About
I'm a Branco Weiss fellow at Stanford in Polly Fordyce's lab. I create microfluidic devices, large-scale datasets, and machine learning algorithms to understand molecular biology.
I did my PhD in computer science at UC Berkeley with Michael I. Jordan, where I studied machine learning systems from a data-centric perspective.
Publications
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Design Automation of Microfluidic Single and Double Emulsion Droplets with Machine Learning
Ali Lashkaripour, David McIntyre, Suzanne Calhoun, Karl Krauth, Douglas Densmore, Polly Fordyce
Nature Communications, 2024
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Recommendation Systems with Distribution-Free Reliability Guarantees
Anastasios Angelopoulos*, Karl Krauth*, Stephen Bates, Yixin Wang, Michael Jordan
COPA, 2023 (Best Paper Award)
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Modeling Content Creator Incentives on Algorithm-Curated Platforms
Jiri Hron*, Karl Krauth*, Michael I. Jordan, Niki Kilbertus, Sarah Dean
ICLR, 2023 (Notable Top 5%)
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Breaking Feedback Loops in Recommender Systems with Causal Inference
Karl Krauth, Yixin Wang, Michael Jordan
arXiv preprint, 2022
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On Component Interaction in Two-Stage Recommender Systems
Jiri Hron, Karl Krauth, Michael Jordan, Niki Kilbertus
NeurIPS, 2021
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The Stereotyping Problem in Collaboratively Filtered Recommender Systems
Wenshuo Guo*, Karl Krauth*, Michael Jordan, Nikhil Garg
ACM EAAMO, 2021
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Do Offline Metrics Predict Online Performance in Recommender Systems?
Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael Jordan
NeurIPS Workshop, 2020
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Exploration in Two-Stage Recommender Systems
Jiri Hron*, Karl Krauth*, Michael Jordan, Niki Kilbertus
NeurIPS Workshop, 2020
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The Effect of Natural Distribution Shift on Question Answering Models
John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt
ICML, 2020
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Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman
ACM SoCC, 2020
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Finite-Time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Karl Krauth*, Stephen Tu*, Benjamin Recht
NeurIPS, 2019
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Generic Inference in Latent Gaussian Process Models
Edwin Bonilla, Karl Krauth*, Amir Dezfouli
JMLR, 2019
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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
Karl Krauth, Edwin Bonilla, Kurt Cutajar Maurizio Filippone
UAI, 2017