Bojan Karlas

Orcid: 0000-0002-6462-3579

According to our database1, Bojan Karlas authored at least 28 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Data Debugging with Shapley Importance over Machine Learning Pipelines.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
DMLR: Data-centric Machine Learning Research - Past, Present and Future.
CoRR, 2023

RAB: Provable Robustness Against Backdoor Attacks.
Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

Proactively Screening Machine Learning Pipelines with ARGUSEYES.
Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023


2022

Data Systems for Managing and Debugging Machine Learning Workflows.
PhD thesis, 2022

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines.
SIGMOD Rec., 2022

DataPerf: Benchmarks for Data-Centric AI Development.
CoRR, 2022

Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines.
CoRR, 2022

Improving Certified Robustness via Statistical Learning with Logical Reasoning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

dcbench: a benchmark for data-centric AI systems.
Proceedings of the DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, 2022

Screening Native Machine Learning Pipelines with ArgusEyes.
Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021
A Data Quality-Driven View of MLOps.
IEEE Data Eng. Bull., 2021

Ease.ML: A Lifecycle Management System for Machine Learning.
Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

Online Active Model Selection for Pre-trained Classifiers.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions.
Proc. VLDB Endow., 2020

End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines.
CoRR, 2020

Building Continuous Integration Services for Machine Learning.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization.
Proc. VLDB Endow., 2019

Data Science through the looking glass and what we found there.
CoRR, 2019

Is advance knowledge of flow sizes a plausible assumption?
Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation, 2019

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment.
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Ease.ml in Action: Towards Multi-tenant Declarative Learning Services.
Proc. VLDB Endow., 2018

Multi-device, Multi-tenant Model Selection with GP-EI.
CoRR, 2018

Network Scheduling in the Dark.
Proceedings of the ACM Symposium on Cloud Computing, 2018

2016
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps.
Proc. Priv. Enhancing Technol., 2016


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