Search Results for author: Bojan Karlaš

Found 8 papers, 5 papers with code

Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines

1 code implementation23 Apr 2022 Bojan Karlaš, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu, Ce Zhang

We present DataScope (ease. ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training.

BIG-bench Machine Learning Fairness

A Data Quality-Driven View of MLOps

no code implementations15 Feb 2021 Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlaš, Wentao Wu, Ce Zhang

Developing machine learning models can be seen as a process similar to the one established for traditional software development.

BIG-bench Machine Learning

Online Active Model Selection for Pre-trained Classifiers

1 code implementation19 Oct 2020 Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš, Johannes Rausch, Ce Zhang, Andreas Krause

Given $k$ 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 while making a small number of queries?

Model Selection

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

1 code implementation11 May 2020 Bojan Karlaš, Peng Li, Renzhi Wu, Nezihe Merve Gürel, Xu Chu, Wentao Wu, Ce Zhang

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data.

BIG-bench Machine Learning

RAB: Provable Robustness Against Backdoor Attacks

1 code implementation19 Mar 2020 Maurice Weber, Xiaojun Xu, Bojan Karlaš, Ce Zhang, Bo Li

In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models.

BIG-bench Machine Learning

Continuous Integration of Machine Learning Models with Towards a Rigorous Yet Practical Treatment

no code implementations1 Mar 2019 Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang

Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development.

2k BIG-bench Machine Learning

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