| Performative prediction JC Perdomo, T Zrnic, C Mendler-Dünner, M Hardt ICML 2020, 2020 | 339 | 2020 |
| Stochastic Optimization for Performative Prediction C Mendler-Dünner, J Perdomo, T Zrnic, M Hardt NeurIPS 2020 33, 2020 | 108 | 2020 |
| Scalable and interpretable product recommendations via overlapping co-clustering R Heckel, M Vlachos, T Parnell, C Mendler-Dünner ICDE 2017, 1033-1044, 2017 | 84 | 2017 |
| Revisiting Design Choices in Proximal Policy Optimization C Ching-Yun Hsu, C Mendler-Dünner, M Hardt RWRL@NeurIPS 2020 - Workshop on Real World Challenges in RL, 2020 | 75* | 2020 |
| Primal-dual rates and certificates C Dünner, S Forte, M Takáč, M Jaggi ICML 2016, 2016 | 70 | 2016 |
| Alternative Microfoundations for Strategic Classification M Jagadeesan, C Mendler-Dünner, M Hardt ICML 2021, 4687-4697, 2021 | 49 | 2021 |
| Performative power M Hardt, M Jagadeesan, C Mendler-Dünner NeurIPS 2022 - Advances in Neural Information Processing Systems, 2022 | 46 | 2022 |
| Computer algorithms for three-dimensional measurement of humeral anatomy: analysis of 140 paired humeri L Vlachopoulos, C Dünner, T Gass, M Graf, O Goksel, C Gerber, ... JSES 2016 - Journal of shoulder and elbow surgery 25, 2016 | 39 | 2016 |
| Snap ML: A hierarchical framework for machine learning C Dünner, T Parnell, D Sarigiannis, N Ioannou, A Anghel, G Ravi, ... NeurIPS 2018 - Advances in Neural Information Processing Systems, 250-260, 2018 | 38* | 2018 |
| A Distributed Second-Order Algorithm You Can Trust C Dünner, A Lucchi, M Gargiani, A Bian, T Hofmann, M Jaggi ICML 2018 - International Conference on Machine Learning, Stockholm, 2018 | 35 | 2018 |
| Addressing interpretability and cold-start in matrix factorization for recommender systems M Vlachos*, C Dünner*, R Heckel, VG Vassiliadis, T Parnell, K Atasu TKDE - IEEE Transactions on Knowledge and Data Engineering 31 (7), 1253-1266, 2018 | 32 | 2018 |
| Regret Minimization with Performative Feedback M Jagadeesan, T Zrnic, C Mendler-Dünner ICML 2022 - International Conference on Machine Learning 162, 9760--9785, 2022 | 30 | 2022 |
| Anticipating performativity by predicting from predictions C Mendler-Dünner, F Ding, Y Wang Advances in neural information processing systems 35, 31171-31185, 2022 | 27 | 2022 |
| Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark C Dünner, T Parnell, K Atasu, M Sifalakis, H Pozidis IEEE Big Data 2017 - IEEE International Conference on Big Data, 2017 | 25* | 2017 |
| Questioning the survey responses of large language models R Dominguez-Olmedo, M Hardt, C Mendler-Dünner arXiv preprint arXiv:2306.07951, 2023 | 24 | 2023 |
| Sampling acquisition functions for batch Bayesian optimization A De Palma, C Mendler-Dünner, T Parnell, A Anghel, H Pozidis BNP@NeurIPS 2018 - Workshop on Bayesian Nonparametrics, 2019 | 22 | 2019 |
| Large-Scale Stochastic Learning using GPUs T Parnell, C Dünner, K Atasu, M Sifalakis, H Pozidis ParLearning 2017 - Proceedings of the 6th International Workshop on Parallel …, 2017 | 19 | 2017 |
| Algorithmic collective action in machine learning M Hardt, E Mazumdar, C Mendler-Dünner, T Zrnic ICML 2023 - International Conference on Machine Learning, 12570-12586, 2023 | 18 | 2023 |
| Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration K Atasu, T Parnell, C Dünner, M Sifalakis, H Pozidis, V Vasileiadis, ... IEEE Big Data 2017 - IEEE International Conference on Big Data, 2017 | 17 | 2017 |
| Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems C Dünner, T Parnell, M Jaggi NIPS 2017 - Advances in Neural Information Processing Systems, 4261-4270, 2017 | 16 | 2017 |
Thomas ParnellPrincipal Research Scientist, IBM ResearchVerified email at zurich.ibm.com
