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 3 of 6

101. ICML 2024LQER: Low-Rank Quantization Error Reconstruction for LLMs
102. ICML 2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
103. ICML 2024Operator SVD with Neural Networks via Nested Low-Rank Approximation
104. ICML 2024Reweighted Solutions for Weighted Low Rank Approximation
105. NeurIPS 2024Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
106. NeurIPS 2024Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
107. NeurIPS 2024Efficient LLM Scheduling by Learning to Rank
108. NeurIPS 2024Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
109. NeurIPS 2024Entrywise error bounds for low-rank approximations of kernel matrices
110. NeurIPS 2024Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
111. NeurIPS 2024Loki: Low-rank Keys for Efficient Sparse Attention
112. NeurIPS 2024Neural Embeddings Rank: Aligning 3D latent dynamics with movements
113. NeurIPS 2024SGD vs GD: Rank Deficiency in Linear Networks
114. NeurIPS 2024Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
115. NeurIPS 2024Weight decay induces low-rank attention layers
116. CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
117. ICLR 2023Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
118. ICLR 2023Learning the Positions in CountSketch
119. ICLR 2023Spatio-temporal point processes with deep non-stationary kernels
120. ICLR 2023Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams.
121. ICML 2023A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
122. ICML 2023Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
123. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
124. ICML 2023CLIPood: Generalizing CLIP to Out-of-Distributions
125. ICML 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
126. ICML 2023Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation
127. ICML 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
128. ICML 2023From Adaptive Query Release to Machine Unlearning
129. ICML 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
130. ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
131. ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
132. ICML 2023LEVER: Learning to Verify Language-to-Code Generation with Execution
133. ICML 2023MALTS: Matching After Learning to Stretch
134. ICML 2023Multi-User Reinforcement Learning with Low Rank Rewards
135. ICML 2023OCD: Learning to Overfit with Conditional Diffusion Models
136. ICML 2023Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
137. ICML 2023Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
138. ICML 2023Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
139. ICML 2023The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
140. ICML 2023Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming
141. ICML 2023Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations
142. ICML 2023Towards Learning to Imitate from a Single Video Demonstration
143. ICML 2023VA-learning as a more efficient alternative to Q-learning
144. ICML 2023VectorMapNet: End-to-end Vectorized HD Map Learning
145. ICML 2023Which Tricks are Important for Learning to Rank?
146. NeurIPS 2023A Batch-to-Online Transformation under Random-Order Model
147. NeurIPS 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
148. NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
149. NeurIPS 2023A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment
150. NeurIPS 2023A Path to Simpler Models Starts With Noise

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.