DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data

Summary: DiffPrep introduces differentiable bi-level search for preprocessing pipelines, enabling optimization in a continuous space. Continuous relaxation enables descent to find pipelines with a single training, yielding up to 6.6pp gains on 15/18 datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6687
Venue: SIGMOD
Year: 2023
Pagerank: 5.5034427e-05
Overall Rank: 5,439 | 62.20%
DOI: 10.1145/3589328

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,739	CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning	2024	SIGMOD	4.4520434e-05
10,328	LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning	2026	VLDB	4.1905499e-05
10,764	Stress-Testing ML Pipelines with Adversarial Data Corruption	2025	VLDB	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
788	ActiveClean: Interactive Data Cleaning For Statistical Modeling	2016	VLDB	0.00016618698
917	Democratizing Data Science through Interactive Curation of ML Pipelines	2019	SIGMOD	0.00015324193
1,403	Detecting Data Errors: Where are we and what needs to be done?	2016	VLDB	0.00012180046
1,629	Data Cleaning: Overview and Emerging Challenges	2016	SIGMOD	0.00011073148
1,914	Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks	2020	SIGMOD	0.00010111859
2,308	Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions	2021	VLDB	9.0634287e-05
4,970	Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation	2022	SIGMOD	5.7900867e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
10,328	LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning	2026	VLDB	4.1905499e-05
8,253	Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines	2023	SIGMOD	4.5444167e-05
10,606	Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence	2025	VLDB	4.1905499e-05
5,965	Automatic Data Acquisition for Deep Learning	2021	VLDB	5.2476363e-05
11,553	Active Reinforcement Learning for Data Preparation: Learn2Clean with Human-In-The-Loop	2020	CIDR	4.1905499e-05
5,306	A Scalable AutoML Approach Based on Graph Neural Networks	2022	VLDB	5.5725759e-05
8,739	CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning	2024	SIGMOD	4.4520434e-05
8,828	HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation	2023	SIGMOD	4.4364918e-05
5,389	Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search	2021	VLDB	5.5339832e-05
3,694	Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines	2022	SIGMOD	6.8316905e-05