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2004.05722
Cited By
Complaint-driven Training Data Debugging for Query 2.0
12 April 2020
Weiyuan Wu
Lampros Flokas
Eugene Wu
Jiannan Wang
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Papers citing
"Complaint-driven Training Data Debugging for Query 2.0"
15 / 15 papers shown
Data Debugging is NP-hard for Classifiers Trained with SGD
International Computing and Combinatorics Conference (COCOON), 2024
Zizheng Guo
Pengyu Chen
Yanzhang Fu
Xuelong Li
307
1
0
02 Aug 2024
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Stefan Grafberger
Paul Groth
Sebastian Schelter
159
4
0
30 Apr 2024
Example-based Explanations for Random Forests using Machine Unlearning
Tanmay Surve
Romila Pradhan
FaML
FAtt
326
5
0
07 Feb 2024
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
Hideaki Takahashi
SILM
361
2
0
29 Dec 2023
MaskSearch: Querying Image Masks at Scale
IEEE International Conference on Data Engineering (ICDE), 2023
Dong He
Jieyu Zhang
Maureen Daum
Alexander Ratner
Magdalena Balazinska
VLM
291
2
0
03 May 2023
XInsight: eXplainable Data Analysis Through The Lens of Causality
Pingchuan Ma
Rui Ding
Shuai Wang
Shi Han
Dongmei Zhang
CML
493
27
0
26 Jul 2022
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
International Conference on Machine Learning (ICML), 2022
Jinkun Lin
Anqi Zhang
Mathias Lécuyer
Jinyang Li
Aurojit Panda
S. Sen
TDI
FedML
264
70
0
20 Jun 2022
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlavs
David Dao
Matteo Interlandi
Yue Liu
Sebastian Schelter
Wentao Wu
Ce Zhang
TDI
269
30
0
23 Apr 2022
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Proceedings of the VLDB Endowment (PVLDB), 2022
Cédric Renggli
Xiaozhe Yao
Luka Kolar
Luka Rimanic
Ana Klimovic
Ce Zhang
OOD
385
7
0
04 Apr 2022
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Proceedings of the VLDB Endowment (PVLDB), 2022
Yan Zhao
Yu Shen
Huaijun Jiang
Wentao Zhang
Jixiang Li
Ji Liu
Ce Zhang
Tengjiao Wang
220
32
0
18 Jan 2022
Interpretable Data-Based Explanations for Fairness Debugging
Romila Pradhan
Jiongli Zhu
Boris Glavic
Babak Salimi
398
68
0
17 Dec 2021
Enabling SQL-based Training Data Debugging for Federated Learning
Proceedings of the VLDB Endowment (PVLDB), 2021
Yejia Liu
Weiyuan Wu
Lampros Flokas
Jiannan Wang
Eugene Wu
FedML
168
16
0
26 Aug 2021
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
The VLDB journal (VLDBJ), 2021
Yan Zhao
Yu Shen
Wentao Zhang
Jiawei Jiang
Bolin Ding
...
Jingren Zhou
Zhi-Xin Yang
Wentao Wu
Ce Zhang
Tengjiao Wang
LRM
236
55
0
19 Jul 2021
Explaining Inference Queries with Bayesian Optimization
Proceedings of the VLDB Endowment (PVLDB), 2021
Brandon Lockhart
Jinglin Peng
Weiyuan Wu
Jiannan Wang
Eugene Wu
227
9
0
10 Feb 2021
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
IEEE International Conference on Data Engineering (ICDE), 2020
Cédric Renggli
Luka Rimanic
Luka Kolar
Wentao Wu
Ce Zhang
351
4
0
16 Oct 2020
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