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Sebastian Schelter

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Stefan Grafberger

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Publications

2024
[j19]
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  - journals/debu/GrafbergerZS024
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  - https://dblp.org/rec/journals/debu/GrafbergerZS024
Stefan Grafberger, Zeyu Zhang, Sebastian Schelter, Ce Zhang:
Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Eng. Bull. 47(1): 63-81 (2024)
2023
[j18]
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  - journals/pacmmod/GrafbergerGS23
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Stefan Grafberger, Paul Groth, Sebastian Schelter:
Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proc. ACM Manag. Data 1(2): 128:1-128:26 (2023)
[j17]
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  - journals/pvldb/GrafbergerGGS23
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Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter:
MLWHATIF: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? Proc. VLDB Endow. 16(12): 4002-4005 (2023)
[c49]
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  - conf/sigmod/SchelterGGK023
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Sebastian Schelter, Stefan Grafberger, Shubha Guha, Bojan Karlas, Ce Zhang:
Proactively Screening Machine Learning Pipelines with ARGUSEYES. SIGMOD Conference Companion 2023: 91-94
[c47]
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  - conf/www/GrafbergerGS23
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Stefan Grafberger, Paul Groth, Sebastian Schelter:
Provenance Tracking for End-to-End Machine Learning Pipelines. WWW (Companion Volume) 2023: 1512
[i14]
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  - journals/corr/abs-2307-03027
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Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, Ce Zhang:
Improving Retrieval-Augmented Large Language Models via Data Importance Learning. CoRR abs/2307.03027 (2023)
2022
[j13]
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  - journals/vldb/GrafbergerGSS22
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Stefan Grafberger, Paul Groth, Julia Stoyanovich, Sebastian Schelter:
Data distribution debugging in machine learning pipelines. VLDB J. 31(5): 1103-1126 (2022)
[c46]
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  - conf/cidr/SchelterGGSK022
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Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
Screening Native Machine Learning Pipelines with ArgusEyes. CIDR 2022
[c45]
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  - conf/deem/GrafbergerGS22
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Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards data-centric what-if analysis for native machine learning pipelines. DEEM@SIGMOD 2022: 3:1-3:5
2021
[c39]
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  - conf/cidr/GrafbergerSS21
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Stefan Grafberger, Julia Stoyanovich, Sebastian Schelter:
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. CIDR 2021
[c33]
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  - conf/sigmod/SchelterGD21
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Sebastian Schelter, Stefan Grafberger, Ted Dunning:
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. SIGMOD Conference 2021: 1545-1557
[c32]
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  - conf/sigmod/GrafbergerGSS21
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Stefan Grafberger, Shubha Guha, Julia Stoyanovich, Sebastian Schelter:
MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. SIGMOD Conference 2021: 2736-2739
2019
[c21]
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  - conf/icde/SchelterGSRKTBL19
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Sebastian Schelter, Stefan Grafberger, Philipp Schmidt, Tammo Rukat, Mario Kießling, Andrey Taptunov, Felix Bießmann, Dustin Lange:
Differential Data Quality Verification on Partitioned Data. ICDE 2019: 1940-1945

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