51 citations · 207 across the 11 of their papers we have counts for
11 papers
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
A Data Quality-Driven View of MLOps
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel +3
Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong…
Online Active Model Selection for Pre-trained Classifiers
Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš +3
Given pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest…
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Bojan Karlaš, Peng Li, Renzhi Wu +4
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubi…
RAB: Provable Robustness Against Backdoor Attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš +2
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there hav…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang +8
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification m…
Data Science through the looking glass and what we found there
Fotis Psallidas, Yiwen Zhu, Bojan Karlas +8
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by…
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Cedric Renggli, Bojan Karlaš, Bolin Ding +4
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learni…
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
Chen Yu, Bojan Karlas, Jie Zhong +2
AutoML has become a popular service that is provided by most leading cloud service providers today. In this paper, we focus on the AutoML problem from the \emph{service provider's…
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Hamza Harkous, Rameez Rahman, Bojan Karlas +1
Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through det…