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 5 of 6

201. NeurIPS 2023Learning to Discover Skills through Guidance
202. NeurIPS 2023Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
203. NeurIPS 2023Learning to Receive Help: Intervention-Aware Concept Embedding Models
204. NeurIPS 2023Learning to Taste: A Multimodal Wine Dataset
205. NeurIPS 2023Learning to Tokenize for Generative Retrieval
206. NeurIPS 2023Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
207. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
208. NeurIPS 2023Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
209. NeurIPS 2023Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
210. NeurIPS 2023MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
211. NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
212. NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
213. NeurIPS 2023Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
214. NeurIPS 2023On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
215. NeurIPS 2023On the Ability of Graph Neural Networks to Model Interactions Between Vertices
216. NeurIPS 2023On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
217. NeurIPS 2023On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
218. NeurIPS 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
219. NeurIPS 2023Partial Matrix Completion
220. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
221. NeurIPS 2023Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
222. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
223. NeurIPS 2023Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
224. NeurIPS 2023Quantifying the Cost of Learning in Queueing Systems
225. NeurIPS 2023Rank-DETR for High Quality Object Detection
226. NeurIPS 2023Rank-N-Contrast: Learning Continuous Representations for Regression
227. NeurIPS 2023Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
228. NeurIPS 2023Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
229. NeurIPS 2023Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
230. NeurIPS 2023RRHF: Rank Responses to Align Language Models with Human Feedback
231. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
232. NeurIPS 2023Score-based Source Separation with Applications to Digital Communication Signals
233. NeurIPS 2023Sharp Spectral Rates for Koopman Operator Learning
234. NeurIPS 2023Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
235. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
236. NeurIPS 2023Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
237. NeurIPS 2023Stochastic Approximation Approaches to Group Distributionally Robust Optimization
238. NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
239. NeurIPS 2023Switching Autoregressive Low-rank Tensor Models
240. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
241. NeurIPS 2023The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data
242. NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
243. NeurIPS 2023The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
244. NeurIPS 2023The s-value: evaluating stability with respect to distributional shifts
245. NeurIPS 2023Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
246. NeurIPS 2023To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
247. NeurIPS 2023Toolformer: Language Models Can Teach Themselves to Use Tools
248. NeurIPS 2023Topological Obstructions and How to Avoid Them
249. NeurIPS 2023Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
250. NeurIPS 2023Towards Higher Ranks via Adversarial Weight Pruning

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.