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Journal Article
A. Gilyen and Li, T., Distributional property testing in a quantum world, Proceedings of ITCS 2020, vol. 25, no. 19, pp. 1-25, 2020.
A. M. Childs and Li, T., Efficient simulation of sparse Markovian quantum dynamics, Quantum Information and Computation, vol. 17, pp. 901-947, 2017.
F. G. S. L. Brandão, Kalev, A., Li, T., Lin, C. Yen- Yu, Svore, K. M., and Wu, X., Exponential Quantum Speed-ups for Semidefinite Programming with Applications to Quantum Learning, 2017.
S. Chakrabarti, Childs, A. M., Hung, S. - H., Li, T., Wang, C., and Wu, X., Quantum algorithm for estimating volumes of convex bodies, 2019.
S. Chakrabarti, Childs, A. M., Li, T., and Wu, X., Quantum algorithms and lower bounds for convex optimization, Quantum, vol. 4, no. 221, 2020.
C. Zhang, Leng, J., and Li, T., Quantum Algorithms for Escaping from Saddle Points, 2020.
D. Wang, You, X., Li, T., and Childs, A. M., Quantum exploration algorithms for multi-armed bandits, 2020.
T. Li and Wu, X., Quantum query complexity of entropy estimation, IEEE Transactions on Information Theory, vol. 65, no. 5, pp. 2899-2921, 2019.
F. G. S. L. Brandão, Kalev, A., Li, T., Lin, C. Yen- Yu, Svore, K. M., and Wu, X., Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning, To appear at the 46th International Colloquium on Automata, Languages and Programming (ICALP 2019), 2018.
S. Chakrabarti, Huang, Y., Li, T., Feizi, S., and Wu, X., Quantum Wasserstein Generative Adversarial Networks, Advances in Neural Information Processing Systems (NIPS), vol. 32, 2019.
N. - H. Chia, Li, T., Lin, H. - H., and Wang, C., Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches, 2019.
N. - H. Chia, Gilyen, A., Li, T., Lin, H. - H., Tang, E., and Wang, C., Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning, to appear in Proceedings of STOC 2020, 2020.
T. Li, Chakrabarti, S., and Wu, X., Sublinear quantum algorithms for training linear and kernel-based classifiers, Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR , vol. 97, pp. 3815-3824, 2019.