| 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 | 581 | 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 | 177 | 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 | 122* | 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 | 101 | 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 | 61 | 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 | 39 | 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 | 28 | 2020 |
| Generic Inference in Latent Gaussian Process Models. EV Bonilla, K Krauth, A Dezfouli J. Mach. Learn. Res. 20, 117:1-117:63, 2019 | 24 | 2019 |
| On component interactions in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus Advances in Neural Information Processing Systems, 2021 | 15 | 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 | 7 | 2022 |
| Exploration in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus arXiv preprint arXiv:2009.08956, 2020 | 7 | 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 | 6 | 2021 |
| Breaking Feedback Loops in Recommender Systems with Causal Inference K Krauth, Y Wang, MI Jordan arXiv preprint arXiv:2207.01616, 2022 | 5 | 2022 |
| Recommendation systems with distribution-free reliability guarantees AN Angelopoulos, K Krauth, S Bates, Y Wang, MI Jordan arXiv preprint arXiv:2207.01609, 2022 | 1 | 2022 |
| The Dynamics of Recommender Systems KM Krauth UC Berkeley, 2022 | | 2022 |
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
