DenoiseRank: Learning to Rank by Diffusion Models
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are implemented from discriminative perspective. In this paper, we aim at addressing LTR from a novel perspective, i.e., by a deep generative model. Specifically, we propose a novel denoise rank model, DenoiseRank, which noises the relevant labels in the diffusion process and denoises them on the query documents in the reverse process to accurately predict their distribution. Our model is the first to address traditional LTR from generative perspective and is a diffusion method for LTR. Our extensive experiments on benchmark datasets demonstrated the effectiveness of DenoiseRank, and we believe it provides a benchmark for generative LTR task.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 24 of its 60 references that are in the map. First posted on arXiv 17 Feb 2026.
Builds on
24 of 60 references.
Closest ancestors · 3
Same direction, newest first
- 2022CARD: Classification and Regression Diffusion Models15 Jun
- 2021Improved Denoising Diffusion Probabilistic Models18 Feb
- 2020Improved Techniques for Training Score-Based Generative Models16 Jun
Other references · 21
- 2017Attention Is All You Needcited by 59,932
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
- 2023Training Diffusion Models with Reinforcement Learningcited by 939
- 2013Introducing LETOR 4.0 Datasetscited by 120
- 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 91
- 2018Learning a Deep Listwise Context Model for Ranking Refinementcited by 62
- 2019SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrievalcited by 48
- 2023Learning to Rank in Generative Retrievalcited by 46
- 2024ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesiscited by 41
- 2021NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sortingcited by 39
- 2024KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniquescited by 37
- 2023Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Modelscited by 26
9 more
- 2021Wasserstein Generative Learning of Conditional Distributioncited by 24
- 2023RankFormer: Listwise Learning-to-Rank Using Listwide Labelscited by 9
- 2021RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Searchcited by 7
- 2024MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labelscited by 7
- 2024ROIC-DM: Robust Text Inference and Classification via Diffusion Modelcited by 5
- 2024DiffCLIP: Few-shot Language-driven Multimodal Classifiercited by 4
- 2024InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimizationcited by 2
- 2024MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Modelcited by 2
- 2024Generative vs. Discriminative modeling under the lens of uncertainty quantificationcited by 2
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- 2023Conditional Denoising Diffusion for Sequential Recommendation
- 2026Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
- 2022Image Embedding for Denoising Generative Models
- 2022From Denoising Diffusions to Denoising Markov Models
- 2024An overview of diffusion models for generative artificial intelligence
- 2024Denoising Task Difficulty-based Curriculum for Training Diffusion Models
- 2025Diffusion Models: A Mathematical Introduction