| Performative prediction JC Perdomo, T Zrnic, C Mendler-Dünner, M Hardt ICML 2020, 2020 | 376 | 2020 |
| Stochastic Optimization for Performative Prediction C Mendler-Dünner, J Perdomo, T Zrnic, M Hardt NeurIPS 2020 33, 2020 | 119 | 2020 |
| Scalable and interpretable product recommendations via overlapping co-clustering R Heckel, M Vlachos, T Parnell, C Mendler-Dünner ICDE 2017, 1033-1044, 2017 | 85 | 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 | 79* | 2020 |
| Primal-dual rates and certificates C Dünner, S Forte, M Takáč, M Jaggi ICML 2016, 2016 | 70 | 2016 |
| Performative power M Hardt*, M Jagadeesan*, C Mendler-Dünner* NeurIPS 2022, 2022 | 50 | 2022 |
| Alternative Microfoundations for Strategic Classification M Jagadeesan, C Mendler-Dünner, M Hardt ICML 2021, 4687-4697, 2021 | 50 | 2021 |
| 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 | 42 | 2016 |
| Snap ML: A hierarchical framework for machine learning C Dünner, T Parnell, D Sarigiannis, N Ioannou, A Anghel, G Ravi, ... NeurIPS 2018, 250-260, 2018 | 39* | 2018 |
| Regret Minimization with Performative Feedback M Jagadeesan, T Zrnic, C Mendler-Dünner ICML 2022 162, 9760--9785, 2022 | 38 | 2022 |
| A Distributed Second-Order Algorithm You Can Trust C Dünner, A Lucchi, M Gargiani, A Bian, T Hofmann, M Jaggi ICML 2018, 2018 | 35 | 2018 |
| Questioning the survey responses of large language models R Dominguez-Olmedo, M Hardt, C Mendler-Dünner NeurIPS 2024, 2023 | 34 | 2023 |
| Anticipating performativity by predicting from predictions C Mendler-Dünner, F Ding, Y Wang NeurIPS 2022 35, 31171-31185, 2022 | 33 | 2022 |
| Addressing interpretability and cold-start in matrix factorization for recommender systems M Vlachos*, C Dünner*, R Heckel, VG Vassiliadis, T Parnell, K Atasu TKDE 2018 31 (7), 1253-1266, 2018 | 33 | 2018 |
| 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, 2017 | 25* | 2017 |
| 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 | 23 | 2019 |
| Performative prediction: Past and future M Hardt, C Mendler-Dünner arXiv preprint arXiv:2310.16608, 2023 | 21 | 2023 |
| Algorithmic collective action in machine learning M Hardt*, E Mazumdar*, C Mendler-Dünner*, T Zrnic* ICML 2023, 12570-12586, 2023 | 19 | 2023 |
| 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 |
| Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems C Dünner, T Parnell, M Jaggi NIPS 2017, 4261-4270, 2017 | 18 | 2017 |
Thomas ParnellPrincipal Research Scientist, IBM ResearchVerified email at zurich.ibm.com
