Travis Dick
Title
Cited by
Cited by
Year
Learning to Branch
MF Balcan, T Dick, T Sandholm, E Vitercik
International Conference on Machine Learning, 2018
922018
Online learning in Markov decision processes with changing cost sequences
T Dick, A Gyorgy, C Szepesvari
International Conference on Machine Learning, 512-520, 2014
562014
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
442017
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
442013
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
402013
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
392018
Envy-free classification
MF Balcan, T Dick, R Noothigattu, AD Procaccia
arXiv preprint arXiv:1809.08700, 2018
38*2018
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.
A Blum, T Dick, N Manoj, H Zhang
J. Mach. Learn. Res. 21, 211:1-211:21, 2020
222020
Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search.
T Dick, CP Quintero, M Jägersand, A Shademan
Robotics: Science and Systems, 2013
202013
Data-driven clustering via parameterized Lloyd's families
MF Balcan, T Dick, C White
arXiv preprint arXiv:1809.06987, 2018
192018
How much data is sufficient to learn high-performing algorithms?
MF Balcan, D DeBlasio, T Dick, C Kingsford, T Sandholm, E Vitercik
arXiv preprint arXiv:1908.02894, 2019
112019
Differentially Private Covariance Estimation.
K Amin, T Dick, A Kulesza, AM Medina, S Vassilvitskii
NeurIPS, 14190-14199, 2019
112019
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
112017
Learning to link
MF Balcan, T Dick, M Lang
arXiv preprint arXiv:1907.00533, 2019
92019
How many random restarts are enough
T Dick, E Wong, C Dann
Technical report, 2014
82014
Learning piecewise Lipschitz functions in changing environments
D Sharma, MF Balcan, T Dick
International Conference on Artificial Intelligence and Statistics, 3567-3577, 2020
72020
Policy gradient reinforcement learning without regret
TB Dick
72015
Semi-bandit optimization in the dispersed setting
MF Balcan, T Dick, W Pegden
Conference on Uncertainty in Artificial Intelligence, 909-918, 2020
62020
Private covariance estimation via iterative eigenvector sampling
K Amin, T Dick, A Kulesza, AM Medina, S Vassilvitskii
2018 NIPS workshop in Privacy-Preserving Machine Learning 250, 2018
62018
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
22021
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