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2020 – today
- 2024
[i8]Xingyou Song, Qiuyi Zhang, Chansoo Lee, Emily Fertig, Tzu-Kuo Huang, Lior Belenki, Greg Kochanski, Setareh Ariafar, Srinivas Vasudevan, Sagi Perel, Daniel Golovin:
The Vizier Gaussian Process Bandit Algorithm. CoRR abs/2408.11527 (2024)- 2023
- [i7]Daniel Golovin, Gábor Bartók, Eric Chen, Emily Donahue, Tzu-Kuo Huang, Efi Kokiopoulou, Ruoyan Qin, Nikhil Sarda, Justin Sybrandt, Vincent Tjeng:
SmartChoices: Augmenting Software with Learned Implementations. CoRR abs/2304.13033 (2023) - 2020
- [c19]Sai Yalamanchi, Tzu-Kuo Huang, Galen Clark Haynes, Nemanja Djuric:
Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps. ITSC 2020: 1-6 - [c18]Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric:
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets. IV 2020: 1655-1662 - [i6]Sai Yalamanchi, Tzu-Kuo Huang, Galen Clark Haynes, Nemanja Djuric:
Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps. CoRR abs/2003.06143 (2020)
2010 – 2019
- 2019
- [j3]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20: 65:1-65:50 (2019) - [c17]Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric:
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks. ICRA 2019: 2090-2096 - [i5]Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric:
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets. CoRR abs/1906.08469 (2019) - 2018
- [j2]Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, Siddhartha Sen:
Enhancing Selectivity in Big Data. IEEE Secur. Priv. 16(1): 34-42 (2018) - [i4]Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric:
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks. CoRR abs/1809.10732 (2018) - 2017
- [c16]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. ICML 2017: 1915-1924 - [c15]Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, Siddhartha Sen:
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization. IEEE Symposium on Security and Privacy 2017: 78-95 - [i3]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. CoRR abs/1703.01014 (2017) - [i2]Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, Siddhartha Sen:
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization. CoRR abs/1705.07512 (2017) - 2016
- [c14]Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu:
Active Learning with Oracle Epiphany. NIPS 2016: 2820-2828 - 2015
- [c13]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. NIPS 2015: 2755-2763 - [c12]Yifei Ma, Tzu-Kuo Huang, Jeff G. Schneider:
Active Search and Bandits on Graphs using Sigma-Optimality. UAI 2015: 542-551 - [i1]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. CoRR abs/1506.08669 (2015) - 2014
- [c11]Xuezhi Wang, Tzu-Kuo Huang, Jeff G. Schneider:
Active Transfer Learning under Model Shift. ICML 2014: 1305-1313 - 2013
- [b1]Tzu-Kuo Huang:
Exploiting Non-Sequence Data in Dynamic Model Learning. Carnegie Mellon University, USA, 2013 - [c10]Tzu-Kuo Huang, Jeff G. Schneider:
Spectral Learning of Hidden Markov Models from Dynamic and Static Data. ICML (3) 2013: 630-638 - [c9]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. NIPS 2013: 333-341 - 2012
- [c8]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Bi-clustered Vector Autoregressive Models. ECML/PKDD (2) 2012: 741-756 - 2011
- [c7]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Auto-regressive Models from Sequence and Non-sequence Data. NIPS 2011: 1548-1556 - 2010
- [c6]Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G. Schneider, Jaime G. Carbonell:
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. SDM 2010: 211-222 - [c5]Tzu-Kuo Huang, Le Song, Jeff G. Schneider:
Learning Nonlinear Dynamic Models from Non-sequenced Data. AISTATS 2010: 350-357
2000 – 2009
- 2009
- [c4]Tzu-Kuo Huang, Jeff G. Schneider:
Learning linear dynamical systems without sequence information. ICML 2009: 425-432 - 2006
- [j1]Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin:
Generalized Bradley-Terry Models and Multi-Class Probability Estimates. J. Mach. Learn. Res. 7: 85-115 (2006) - [c3]Tzu-Kuo Huang, Chih-Jen Lin
, Ruby C. Weng:
Ranking individuals by group comparisons. ICML 2006: 425-432 - 2004
- [c2]Simon M. Lucas, Tzu-Kuo Huang:
Sequence Recognition with Scanning N-Tuple Ensembles. ICPR (3) 2004: 410-413 - [c1]Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
A Generalized Bradley-Terry Model: From Group Competition to Individual Skill. NIPS 2004: 601-608
Coauthor Index
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