| Principled weight initialization for hypernetworks O Chang, L Flokas, H Lipson International Conference on Learning Representations, 2019 | 87* | 2019 |
| Poincaré recurrence, cycles and spurious equilibria in gradient-descent-ascent for non-convex non-concave zero-sum games EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in Neural Information Processing Systems 32, 2019 | 66 | 2019 |
| No-regret learning and mixed nash equilibria: They do not mix EV Vlatakis-Gkaragkounis, L Flokas, T Lianeas, P Mertikopoulos, ... Advances in Neural Information Processing Systems 33, 1380-1391, 2020 | 48 | 2020 |
| Complaint-driven training data debugging for query 2.0 W Wu, L Flokas, E Wu, J Wang Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 47 | 2020 |
| No-regret learning and mixed Nash equilibria: They do not mix L Flokas, EV Vlatakis-Gkaragkounis, T Lianeas, P Mertikopoulos, ... arXiv preprint arXiv:2010.09514, 2020 | 41 | 2020 |
| Efficiently avoiding saddle points with zero order methods: No gradients required EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in Neural Information Processing Systems 32, 2019 | 30 | 2019 |
| Solving min-max optimization with hidden structure via gradient descent ascent EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in Neural Information Processing Systems 34, 2373-2386, 2021 | 22 | 2021 |
| Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent L Flokas, EV Vlatakis-Gkaragkounis, G Piliouras arXiv preprint arXiv:2101.05248, 2021 | 22* | 2021 |
| Assessing SATNet's ability to solve the symbol grounding problem O Chang, L Flokas, H Lipson, M Spranger Advances in Neural Information Processing Systems 33, 1428-1439, 2020 | 21 | 2020 |
| Hunter nmt system for wmt18 biomedical translation task: Transfer learning in neural machine translation A Khan, S Panda, J Xu, L Flokas Proceedings of the Third Conference on Machine Translation: Shared Task …, 2018 | 16 | 2018 |
| Enabling SQL-based Training Data Debugging for Federated Learning Y Liu, W Wu, L Flokas, J Wang, E Wu arXiv preprint arXiv:2108.11884, 2021 | 14 | 2021 |
| Online wideband spectrum sensing using sparsity L Flokas, P Maragos IEEE Journal of Selected Topics in Signal Processing 12 (1), 35-44, 2018 | 9 | 2018 |
| ConnectorX: accelerating data loading from databases to dataframes X Wang, W Wu, J Wu, Y Chen, N Zrymiak, C Qu, L Flokas, G Chow, ... Proceedings of the VLDB Endowment 15 (11), 2994-3003, 2022 | 8 | 2022 |
| Efficiently avoiding saddle points with zero order methods: No gradients required L Flokas, EV Vlatakis-Gkaragkounis, G Piliouras arXiv preprint arXiv:1910.13021, 2019 | 7 | 2019 |
| Complaint-Driven Training Data Debugging at Interactive Speeds L Flokas, W Wu, Y Liu, J Wang, N Verma, E Wu Proceedings of the 2022 International Conference on Management of Data, 369-383, 2022 | 6 | 2022 |
| How I stopped worrying about training data bugs and started complaining L Flokas, W Wu, J Wang, N Verma, E Wu Proceedings of the Sixth Workshop on Data Management for End-To-End Machine …, 2022 | | 2022 |
| Hard Threshold Least Mean Squares Algorithm L Flokas, P Maragos arXiv preprint arXiv:1608.01128, 2016 | | 2016 |
Emmanouil-Vasileios Vlatakis-Gkarag...PhD Candidate in Computer Science, Columbia UniversityDirección de correo verificada de cs.columbia.edu
