Karl Krauth
Karl Krauth
PhD student, UC Berkeley
Verified email at - Homepage
Cited by
Cited by
Cloud programming simplified: A berkeley view on serverless computing
E Jonas, J Schleier-Smith, V Sreekanti, CC Tsai, A Khandelwal, Q Pu, ...
arXiv preprint arXiv:1902.03383, 2019
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
Serverless linear algebra
V Shankar, K Krauth, K Vodrahalli, Q Pu, B Recht, I Stoica, ...
Proceedings of the 11th ACM Symposium on Cloud Computing, 281-295, 2020
The Effect of Natural Distribution Shift on Question Answering Models
J Miller, K Krauth, B Recht, L Schmidt
International Conference on Machine Learning, 2020
AutoGP: Exploring the capabilities and limitations of Gaussian process models
K Krauth, EV Bonilla, K Cutajar, M Filippone
Conference for Uncertainty in Artificial intelligence (UAI), 2016
Finite-time analysis of approximate policy iteration for the linear quadratic regulator
K Krauth, S Tu, B Recht
Advances in Neural Information Processing Systems, 2019
Do offline metrics predict online performance in recommender systems?
K Krauth, S Dean, A Zhao, W Guo, M Curmei, B Recht, MI Jordan
arXiv preprint arXiv:2011.07931, 2020
Generic Inference in Latent Gaussian Process Models.
EV Bonilla, K Krauth, A Dezfouli
J. Mach. Learn. Res. 20, 117:1-117:63, 2019
On component interactions in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
Advances in Neural Information Processing Systems, 2021
Modeling content creator incentives on algorithm-curated platforms
J Hron, K Krauth, MI Jordan, N Kilbertus, S Dean
arXiv preprint arXiv:2206.13102, 2022
Exploration in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
arXiv preprint arXiv:2009.08956, 2020
The Stereotyping Problem in Collaboratively Filtered Recommender Systems
W Guo, K Krauth, MI Jordan, N Garg
ACM Conference on Equity and Access in Algorithms, Mechanisms, and …, 2021
Breaking Feedback Loops in Recommender Systems with Causal Inference
K Krauth, Y Wang, MI Jordan
arXiv preprint arXiv:2207.01616, 2022
Recommendation systems with distribution-free reliability guarantees
AN Angelopoulos, K Krauth, S Bates, Y Wang, MI Jordan
arXiv preprint arXiv:2207.01609, 2022
The Dynamics of Recommender Systems
KM Krauth
UC Berkeley, 2022
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