Michaela Hardt

According to our database, Michaela Hardt authored at least 12 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

The PetShop Dataset - Finding Causes of Performance Issues across Microservices.

Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan

Proceedings of the Causal Learning and Reasoning, 2024

2023

Toward Falsifying Causal Graphs Using a Permutation-Based Test.

Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing

CoRR, 2023

Causal information splitting: Engineering proxy features for robustness to distribution shifts.

Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Causal forecasting: generalization bounds for autoregressive models.

Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud.

Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Explaining an increase in predicted risk for clinical alerts.

Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael D. Howell, Greg Corrado, Claire Cui, Moritz Hardt

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Linear Dynamics: Clustering without identification.

Chloe Ching-Yun Hsu, Michaela Hardt, Moritz Hardt

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2018

Scalable and accurate deep learning with electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean

npj Digit. Medicine, 2018

Reply: metrics to assess machine learning models.

Alvin Rajkomar, Andrew M. Dai, Mimi Sun, Michaela Hardt, Kai Chen, Kathryn Rough, Jeffrey Dean

npj Digit. Medicine, 2018

Scalable and accurate deep learning for electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg Corrado, Jeff Dean

CoRR, 2018

2012

Privacy-aware personalization for mobile advertising.

Michaela Hardt, Suman Nath

Proceedings of the ACM Conference on Computer and Communications Security, 2012

Non-tracking web analytics.

Istemi Ekin Akkus, Ruichuan Chen, Michaela Hardt, Paul Francis, Johannes Gehrke

Proceedings of the ACM Conference on Computer and Communications Security, 2012