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 2 of 3

51. NeurIPS 2023Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
52. NeurIPS 2023Learning Energy-based Model via Dual-MCMC Teaching
53. NeurIPS 2023Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
54. NeurIPS 2023Learning to Augment Distributions for Out-of-distribution Detection
55. NeurIPS 2023Learning to Compress Prompts with Gist Tokens
56. NeurIPS 2023Learning to Discover Skills through Guidance
57. NeurIPS 2023Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
58. NeurIPS 2023Learning to Receive Help: Intervention-Aware Concept Embedding Models
59. NeurIPS 2023Learning to Taste: A Multimodal Wine Dataset
60. NeurIPS 2023Learning to Tokenize for Generative Retrieval
61. NeurIPS 2023Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
62. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
63. NeurIPS 2023Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
64. NeurIPS 2023Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
65. NeurIPS 2023MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
66. NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
67. NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
68. NeurIPS 2023Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
69. NeurIPS 2023On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
70. NeurIPS 2023On the Ability of Graph Neural Networks to Model Interactions Between Vertices
71. NeurIPS 2023On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
72. NeurIPS 2023On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
73. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
74. NeurIPS 2023Partial Matrix Completion
75. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
76. NeurIPS 2023Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
77. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
78. NeurIPS 2023Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
79. NeurIPS 2023Quantifying the Cost of Learning in Queueing Systems
80. NeurIPS 2023Rank-DETR for High Quality Object Detection
81. NeurIPS 2023Rank-N-Contrast: Learning Continuous Representations for Regression
82. NeurIPS 2023Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
83. NeurIPS 2023Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
84. NeurIPS 2023Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
85. NeurIPS 2023RRHF: Rank Responses to Align Language Models with Human Feedback
86. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
87. NeurIPS 2023Score-based Source Separation with Applications to Digital Communication Signals
88. NeurIPS 2023Sharp Spectral Rates for Koopman Operator Learning
89. NeurIPS 2023Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
90. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
91. NeurIPS 2023Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
92. NeurIPS 2023Stochastic Approximation Approaches to Group Distributionally Robust Optimization
93. NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
94. NeurIPS 2023Switching Autoregressive Low-rank Tensor Models
95. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
96. NeurIPS 2023The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data
97. NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
98. NeurIPS 2023The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
99. NeurIPS 2023The s-value: evaluating stability with respect to distributional shifts
100. NeurIPS 2023Thought Cloning: Learning to Think while Acting by Imitating Human Thinking

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.