Awesome ML Data Quality Papers

This is a list of papers about training data quality management for ML models.

Introduction

Data scientists spend ∼80% time on data preparation for an ML pipeline since the data quality issues are unknown beforehand thereby leading to iterative debugging [1]. A good Data Quality Management System for ML (DQMS for ML) helps data scientists break free from the arduous process of data selection and debugging, particularly in the current era of big data and large models. Automating the management of training data quality effectively is crucial for improving the efficiency and quality of ML pipelines.

With the emergence and development of "Data-Centric AI", there has been increasing research focus on optimizing the quality of training data rather than solely concentrating on model structures and training strategies. This is the motivation behind maintaining this repository.

Before we proceed, let's define data quality for ML. In contrast to traditional data cleaning, training data quality for ML refers to the impact of individual or groups of data samples on the behavior of ML models for a given task. It's important to note that the behavior of the model we are concerned with goes beyond performance metrics like accuracy, recall, AUC, MSE, etc. We also consider more generalizable metrics such as model fairness, robustness, and so on.

Considering the following pipeline, DQMS acts as a middleware between data, ML model, and user, necessitating interactions with each of them.

A DQMS for ML typically consists of three components: Data Sculptor [2], Data Attributer, and Data Profiler. To achieve a well-performing ML model, multiple rounds of training are often required. In this process, the DQMS needs to iteratively adjust the training data based on the results of each round of model training. The workflow of DQMS in one round of training is as follows: (a) Data sculptor first acquires the training dataset from a data source and trains the ML model with it. (b) After training for one round (several epochs), Data Attributer absorbs feedback from the model and user's task requirements and computes the data quality assessment. (c) Data Profiler then provides a user-friendly summary of the training data. (d) Meanwhile, Data Sculptor utilizes the data quality assessment as feedback to acquire higher-quality training data, thus initiating a new iteration.

We collect the recent influential papers about DQMS for ML and annotate the relevant DQMS components involved in these papers, where DS = Data Sculptor, DA = Data Attributer, and DP = Data Profiler. The following papers are listed in roughly chronological order of publication.

Paper List

2024

Venue	Paper	Links	Tags	TLDR
	On the Inflation of KNN-Shapley Value	paper	DA
	Data Valuation for Graphs	paper	DA
	Precedence-Constrained Winter Value for Effective Graph Data Valuation	paper	DA
	Data Shapley in One Training Run	paper	DA
	Generalized Group Data Attribution	paper	DA
	Top-m Data Values Identification	paper	DA
NIPS'24	DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation	paper	DA
NIPS'24	SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning	paper	DA	It first divide the data into clusters and compute the Shapley value of clusters, and then select representative data points inside cluster.
NIPS'24	2D-OOB: Attributing Data Contribution through Joint Valuation Framework	paper code	DA
NIPS'24	Training Data Attribution via Approximate Unrolling	paper	DA
NIPS'24	Data Attribution for Text-to-Image Models by Unlearning Synthesized Images		DA
NIPS'24	Efficient Sketches for Training Data Attribution and Studying the Loss Landscape		DA
NIPS'24	MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models	paper	DS
ICDE'24	When Data Pricing Meets Non-cooperative Game Theory	paper	DA
arXiv'24	Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection	paper code	DS DA
KDD'24	EcoVal: An Efficient Data Valuation Framework for Machine Learning	paper code	DA	For the efficiency of data valuation, this work first divide the data into clusters, and compute the cluster value with LOO. It then assign the individual data value inside a cluster through produce function.
KDD'24	Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization	paper code	DS DP	This paper shows memorization score is effective for data summarization / selection tasks, and proposes to approximate memorization with SGD.
KDD'24	Scalable Rule Lists Learning with Sampling	paper code	DP	This work proposes to learn the approximately optimal rule set through sampling by preserving both accuracy and efficiency.
KDD'24	AIM: Attributing, Interpreting, Mitigating Data Unfairness	paper code	DP
KDD'24	CAT: Interpretable Concept-based Taylor Additive Models	paper code
KDD'24	Dataset Regeneration for Sequential Recommendation	paper code	DS
arXiv'24	What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions	paper	DA
arXiv'24	CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning	paper	DA
ICML'24	QuRating: Selecting High-Quality Data for Training Language Models	paper code	DS
ICML'24	Scaling Laws for the Value of Individual Data Points in Machine Learning	paper code	DA	This work proposes individual scaling law for distinguishing how the marginal contribution of a data point varies as the dataset size growing. It then proposes two methods to estimate the individual scaling law.
ICML'24	Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits	paper	DA DS	In general cases without considering the structural assumptions of utility functions, Data Shapley’s performance in data selection tasks can be no better than that of random guessing. It proposes a heuristic for predicting Data Shapley’s optimality for data selection.
ICML'24	Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach	paper	DA
ICML'24	Distributionally Robust Data Valuation	paper code	DA
ICML'24	Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions	paper code	DA	It proves that Shapley value shows better robustness compared to LOO and proposes FreeShap to estimate Shapley using eNTK without retraining.
ICML'24	Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection	paper code	DA
ICML'24	Optimal Coresets for Low-Dimensional Geometric Median	paper	DS
ICML'24	No Dimensional Sampling Coresets for Classification	paper	DS
ICML'24	Coresets for Multiple $ℓ_𝑝$ Regression	paper	DS
ICML'24	Deletion-Anticipative Data Selection with a Limited Budget	paper	DS
ICML'24	Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond	paper	DS
ICML'24	Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints	paper code	DS
ICML'24	Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary	paper	DS	This work proposes to select a coreset that maintains the decision boundary of model trained on full dataset. It measures the distance between a sample to its nearest decision boundary and selects data based on this distance.
ICML'24	DsDm: Dataset Selection with Datamodels	paper	DS	DsDm converts the data selection problem into loss minimization problem in target data. It then uses linear datamodel to approximate the loss mapping and select the bottom-k samples with smallest estimated loss.
ICML'24	BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges		DS
ICML'24	LESS: Selecting Influential Data for Targeted Instruction Tuning	paper code	DS
ICML'24	Exploiting Negative Samples: A Catalyst for Cohort Discovery in Healthcare Analytics	paper	DA DP	This work proposes to leverage data Shapley value to value each data in negative sample, and employs manifold learning and clustering to find influential patterns in negative samples.
CVPR'24	The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes	paper	DS
VLDB'24	Counterfactual Explanation of Shapley Value in Data Coalitions	paper code	DA	If the Shapley value of data owner A is higher than B, the counterfactual explanation aims to find a smallest subset of data in A that such that moving it from A to B makes Shapley value of A less than that of B. This work proposes a greedy based search to find the counterfactual.
VLDB'24	P-Shapley: Shapley Values on Probabilistic Classifiers	paper code	DA	This paper introduces P-Shapley with raw probability (instead of accuracy) as utility function and proposes calibration function to enlarge the utility change when the predicted probability is high.
VLDB'24	MetaStore: Analyzing Deep Learning Meta-Data at Scale	paper	DA DP
VLDB'24	Optimizing Data Acquisition to Enhance Machine Learning Performance	paper code	DS
VLDB'24	MisDetect: Iterative Mislabel Detection using Early Loss	paper code	DA
VLDB'24	Outlier Summarization via Human Interpretable Rules	paper code	DP	It trains a decision tree model to summarize the rule patterns of outliers.
VLDB'24	Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities	paper code	DS	It uses generative AI for augmentation, ensuring that the generated data covering the original data distribution with a smallest size.
VLDB'24	DataPrice: An Interactive System for Pricing Datasets in Data Marketplaces	paper	DA
SIGMOD'24	Rock: Cleaning Data by Embedding ML in Logic Rules	paper	DP DA	Rock implements a uniform data cleaning framework that unifies ML and logic deduction.
SIGMOD'24	Data Acquisition for Improving Model Confidence	paper	DS
SIGMOD'24	Controllable Tabular Data Synthesis Using Diffusion Models	paper	DS
SIGMOD'24	Fast Shapley Value Computation in Data Assemblage Tasks as Cooperative Simple Games	paper code	DA	It assigns a Shapley score for data owners and their corresponding datasets in data market.
WWW'24	Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale Graphs	paper code	DS	This work selects training nodes that are similar to test nodes by minimizing their bottleneck distance. To avoid bias caused by trivial selection, it uses a greedy alg. to assure the representativeness of selected nodes.
AAAI'24	DeRDaVa: Deletion-Robust Data Valuation for Machine Learning	paper	DA
AAAI'24	Quality-Diversity Generative Sampling for Learning with Synthetic Data	paper code	DS
AAAI'24	Approximating the Shapley Value without Marginal Contributions	paper	DA	It transfer Shapley value by $\phi_i = \phi_i^+ + \phi_i^-$. It samples coalitions and update $\phi_i^+$ and $\phi_i^-$ separately.
WSDM'24	FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes	paper	DA
WSDM'24	Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function	paper code	DA
ICLR'24	"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection	paper code	DS DA	It extends influence function considering utility, fairness and robustness. It trains a decision tree to further estimate and interpret the influence score.
ICLR'24	Canonpipe: Data Debugging with Shapley Importance over Machine Learning Pipelines	paper code	DA	It explores data valuation on raw data before preprocessing. It uses data provenance in ML pipelines and proposes data Shapley under a KNN approximation.
ICLR'24	Time Travel in LLMs: Tracing Data Contamination in Large Language Models	paper code	DA	Data contamination means the presence of test data from downstream tasks in the pre-training data of LLMs. This work explore both instance and partition level methods to identify potential contamination.
ICLR'24	GIO: Gradient Information Optimization for Training Dataset Selection	paper code	DA	GIO selects a small subset of data from large source data by minimizing the KL divergence between the target distribution and subset.
ICLR'24	Intriguing Properties of Data Attribution on Diffusion Models	paper code	DA	This paper proposes D-TRAK to attribute images generated by diffusion models back to the training data.
ICLR'24	D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning	paper code	DS	A data pruning method that takes diversity into consideration. It is implemented by forward and reverse message passing in the KNN graph.
ICLR'24	Effective pruning of web-scale datasets based on complexity of concept clusters	paper code	DS
ICLR'24	Towards a statistical theory of data selection under weak supervision	paper	DS
ICLR'24	Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs	paper code	DS
ICLR'24	DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models	paper code	DA	DataInf approximate influence function by swapping the order of the matrix inversion and average calculation.
ICLR'24	What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning	paper	DS
ICLR'24	Real-Fake: Effective Training Data Synthesis Through Distribution Matching	paper code	DS
ICLR'24	InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning	paper code	DS	InfoBatch uses training loss to prune well-learned samples in each epoch and estimate gradient distribution for unbiased learning.
arXiv'24	A Decade's Battle on Dataset Bias: Are We There Yet?	paper code	DS
arXiv'24	On the Cause of Unfairness: A Training Sample Perspective	paper	DA	The fairness influence can be computed by replacing the training sample with its concept counterfactual sample.

2023

Venue	Paper	Links	Tags	TLDR
arXiv'23	Accelerated Shapley Value Approximation for Data Evaluation	paper	DA	Not all coalition sizes are evaluated, small coalitions may introduce noise and large ones may have little contributions. To estimate the effect of coalitions with size k, about O(1 / k^2) sample coalitions is sufficient.
arXiv'23	The Journey, Not the Destination: How Data Guides Diffusion Models	paper code	DA	-
NIPS'23	The Memory Perturbation Equation: Understanding Model’s Sensitivity to Data	paper code	DA DP	-
NIPS'23	Theoretical and Practical Perspectives on what Influence Functions Do	paper	DA	This work discusses some problematic assumptions of IF. While most of them can be addressed, IF can predict perturbated param accurately for a limited amount of time-steps.
NIPS'23	Data Selection for Language Models via Importance Resampling	paper code	DS DA	It selects data satisfying a target distribution from raw data by reducing KL divergence to the target over random selection.
NIPS'23	Model Shapley: Equitable Model Valuation with Black-box Access	paper code	DA	-
NIPS'23	Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation	paper	DA	Extend KNN-Shapley while considering data privacy.
NIPS'23	GEX: A flexible method for approximating influence via Geometric Ensemble	paper code	DA	-
NIPS'23	Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks	paper code	DS	-
NIPS'23	Data Pruning via Moving-one-Sample-out	paper	DS	This work proposes a Moso score (similar to LOO) and an approximates it using gradient over all training epochs.
NIPS'23	Towards Free Data Selection with General-Purpose Models	paper code	DS	-
NIPS'23	Towards Accelerated Model Training via Bayesian Data Selection	paper	DS	-
NIPS'23	Robust Data Valuation with Weighted Banzhaf Values	paper	DA	-
NIPS'23	UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models	paper	DS	-
NIPS'23	Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources	paper code	DS	Given publicly known pilot data from different data sources, it returns the optimal combination of data sources.
NIPS'23	Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy	paper code	DS	-
NIPS'23	Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases	paper	DS	-
NIPS'23	Core-sets for Fair and Diverse Data Summarization	paper code	DS DP	It selects a fixed size of coreset for different groups of data while preserving diversity.
NIPS'23	Retaining Beneficial Information from Detrimental Data for Neural Network Repair	paper	DS	-
NIPS'23	Expanding Small-Scale Datasets with Guided Imagination	paper code	DS	-
NIPS'23	Error Discovery By Clustering Influence Embeddings	paper code	DA	This work cluster influence embedding (a low dimension of influence vector of training samples) for all test samples to summarize the prediction error.
NIPS'23	HiBug: On Human-Interpretable Model Debug	paper code	DP DS	-
NIPS'23	Skill-it! A data-driven skills framework for understanding and training language models	paper code	DP DS	-
ICML'23	Discover and Cure: Concept-aware Mitigation of Spurious Correlation	paper code	DS DA	Discover spurious correlation from concept level and perform concept-based data augmentation to mitigate bias.
ICML'23	TRAK: Attributing Model Behavior at Scale	paper code	DA	TRAK first defines a Newton approximation to estimate LOO for logistic regression and then extends it to NNs (including CLIP, mT5) by view them as the linear model acting on input gradient.
ICML'23	RGE: A Repulsive Graph Rectification for Node Classification via Influence	paper code	DA	RGE identifies a group of negative edges that are most harmful for GNNs. It iteratively selects negative edges by their individual influence and prefers distant edges first.
ICML'23	Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value	paper code	DA	Data-OOB measures the average score when a datum (OOB data) is not selected in the bootstrap dataset.
ICML'23	2D-Shapley: A Framework for Fragmented Data Valuation	paper	DA
ICML'23	Towards Sustainable Learning: Coresets for Data-efficient Deep Learning	paper code	DS	-
ICML'23 Workshop	Training on Thin Air: Improve Image Classification with Generated Data	paper	DS	-
ICML'23 Workshop	Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation	paper code	DA DS	-
VLDB'23	Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets	paper code	DA	-
VLDB'23	Computing Rule-Based Explanations by Leveraging Counterfactuals	paper code	DP	-
VLDB'23	Data Collection and Quality Challenges for Deep Learning	paper	DS DA	-
SIGMOD'23	GoodCore: Coreset Selection over Incomplete Data for Data-effective and Data-efficient Machine Learning	paper	DS	GoodCore selects a coreset that achieves expected low gradient approximation error among all possible worlds of missing data.
SIGMOD'23	XInsight: eXplainable Data Analysis Through The Lens of Causality	paper	DP	-
SIGMOD'23	HybridPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation	paper code	DS DP	-
SIGMOD'23	Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications	paper
ACL'23	Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values	paper	DA
arXiv'23	Simfluence: Modeling the influence of individual training examples by simulating training runs	paper	DS	Trains a simulator that generates a time series that predicts what the loss on $z_{test}$ would be after each step of the training run (a loss trajectory).
ICLR'23	Data Valuation Without Training of a Model	paper code	DA	It proposes a score to measures the gap in data complexity where a certain data instance is removed from the full dataset.
ICLR'23	Distilling Model Failures as Directions in Latent Space	paper code	DS DP	-
ICLR'23	LAVA: Data Valuation without Pre-Specified Learning Algorithms	paper code	DA	LAVA uses a Wasserstein distance to estimate the upper bound of test performance. It values a training sample by its sensitivity to the distance.
ICLR'23	Concept-level Debugging of Part-Prototype Networks	paper code	DP	-
ICLR'23	Dataset Pruning: Reducing Training Data by Examining Generalization Influence	paper	DS	-
ICLR'23	Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning	paper code	DS	-
ICLR'23	Learning to Estimate Shapley Values with Vision Transformers	paper code	DA	-
ICLR'23	Characterizing the Influence of Graph Elements	paper code	DA	Introduce influence function into graphs, considering node- and edge-removal influence and the linear SGC model.
ICLR'23	Dataset pruning: Reducing training data by examining generalization influence.	paper	DA
ICDE'23	Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise	paper code	DP	-
ICDE'23	Detection of Groups with Biased Representation in Ranking	paper	DA	-
AAAI'23	Fundamentals of Task-Agnostic Data Valuation	paper	DA	-
AAAI'23	Interpreting Unfairness in Graph Neural Networks via Training Node Attribution	paper code	DA	This work proposes a Probabilistic Distribution Disparity to define node-contributed model bias and use gradient approximation to estimate node-level bias.
AAAI'23	Interpreting Unfairness in Graph Neural Networks via Training Node Attribution	paper code	DA
WWW'23	GIF: A General Graph Unlearning Strategy via Influence Function	paper code	DA	GIF extends influence function to graph data by considering both the directly affected node(s) and the influenced neighborhoods.
AISTATS'23	Data Banzhaf: A Robust Data Valuation Framework for Machine Learning	paper	DA	-
arXiv'23	Data-Juicer: A One-Stop Data Processing System for Large Language Models	paper code	DS DP	-
arXiv'23	Simfluence: Modeling the influence of individual training examples by simulating training runs	paper	DA
arXiv'23	Studying Large Language Model Generalization with Influence Functions	paper	DA	-
TMLR'23	Synthetic Data from Diffusion Models Improves ImageNet Classification	paper	DS	-

2022

Venue	Paper	Links	Tags	TLDR
NIPS'22	CS-SHAPLEY: Class-wise Shapley Values for Data Valuation in Classification	paper code	DA
NIPS'22	Beyond neural scaling laws: beating power law scaling via data pruning	paper	DS
NIPS'22	Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP	paper code	DS
NIPS'22	Quantifying memorization across neural language models	paper	DA
ICML'22	Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments	paper	DA	It proposes the AME score $E_S[U(S\cup {z})-U(S)]$ with $S$ being a random set. The AME score can be approximated by a LASSO model.
ICML'22	Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations	papeer code	DS DP	It learns CAV and move those misclassified training samples toward the direction of CAV.
ICML'22	Datamodels: Predicting Predictions from Training Data	paper code	DA	Datamodels learns a linear model to predict the model output on one test data. It takes as input the one-hot mask of training samples.
ICML'22	Prioritized Training on Points that are learnable, Worth Learning, and Not Yet Learnt	paper code	DS
ICML'22	Achieving Fairness at No Utility Cost via Data Reweighing with Influence	paper code	DA	It employs DP and EOP to compute IF and performs soft reweighing on training samples. The proof of no-utility-degradation is provided.
ICML'22	DAVINZ: Data Valuation using Deep Neural Networks at Initialization	paper	DA	It uses NTK-based bound to approximate validation performance without training.
ICML'22	Understanding Instance-Level Impact of Fairness Constraint	paper code	DA	IF = IF of loss + IF of fairness constraint. It considers several constraints including DP, EOP, covariance, information, etc. and uses NTK to estimate IF.
ICSE'22	Training data debugging for the fairness of machine learning software	paper code	DS
ICLR'22	Domino: Discovering systematic errors with cross-modal embeddings	paper code	DA DP
ICLR'22	Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning	paper	DA
VLDB'22	Toward Interpretable and Actionable Data Analysis with Explanations and Causality	paper	DP
SIGMOD'22	Complaint-Driven Training Data Debugging at Interactive Speeds	paper	DA
SIGMOD'22	Interpretable Data-Based Explanations for Fairness Debugging	paper video	DA DP
ACL'22	Deduplicating training data makes language models better	paper code	DS
AAAI'22	Scaling Up Influence Functions	paper code	DA
AISTATS'22	Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning	paper code	DA

2021 and before

Venue	Paper	Links	Tags	TLDR
NIPS'21	Explaining Latent Representations with a Corpus of Examples	paper code	DA
NIPS'21	Validation free and replication robust volume-based data valuation	paper code	DA
NIPS'21	Deep Learning on a Data Diet: Finding Important Examples Early in Training	paper	DS
NIPS21	Interactive Label Cleaning with Example-based Explanations	paper code	DP
ICML'21	GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training	paper code	DS
CVPR'21	Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?	paper code	DA
CHI'21	Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency	paper	DP
NIPS'20	Multi-Stage Influence Function	paper	DA
NIPS'20	Estimating Training Data Influence by Tracing Gradient Descent	paper code	DA	TracIn measures the influence of training batched samples during training by estimating the test loss change w.r.t. earlier epochs.
ICML'20	On second-order group influence functions for black-box predictions	paper	DA	The influence score of a group = the sum of individual influence per sample + cross-dependencies among samples in the group.
ICML'20	Coresets for data-efficient training of machine learning models	paper code	DS
ICML'20	Optimizing Data Usage via Differentiable Rewards	paper	DS
ICML'20	Data Valuation using Reinforcement Learning	paper code	DA	DVRL employs a learnable NN as data value estimator to select data samples during training and use a RL signal to update it.
ICLR'20	Selection via proxy: Efficient data selection for deep learning	paper code	DS
SIGMOD'20	Complaint Driven Training Data Debugging for Query'2.0	paper video	DA
PMLR'20	Identifying Statistical Bias in Dataset Replication	paper code
NIPS'19	Data Cleansing for Models Trained with SGD	paper code	DA	The proposed SGD-Influence scales the influence estimation into SGD-base NNs without the convex and optimal assumptions.
ICML'19	Data Shapley: Equitable Valuation of Data for Machine Learning	paper code	DA
VLDB'19	Efficient task-specific data valuation for nearest neighbor algorithms	paper	DA
AISTATS'19	Towards Efficient Data Valuation Based on the Shapley Value	paper	DA
ICML'17	Understanding Black-box Predictions via Influence Functions	paper code	DA

Surveys

Venue	Paper	Links	Tags
arXiv'24	A Survey on Data Selection for Language Models	paper	DS
Nature Machine Intelligence'22	Advances, challenges and opportunities in creating data for trustworthy AI	paper	DS DA
arXiv'23	Data-centric Artificial Intelligence: A Survey	paper	DS DA DP
arXiv'23	Data Management For Large Language Models: A Survey	paper code	DS DA
arXiv'23	Training Data Influence Analysis and Estimation: A Survey	paper code	DA
TKDE'22	Data Management for Machine Learning: A Survey	paper	DS DA
IJCAI'21	Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges	paper	DA
TACL'21	Explanation-Based Human Debugging of NLP Models: A Survey	paper	DP DA

Benchmarks

Venue	Paper	Links	Tags
NIPS'23	DataPerf: Benchmarks for Data-Centric AI Development	paper code website	DS DA DP
NIPS'23	OpenDataVal: a Unified Benchmark for Data Valuation	paper code	DA
NIPS'23	Improving multimodal datasets with image captioning	paper code	DS
NIPS'23	Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias	paper code	DS
DEEM'22	dcbench: A Benchmark for Data-Centric AI Systems	paper code	DS

Related Workshops and Tutorials

1. [ICML'23] DMLR Workshop: Data-centric Machine Learning Research video DMLR Website
2. [NIPS'23] Tutorial: Data Contribution Estimation for Machine Learning Website

Related Repos

1. More papers about Data Valuation can be found in awesome-data-valuation. DA
2. More papers about Data Pruning can be found in Awesome-Coreset-Selection. DS

Reference

[1] Gupta, Nitin, et al. "Data quality for machine learning tasks." Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021.

[2] Liang, Weixin, et al. "Advances, challenges and opportunities in creating data for trustworthy AI." Nature Machine Intelligence 4.8 (2022): 669-677.