| 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, ... Transaction on Machine Learning Research, 2022 | 1338 | 2022 |
| 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 | 923 | 2019 |
| 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 | 186 | 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 | 177 | 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), 2017 | 67 | 2017 |
| 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 | 64 | 2019 |
| Do offline metrics predict online performance in recommender systems? K Krauth, S Dean, A Zhao, W Guo, M Curmei, B Recht, MI Jordan NeurIPS Workshop on Consequential Decisions in Dynamic Environments, 2020 | 46 | 2020 |
| Modeling content creator incentives on algorithm-curated platforms J Hron, K Krauth, MI Jordan, N Kilbertus, S Dean International Conference on Learning Representations, 2023 | 39 | 2023 |
| On component interactions in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus Advances in Neural Information Processing Systems, 2021 | 37 | 2021 |
| Generic inference in latent Gaussian process models EV Bonilla, K Krauth, A Dezfouli Journal of Machine Learning Research 20 (117), 1-63, 2019 | 28 | 2019 |
| Breaking feedback loops in recommender systems with causal inference K Krauth, Y Wang, MI Jordan arXiv preprint arXiv:2207.01616, 2022 | 23 | 2022 |
| 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 | 20 | 2021 |
| Recommendation systems with distribution-free reliability guarantees AN Angelopoulos, K Krauth, S Bates, Y Wang, MI Jordan Conformal and Probabilistic Prediction with Applications, 175-193, 2023 | 19 | 2023 |
| Design automation of microfluidic single and double emulsion droplets with machine learning A Lashkaripour, DP McIntyre, SGK Calhoun, K Krauth, DM Densmore, ... Nature Communications 15 (1), 83, 2024 | 18 | 2024 |
| Exploration in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus ACM RecSys Workshop on Bandit and Reinforcement Learning from User Interactions, 2020 | 13 | 2020 |
| The dynamics of recommender systems KM Krauth University of California, Berkeley, 2022 | 1 | 2022 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyZweryfikowany adres z cs.berkeley.edu