1 code implementation • 8 Nov 2023 • Michaela Hardt, William R. Orchard, Patrick Blöbaum, Shiva Kasiviswanathan, Elke Kirschbaum
Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking.
no code implementations • 16 May 2023 • Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing
By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random.
no code implementations • 10 May 2023 • Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases.
1 code implementation • 18 Nov 2021 • Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts, albeit with additional structure (restriction to interventional distributions under the VAR model).
1 code implementation • 7 Sep 2021 • Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
1 code implementation • 2 Aug 2019 • Chloe Ching-Yun Hsu, Michaela Hardt, Moritz Hardt
Linear dynamical systems are a fundamental and powerful parametric model class.
no code implementations • 10 Jul 2019 • Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew Dai, Michael Howell, Greg Corrado, Claire Cui, Moritz Hardt
We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.
no code implementations • 24 Jan 2018 • 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 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 Howell, Claire Cui, Greg Corrado, Jeff Dean
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.