| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 702 | 2024 |
| Model-based reinforcement learning via meta-policy optimization I Clavera, J Rothfuss, J Schulman, Y Fujita, T Asfour, P Abbeel Conference on Robot Learning (CoRL) 2018, 2018 | 304 | 2018 |
| ProMP: Proximal Meta-Policy Search J Rothfuss, D Lee, I Clavera, T Asfour, P Abbeel International Conference on Learning Representations (ICLR) 2019, 2019 | 239 | 2019 |
| PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees J Rothfuss, V Fortuin, M Josifoski, A Krause International Conference on Machine Learning (ICML) 2021, 2021 | 130 | 2021 |
| DiBS: Differentiable Bayesian Structure Learning L Lorch, J Rothfuss, B Schölkopf, A Krause Advances in Neural Information Processing Systems (NeurIPS), 2021 | 90 | 2021 |
| Conditional density estimation with neural networks: Best practices and benchmarks J Rothfuss, F Ferreira, S Walther, M Ulrich arXiv preprint arXiv:1903.00954, 2019 | 90 | 2019 |
| Meta-Learning Reliable Priors in the Function Space J Rothfuss, D Heyn, J Chen, A Krause Advances in Neural Information Processing Systems 34 (NeurIPS), 2021 | 59* | 2021 |
| Amortized Inference for Causal Structure Learning L Lorch, S Sussex, J Rothfuss, A Krause, B Schölkopf Advances in Neural Information Processing (NeurIPS), 2022 | 56 | 2022 |
| Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution J Rothfuss, F Ferreira, EE Aksoy, Y Zhou, T Asfour IEEE Robotics and Automation Letters 3 (4), 4007-4014, 2018 | 46 | 2018 |
| Variational causal networks: Approximate bayesian inference over causal structures Y Annadani, J Rothfuss, A Lacoste, N Scherrer, A Goyal, Y Bengio, ... arXiv preprint arXiv:2106.07635, 2021 | 36 | 2021 |
| Noise regularization for conditional density estimation J Rothfuss, F Ferreira, S Boehm, S Walther, M Ulrich, T Asfour, A Krause arXiv preprint arXiv:1907.08982, 2019 | 35 | 2019 |
| Meta-Learning Priors for Safe Bayesian Optimization J Rothfuss, C Koenig, A Rupenyan, A Krause Conference on Robot Learning (CoRL) 2022, 2022 | 33 | 2022 |
| BaCaDI: Bayesian Causal Discovery with Unknown Interventions A Hägele, J Rothfuss, L Lorch, VR Somnath, B Schölkopf, A Krause International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 | 18 | 2023 |
| Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice J Rothfuss, M Josifoski, V Fortuin, A Krause Journal of Machine Learning Research (JMLR), 2023 | 16 | 2023 |
| Robustness to pruning predicts generalization in deep neural networks L Kuhn, C Lyle, AN Gomez, J Rothfuss, Y Gal arXiv preprint arXiv:2103.06002, 2021 | 14 | 2021 |
| Hallucinated Adversarial Control for Conservative Offline Policy Evaluation J Rothfuss, B Sukhija, T Birchler, P Kassraie, A Krause Conference on Uncertainty in Artificial Intelligence (UAI), 2023 | 11 | 2023 |
| Instance-dependent generalization bounds via optimal transport S Hou, P Kassraie, A Kratsios, J Rothfuss, A Krause Journal of Machine Learning Reasearch (JMLR), 2023 | 11 | 2023 |
| Meta-Learning Hypothesis Spaces for Sequential Decision-making P Kassraie, J Rothfuss, A Krause International Conference on Machine Learning (ICML), 2022 | 9 | 2022 |
| Lifelong Bandit Optimization: No Prior and No Regret F Schur, P Kassraie, J Rothfuss, A Krause Conference on Uncertainty in Artificial Intelligence (UAI), 2023 | 6 | 2023 |
| MARS: Meta-learning as score matching in the function space KL Pavasovic, J Rothfuss, A Krause International Conference on Learning Representations (ICLR), 2023 | 6 | 2023 |
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
