Karl Krauth

According to our database, Karl Krauth authored at least 15 papers between 2017 and 2023.

Collaborative distances:

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

2023

Modeling content creator incentives on algorithm-curated platforms.

Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Recommendation Systems with Distribution-Free Reliability Guarantees.

Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

2022

The Dynamics of Recommender Systems

Karl Krauth

PhD thesis, 2022

Breaking Feedback Loops in Recommender Systems with Causal Inference.

Karl Krauth, Yixin Wang, Michael I. Jordan

CoRR, 2022

2021

On Component Interactions in Two-Stage Recommender Systems.

Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Stereotyping Problem in Collaboratively Filtered Recommender Systems.

Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg

Proceedings of the EAAMO 2021: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtual Event, USA, October 5, 2021

2020

Do Offline Metrics Predict Online Performance in Recommender Systems?

Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan

CoRR, 2020

Exploration in two-stage recommender systems.

Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus

CoRR, 2020

The Effect of Natural Distribution Shift on Question Answering Models.

John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

Proceedings of the 37th International Conference on Machine Learning, 2020

Serverless linear algebra.

Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman

Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019

Generic Inference in Latent Gaussian Process Models.

Edwin V. Bonilla, Karl Krauth, Amir Dezfouli

J. Mach. Learn. Res., 2019

Cloud Programming Simplified: A Berkeley View on Serverless Computing.

Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, João Carreira, Karl Krauth, Neeraja Jayant Yadwadkar, Joseph E. Gonzalez, Raluca Ada Popa, Ion Stoica, David A. Patterson

CoRR, 2019

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator.

Karl Krauth, Stephen Tu, Benjamin Recht

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

numpywren: serverless linear algebra.

Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley

CoRR, 2018

2017

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models.

Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017