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 2 of 3

51. NeurIPS 2023Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Bone Shape Reconstruction
52. NeurIPS 2023Benchmarking Robustness to Adversarial Image Obfuscations
53. NeurIPS 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
54. NeurIPS 2023Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
55. NeurIPS 2023CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
56. NeurIPS 2023Combinatorial Group Testing with Selfish Agents
57. NeurIPS 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
58. NeurIPS 2023Context-lumpable stochastic bandits
59. NeurIPS 2023DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
60. NeurIPS 2023Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
61. NeurIPS 2023Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
62. NeurIPS 2023Does progress on ImageNet transfer to real-world datasets?
63. NeurIPS 2023DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
64. NeurIPS 2023Efficient Low-rank Backpropagation for Vision Transformer Adaptation
65. NeurIPS 2023Efficient Model-Free Exploration in Low-Rank MDPs
66. NeurIPS 2023Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation
67. NeurIPS 2023Estimating Koopman operators with sketching to provably learn large scale dynamical systems
68. NeurIPS 2023Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
69. NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
70. NeurIPS 2023Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
71. NeurIPS 2023FedL2P: Federated Learning to Personalize
72. NeurIPS 2023From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
73. NeurIPS 2023Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
74. NeurIPS 2023How many samples are needed to leverage smoothness?
75. NeurIPS 2023How to Scale Your EMA
76. NeurIPS 2023Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
77. NeurIPS 2023Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
78. NeurIPS 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
79. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
80. NeurIPS 2023Kissing to Find a Match: Efficient Low-Rank Permutation Representation
81. NeurIPS 2023Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
82. NeurIPS 2023Learning Energy-based Model via Dual-MCMC Teaching
83. NeurIPS 2023Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
84. NeurIPS 2023Learning to Augment Distributions for Out-of-distribution Detection
85. NeurIPS 2023Learning to Compress Prompts with Gist Tokens
86. NeurIPS 2023Learning to Discover Skills through Guidance
87. NeurIPS 2023Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
88. NeurIPS 2023Learning to Receive Help: Intervention-Aware Concept Embedding Models
89. NeurIPS 2023Learning to Taste: A Multimodal Wine Dataset
90. NeurIPS 2023Learning to Tokenize for Generative Retrieval
91. NeurIPS 2023Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
92. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
93. NeurIPS 2023Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
94. NeurIPS 2023Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
95. NeurIPS 2023MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
96. NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
97. NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
98. NeurIPS 2023Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
99. NeurIPS 2023On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
100. NeurIPS 2023On the Ability of Graph Neural Networks to Model Interactions Between Vertices

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.