
98. ICML 2024High-Dimensional Geometric Streaming for Nearly Low Rank Data
99. ICML 2024Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
100. ICML 2024Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery

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.