| Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations D Basu, D Data, C Karakus, S Diggavi Advances in Neural Information Processing Systems, 14668-14679, 2019 | 406 | 2019 |
| Straggler mitigation in distributed optimization through data encoding C Karakus, Y Sun, S Diggavi, W Yin Advances in Neural Information Processing Systems, 5434-5442, 2017 | 163 | 2017 |
| Redundancy techniques for straggler mitigation in distributed optimization and learning C Karakus, Y Sun, S Diggavi, W Yin Journal of Machine Learning Research 20 (72), 1-47, 2019 | 66 | 2019 |
| Encoded distributed optimization C Karakus, Y Sun, S Diggavi 2017 IEEE international symposium on information theory (ISIT), 2890-2894, 2017 | 57 | 2017 |
| Shifting network tomography toward a practical goal D Ghita, C Karakus, K Argyraki, P Thiran Proceedings of the Seventh COnference on emerging Networking EXperiments and …, 2011 | 55 | 2011 |
| Opportunistic scheduling for full-duplex uplink-downlink networks C Karakus, S Diggavi 2015 IEEE International Symposium on Information Theory (ISIT), 1019-1023, 2015 | 27 | 2015 |
| Privacy-utility trade-off of linear regression under random projections and additive noise M Showkatbakhsh, C Karakus, S Diggavi 2018 IEEE International Symposium on Information Theory (ISIT), 186-190, 2018 | 23 | 2018 |
| Enhancing multiuser MIMO through opportunistic D2D cooperation C Karakus, S Diggavi IEEE Transactions on Wireless Communications 16 (9), 5616-5629, 2017 | 22 | 2017 |
| Reference signals and link adaptation for massive MIMO JB Soriaga, PK Vitthaladevuni, C Karakus, JI Tingfang US Patent 10,505,597, 2019 | 20 | 2019 |
| Amazon sagemaker model parallelism: A general and flexible framework for large model training C Karakus, R Huilgol, F Wu, A Subramanian, C Daniel, D Cavdar, T Xu, ... arXiv preprint arXiv:2111.05972, 2021 | 18 | 2021 |
| Herring: Rethinking the parameter server at scale for the cloud I Thangakrishnan, D Cavdar, C Karakus, P Ghai, Y Selivonchyk, C Pruce SC20: International Conference for High Performance Computing, Networking …, 2020 | 17 | 2020 |
| Gaussian interference channel with intermittent feedback C Karakus, IH Wang, S Diggavi IEEE Transactions on Information Theory 61 (9), 4663-4699, 2015 | 14 | 2015 |
| Rate splitting is approximately optimal for fading Gaussian interference channels J Sebastian, C Karakus, S Diggavi, IH Wang 2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015 | 12 | 2015 |
| Approximate capacity of fast fading interference channels with no instantaneous CSIT J Sebastian, C Karakus, S Diggavi IEEE Transactions on Communications 66 (12), 6015-6027, 2018 | 7 | 2018 |
| Differentially private consensus-based distributed optimization M Showkatbakhsh, C Karakus, S Diggavi arXiv preprint arXiv:1903.07792, 2019 | 6 | 2019 |
| Densifying assumed-sparse tensors: Improving memory efficiency and mpi collective performance during tensor accumulation for parallelized training of neural machine translation … D Cavdar, V Codreanu, C Karakus, JA Lockman, D Podareanu, ... High Performance Computing: 34th International Conference, ISC High …, 2019 | 4 | 2019 |
| Approximately achieving the feedback interference channel capacity with point-to-point codes J Sebastian, C Karakus, S Diggavi 2016 IEEE International Symposium on Information Theory (ISIT), 715-719, 2016 | 3 | 2016 |
| Interference channel with intermittent feedback C Karakus, IH Wang, S Diggavi 2013 IEEE International Symposium on Information Theory, 26-30, 2013 | 3 | 2013 |
| An Achievable Rate Region for Gaussian Interference Channel with Intermittent Feedback C Karakus, I Wang, S Diggavi Communication, Control, and Computing (Allerton), 2013 51st Annual Allerton …, 2013 | 2 | 2013 |
| MADA: Meta-adaptive optimizers through hyper-gradient descent K Ozkara, C Karakus, P Raman, M Hong, S Sabach, B Kveton, V Cevher arXiv preprint arXiv:2401.08893, 2024 | | 2024 |
Suhas DiggaviUCLA在 ee.ucla.edu 的电子邮件经过验证
