Learning to Branch MF Balcan, T Dick, T Sandholm, E Vitercik
International Conference on Machine Learning, 2018
212 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
90 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
85 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
74 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
62 2018 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
54 2013 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
50 2013 Differentially private covariance estimation K Amin, T Dick, A Kulesza, A Munoz, S Vassilvitskii
Advances in Neural Information Processing Systems 32, 2019
47 2019 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
41 2021 Envy-free classification MFF Balcan, T Dick, R Noothigattu, AD Procaccia
Advances in Neural Information Processing Systems 32, 2019
40 2019 Data-driven clustering via parameterized Lloyd's families MFF Balcan, T Dick, C White
Advances in Neural Information Processing Systems 31, 2018
27 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
22 2023 Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. T Dick, CP Quintero, M Jägersand, A Shademan
Robotics: Science and Systems, 2013
22 2013 Semi-bandit optimization in the dispersed setting MF Balcan, T Dick, W Pegden
Conference on Uncertainty in Artificial Intelligence, 909-918, 2020
18 2020 Learning piecewise Lipschitz functions in changing environments D Sharma, MF Balcan, T Dick
International Conference on Artificial Intelligence and Statistics, 3567-3577, 2020
17 2020 Learning to link MF Balcan, T Dick, M Lang
arXiv preprint arXiv:1907.00533, 2019
17 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
17 2014 How much data is sufficient to learn high-performing algorithms? MF Balcan, D DeBlasio, T Dick, C Kingsford, T Sandholm, E Vitercik
not applicable-unpublished manuscript, 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 Policy gradient reinforcement learning without regret TB Dick
11 2015