Karl Krauth

According to our database1, Karl Krauth authored at least 16 papers between 2017 and 2023.

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Bibliography

2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023

Modeling content creator incentives on algorithm-curated platforms.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Recommendation Systems with Distribution-Free Reliability Guarantees.
Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

2022
The Dynamics of Recommender Systems
PhD thesis, 2022

Breaking Feedback Loops in Recommender Systems with Causal Inference.
CoRR, 2022

2021
On Component Interactions in Two-Stage Recommender Systems.
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.
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?
CoRR, 2020

Exploration in two-stage recommender systems.
CoRR, 2020

The Effect of Natural Distribution Shift on Question Answering Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

Serverless linear algebra.
Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019
Generic Inference in Latent Gaussian Process Models.
J. Mach. Learn. Res., 2019

Cloud Programming Simplified: A Berkeley View on Serverless Computing.
CoRR, 2019

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
numpywren: serverless linear algebra.
CoRR, 2018

2017
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017


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