Tongyang Li

Zhang, C. ., Leng, J. ., & Li, T. . (2021). Quantum Algorithms for Escaping from Saddle Points. Quantum, 5. http://doi.org/10.22331/q-2021-08-20-529 (Original work published August 2021)

Childs, A. M., Hung, S.-H. ., & Li, T. . (2021). Quantum Query Complexity with Matrix-Vector Products. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). http://doi.org/10.4230/LIPIcs.ICALP.2021.55 (Original work published February 2021)

Wang, D. ., You, X. ., Li, T. ., & Childs, A. M. (2021). Quantum Exploration Algorithms for Multi-Armed Bandits. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 10102–10110). Association for the Advancement of Artificial Intelligence (AAAI). http://doi.org/10.1609/aaai.v35i11.17212 (Original work published May 2021)

Li, T. ., Wang, C. ., Chakrabarti, S. ., & Wu, X. . (2021). Sublinear Classical and Quantum Algorithms for General Matrix Games. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 8465–8473). Association for the Advancement of Artificial Intelligence (AAAI). http://doi.org/10.1609/aaai.v35i10.17028 (Original work published May 2021)

Chia, N.-H. ., Gilyen, A. ., Li, T. ., Lin, H.-H. ., Tang, E. ., & Wang, C. . (2020). Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing Quantum machine learning. In Proceedings of STOC 2020. ACM. http://doi.org/10.1145/3357713.3384314 (Original work published June 2020)

Chakrabarti, S. ., Childs, A. M., Li, T. ., & Wu, X. . (2020). Quantum algorithms and lower bounds for convex optimization. Quantum, 4. http://doi.org/10.22331/q-2020-01-13-221 (Original work published January 2020)

Gilyen, A. ., & Li, T. . (2020). Distributional property testing in a quantum world. Proceedings of ITCS 2020, 25, 1–25. http://doi.org/10.4230/LIPIcs.ITCS.2020.25 (Original work published January 2020)

Chakrabarti, S. ., Huang, Y. ., Li, T. ., Feizi, S. ., & Wu, X. . (2019). Quantum Wasserstein Generative Adversarial Networks. Advances in Neural Information Processing Systems (NIPS), 32. http://doi.org/https://papers.nips.cc/paper/8903-quantum-wasserstein-generative-adversarial-networks.pdf (Original work published October 2019)

Li, T. ., Chakrabarti, S. ., & Wu, X. . (2019). Sublinear quantum algorithms for training linear and kernel-based classifiers. Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR, 97, 3815–3824. Retrieved from https://arxiv.org/abs/1904.02276 (Original work published April 2019)

Chia, N.-H. ., Li, T. ., Lin, H.-H. ., & Wang, C. . (2020). Quantum-Inspired Sublinear Algorithm for Solving Low-Rank Semidefinite Programming. In 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. http://doi.org/10.4230/LIPICS.MFCS.2020.23 (Original work published August 2020)