| Learning to Branch MF Balcan, T Dick, T Sandholm, E Vitercik International Conference on Machine Learning, 2018 | 233 | 2018 |
| Differentially private clustering in high-dimensional euclidean spaces MF Balcan, T Dick, Y Liang, W Mou, H Zhang International Conference on Machine Learning, 322-331, 2017 | 96 | 2017 |
| Online learning in Markov decision processes with changing cost sequences T Dick, A Gyorgy, C Szepesvari International Conference on Machine Learning, 512-520, 2014 | 88 | 2014 |
| Random Smoothing Might be Unable to Certify Robustness for High-Dimensional Images A Blum, T Dick, N Manoj, H Zhang Journal of machine learning research 21 (211), 1-21, 2020 | 81 | 2020 |
| Dispersion for data-driven algorithm design, online learning, and private optimization MF Balcan, T Dick, E Vitercik 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS
, 2018 | 71 | 2018 |
| How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design MF Balcan, D DeBlasio, T Dick, C Kingsford, T Sandholm, E Vitercik Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
, 2021 | 56 | 2021 |
| Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints PM Pilarski, TB Dick, RS Sutton 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), 1-8, 2013 | 55 | 2013 |
| Differentially private covariance estimation K Amin, T Dick, A Kulesza, A Munoz, S Vassilvitskii Advances in Neural Information Processing Systems 32, 2019 | 49 | 2019 |
| Sepo: Selecting by pointing as an intuitive human-robot command interface CP Quintero, RT Fomena, A Shademan, N Wolleb, T Dick, M Jagersand 2013 IEEE International Conference on Robotics and Automation, 1166-1171, 2013 | 49 | 2013 |
| Envy-free classification MFF Balcan, T Dick, R Noothigattu, AD Procaccia Advances in Neural Information Processing Systems 32, 2019 | 42 | 2019 |
| Data-driven clustering via parameterized Lloyd's families MFF Balcan, T Dick, C White Advances in Neural Information Processing Systems 31, 2018 | 36 | 2018 |
| Confidence-ranked reconstruction of census microdata from published statistics T Dick, C Dwork, M Kearns, T Liu, A Roth, G Vietri, ZS Wu Proceedings of the National Academy of Sciences 120 (8), e2218605120, 2023 | 27 | 2023 |
| Semi-bandit optimization in the dispersed setting MF Balcan, T Dick, W Pegden Conference on Uncertainty in Artificial Intelligence, 909-918, 2020 | 23 | 2020 |
| Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. T Dick, CP Quintero, M Jägersand, A Shademan Robotics: Science and Systems, 2013 | 23 | 2013 |
| Learning piecewise Lipschitz functions in changing environments D Sharma, MF Balcan, T Dick International Conference on Artificial Intelligence and Statistics, 3567-3577, 2020 | 21 | 2020 |
| Learning to link MF Balcan, T Dick, M Lang arXiv preprint arXiv:1907.00533, 2019 | 21 | 2019 |
| How many random restarts are enough T Dick, E Wong, C Dann URL: https://www. cs. cmu. edu/~ epxing/Class/10715-14f/projectreports
, 2014 | 18 | 2014 |
| How much data is sufficient to learn high-performing algorithms? MF Balcan, D DeBlasio, T Dick, C Kingsford, T Sandholm, E Vitercik Journal of the ACM, 2019 | 16 | 2019 |
| Data driven resource allocation for distributed learning T Dick, M Li, VK Pillutla, C White, N Balcan, A Smola Artificial Intelligence and Statistics, 662-671, 2017 | 15 | 2017 |
| Measuring re-identification risk CJ Carey, T Dick, A Epasto, A Javanmard, J Karlin, S Kumar, ... Proceedings of the ACM on Management of Data 1 (2), 1-26, 2023 | 11 | 2023 |
