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2020 – today
- 2024
[c25]Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner:
Causal Inference out of Control: Estimating Performativity without Treatment Randomization. ICML 2024- [i31]Joachim Baumann, Celestine Mendler-Dünner:
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists. CoRR abs/2404.04269 (2024) - [i30]Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt:
An engine not a camera: Measuring performative power of online search. CoRR abs/2405.19073 (2024) - [i29]André F. Cruz, Moritz Hardt, Celestine Mendler-Dünner:
Evaluating language models as risk scores. CoRR abs/2407.14614 (2024) - 2023
- [c24]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. ICML 2023: 12570-12586 - [c23]Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi:
Collaborative Learning via Prediction Consensus. NeurIPS 2023 - [i28]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. CoRR abs/2302.04262 (2023) - [i27]Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner:
Causal Inference out of Control: Estimating the Steerability of Consumption. CoRR abs/2302.04989 (2023) - [i26]Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi:
Collaborative Learning via Prediction Consensus. CoRR abs/2305.18497 (2023) - [i25]Ricardo Dominguez-Olmedo, Moritz Hardt, Celestine Mendler-Dünner:
Questioning the Survey Responses of Large Language Models. CoRR abs/2306.07951 (2023) - [i24]Moritz Hardt, Celestine Mendler-Dünner:
Performative Prediction: Past and Future. CoRR abs/2310.16608 (2023) - 2022
- [c22]Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner:
Regret Minimization with Performative Feedback. ICML 2022: 9760-9785 - [c21]Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner:
Performative Power. NeurIPS 2022 - [c20]Celestine Mendler-Dünner, Frances Ding, Yixin Wang:
Anticipating Performativity by Predicting from Predictions. NeurIPS 2022 - [i23]Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner:
Regret Minimization with Performative Feedback. CoRR abs/2202.00628 (2022) - [i22]Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner:
Performative Power. CoRR abs/2203.17232 (2022) - [i21]Celestine Mendler-Dünner, Frances Ding, Yixin Wang:
Predicting from Predictions. CoRR abs/2208.07331 (2022) - 2021
- [c19]Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui
, Nikolaos Papandreou, Thomas P. Parnell:
Differentially Private Stochastic Coordinate Descent. AAAI 2021: 7176-7184 - [c18]Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt:
Alternative Microfoundations for Strategic Classification. ICML 2021: 4687-4697 - [c17]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. NeurIPS 2021: 13719-13731 - [i20]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. CoRR abs/2106.12012 (2021) - [i19]Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt:
Alternative Microfoundations for Strategic Classification. CoRR abs/2106.12705 (2021) - 2020
- [j3]Thomas P. Parnell, Celestine Dünner, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis:
Tera-scale coordinate descent on GPUs. Future Gener. Comput. Syst. 108: 1173-1191 (2020) - [c16]Celestine Mendler-Dünner, Aurélien Lucchi:
Randomized Block-Diagonal Preconditioning for Parallel Learning. ICML 2020: 6841-6851 - [c15]Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt:
Performative Prediction. ICML 2020: 7599-7609 - [c14]Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic, Moritz Hardt:
Stochastic Optimization for Performative Prediction. NeurIPS 2020 - [i18]Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt:
Performative Prediction. CoRR abs/2002.06673 (2020) - [i17]Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic, Moritz Hardt:
Stochastic Optimization for Performative Prediction. CoRR abs/2006.06887 (2020) - [i16]Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui, Nikolaos Papandreou, Thomas P. Parnell:
Differentially Private Stochastic Coordinate Descent. CoRR abs/2006.07272 (2020) - [i15]Celestine Mendler-Dünner, Aurélien Lucchi:
Randomized Block-Diagonal Preconditioning for Parallel Learning. CoRR abs/2006.13591 (2020) - [i14]Chloe Ching-Yun Hsu, Celestine Mendler-Dünner, Moritz Hardt:
Revisiting Design Choices in Proximal Policy Optimization. CoRR abs/2009.10897 (2020)
2010 – 2019
- 2019
- [b1]Celestine Mendler-Dünner:
System-Aware Algorithms For Machine Learning. ETH Zurich, Zürich, Switzerland, 2019 - [j2]Michail Vlachos, Celestine Dünner, Reinhard Heckel, Vassilios G. Vassiliadis, Thomas P. Parnell, Kubilay Atasu:
Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems. IEEE Trans. Knowl. Data Eng. 31(7): 1253-1266 (2019) - [c13]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. HiPC 2019: 184-194 - [c12]Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell:
SySCD: A System-Aware Parallel Coordinate Descent Algorithm. NeurIPS 2019: 590-600 - [i13]Alessandro De Palma, Celestine Mendler-Dünner, Thomas P. Parnell, Andreea Anghel, Haralampos Pozidis:
Sampling Acquisition Functions for Batch Bayesian Optimization. CoRR abs/1903.09434 (2019) - [i12]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. CoRR abs/1905.00626 (2019) - [i11]Michael Kaufmann, Kornilios Kourtis, Celestine Mendler-Dünner, Adrian Schüpbach, Thomas P. Parnell:
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle. CoRR abs/1909.04885 (2019) - [i10]Andreea Anghel, Nikolas Ioannou, Thomas P. Parnell, Nikolaos Papandreou, Celestine Mendler-Dünner, Haris Pozidis:
Breadth-first, Depth-next Training of Random Forests. CoRR abs/1910.06853 (2019) - [i9]Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell:
SySCD: A System-Aware Parallel Coordinate Descent Algorithm. CoRR abs/1911.07722 (2019) - 2018
- [c11]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. ICML 2018: 1357-1365 - [c10]Celestine Dünner, Thomas P. Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi, Madhusudanan Kandasamy, Haralampos Pozidis:
Snap ML: A Hierarchical Framework for Machine Learning. NeurIPS 2018: 250-260 - [i8]Celestine Dünner, Thomas P. Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Haralampos Pozidis:
Snap ML: A Hierarchical Framework for Machine Learning. CoRR abs/1803.06333 (2018) - [i7]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. CoRR abs/1806.07569 (2018) - [i6]Nikolas Ioannou, Celestine Dünner, Kornilios Kourtis, Thomas P. Parnell:
Parallel training of linear models without compromising convergence. CoRR abs/1811.01564 (2018) - 2017
- [c9]Celestine Dünner, Thomas P. Parnell, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis:
Understanding and optimizing the performance of distributed machine learning applications on apache spark. IEEE BigData 2017: 331-338 - [c8]Kubilay Atasu, Thomas P. Parnell, Celestine Dünner, Manolis Sifalakis, Haralampos Pozidis, Vasileios Vasileiadis, Michail Vlachos, Cesar Berrospi, Abdel Labbi:
Linear-complexity relaxed word Mover's distance with GPU acceleration. IEEE BigData 2017: 889-896 - [c7]Reinhard Heckel, Michail Vlachos, Thomas P. Parnell, Celestine Dünner:
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. ICDE 2017: 1033-1044 - [c6]Kubilay Atasu, Thomas P. Parnell, Celestine Dünner, Michail Vlachos, Haralampos Pozidis:
High-Performance Recommender System Training Using Co-Clustering on CPU/GPU Clusters. ICPP 2017: 372-381 - [c5]Thomas P. Parnell, Celestine Dünner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis:
Large-Scale Stochastic Learning Using GPUs. IPDPS Workshops 2017: 419-428 - [c4]Celestine Dünner, Thomas P. Parnell, Martin Jaggi:
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems. NIPS 2017: 4258-4267 - [i5]Thomas P. Parnell, Celestine Dünner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis:
Large-Scale Stochastic Learning using GPUs. CoRR abs/1702.07005 (2017) - [i4]Celestine Dünner, Thomas P. Parnell, Martin Jaggi:
Efficient Use of Limited-Memory Resources to Accelerate Linear Learning. CoRR abs/1708.05357 (2017) - [i3]Kubilay Atasu, Thomas P. Parnell, Celestine Dünner, Manolis Sifalakis, Haralampos Pozidis, Vasileios Vasileiadis, Michail Vlachos, Cesar Berrospi, Abdel Labbi:
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration. CoRR abs/1711.07227 (2017) - 2016
- [j1]Thomas P. Parnell, Celestine Dünner, Thomas Mittelholzer, Nikolaos Papandreou:
Capacity of the MLC NAND Flash Channel. IEEE J. Sel. Areas Commun. 34(9): 2354-2365 (2016) - [c3]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. ICML 2016: 783-792 - [i2]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. CoRR abs/1602.05205 (2016) - [i1]Celestine Dünner, Thomas P. Parnell, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis:
High-Performance Distributed Machine Learning using Apache SPARK. CoRR abs/1612.01437 (2016) - 2015
- [c2]Thomas P. Parnell, Celestine Dünner, Thomas Mittelholzer, Nikolaos Papandreou, Haralampos Pozidis:
Endurance limits of MLC NAND flash. ICC 2015: 376-381 - 2014
- [c1]Raphael T. L. Rolny, Celestine Dünner, Armin Wittneben:
Power control for cellular networks with large antenna arrays and ubiquitous relaying. SPAWC 2014: 65-69
Coauthor Index
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last updated on 2024-10-07 22:12 CEST by the dblp team
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