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 3 of 3

101. NeurIPS 2023On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
102. NeurIPS 2023On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
103. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
104. NeurIPS 2023Partial Matrix Completion
105. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
106. NeurIPS 2023Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
107. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
108. NeurIPS 2023Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
109. NeurIPS 2023Quantifying the Cost of Learning in Queueing Systems
110. NeurIPS 2023Rank-DETR for High Quality Object Detection
111. NeurIPS 2023Rank-N-Contrast: Learning Continuous Representations for Regression
112. NeurIPS 2023Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
113. NeurIPS 2023Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
114. NeurIPS 2023Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
115. NeurIPS 2023RRHF: Rank Responses to Align Language Models with Human Feedback
116. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
117. NeurIPS 2023Score-based Source Separation with Applications to Digital Communication Signals
118. NeurIPS 2023Sharp Spectral Rates for Koopman Operator Learning
119. NeurIPS 2023Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
120. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
121. NeurIPS 2023Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
122. NeurIPS 2023Stochastic Approximation Approaches to Group Distributionally Robust Optimization
123. NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
124. NeurIPS 2023Switching Autoregressive Low-rank Tensor Models
125. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
126. NeurIPS 2023The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data
127. NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
128. NeurIPS 2023The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
129. NeurIPS 2023The s-value: evaluating stability with respect to distributional shifts
130. NeurIPS 2023Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
131. NeurIPS 2023To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
132. NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
133. NeurIPS 2023Topological Obstructions and How to Avoid Them
134. NeurIPS 2023Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
135. NeurIPS 2023Towards Higher Ranks via Adversarial Weight Pruning
136. NeurIPS 2023Training neural operators to preserve invariant measures of chaotic attractors
137. NeurIPS 2023Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks
138. NeurIPS 2023Transformers learn through gradual rank increase
139. NeurIPS 2023Transformers learn to implement preconditioned gradient descent for in-context learning
140. NeurIPS 2023Two-Stage Learning to Defer with Multiple Experts
141. NeurIPS 2023Unbalanced Low-rank Optimal Transport Solvers
142. NeurIPS 2023Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning

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.