(Last updated: Aug 2023)

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Research Articles

(1) Jiang, H.; Li, Q. Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. In Geometric Science of Information; Nielsen, F., Barbaresco, F., Eds.; Lecture Notes in Computer Science; Springer Nature Switzerland: Cham, 2023; pp 342–350. https://doi.org/10.1007/978-3-031-38299-4_36.

(2) Tian, S. I. P.; Ren, Z.; Venkataraj, S.; Cheng, Y.; Bash, D.; Oviedo, F.; Senthilnath, J.; Chellappan, V.; Lim, Y.-F.; Aberle, A. G.; MacLeod, B. P.; Parlane, F. G. L.; Berlinguette, C. P.; Li, Q.; Buonassisi, T.; Liu, Z. Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films. Digital Discovery 2023. https://doi.org/10.1039/D2DD00149G.

(3) Arisaka, S.; Li, Q. Principled Acceleration of Iterative Numerical Methods Using Machine Learning. In Proceedings of the 40th International Conference on Machine Learning; PMLR, 2023; pp 1041–1059.

(4) Jiang, H.; Li, Q.; Li, Z.; Wang, S. A Brief Survey on the Approximation Theory for Sequence Modelling. Journal of Machine Learning 2023, 2 (1), 1–30. https://doi.org/10.4208/jml.221221.

(5) Siemenn, A. E.; Ren, Z.; Li, Q.; Buonassisi, T. Fast Bayesian Optimization of Needle-in-a-Haystack Problems Using Zooming Memory-Based Initialization (ZoMBI). npj Comput Mater 2023, 9 (1), 1–13. https://doi.org/10.1038/s41524-023-01048-x.

(6) Bao, C.; Li, Q.; Shen, Z.; Tai, C.; Wu, L.; Xiang, X. Approximation Analysis of Convolutional Neural Networks. East Asian Journal on Applied Mathematics 2023, 3, 524–549. https://doi.org/10.4208/eajam.2022-270.070123.

(7) Zhang, M.; Li, Q.; Liu, J. On Stability and Regularization for Data-Driven Solution of Parabolic Inverse Source Problems. Journal of Computational Physics 2023, 474, 111769. https://doi.org/10.1016/j.jcp.2022.111769.

(8) Lin, B.; Li, Q.; Ren, W. Computing High-Dimensional Invariant Distributions from Noisy Data. Journal of Computational Physics 2023, 474, 111783. https://doi.org/10.1016/j.jcp.2022.111783.

(9) Hippalgaonkar, K.; Li, Q.; Wang, X.; Fisher, J. W.; Kirkpatrick, J.; Buonassisi, T. Knowledge-Integrated Machine Learning for Materials: Lessons from Gameplaying and Robotics. Nat Rev Mater 2023, 1–20. https://doi.org/10.1038/s41578-022-00513-1.

(10) Siemenn, A. E.; Ren, Z.; Li, Q.; Buonassisi, T. Accelerating the Discovery of Rare Materials with Bounded Optimization Techniques. In NeurIPS Workshop on AI for Accelerated Design; 2022.

(11) Anwar Ali, H. P.; Zhao, Z.; Tan, Y. J.; Yao, W.; Li, Q.; Tee, B. C. K. Dynamic Modeling of Intrinsic Self-Healing Polymers Using Deep Learning. ACS Appl. Mater. Interfaces 2022, 14 (46), 52486–52498. https://doi.org/10.1021/acsami.2c14543.

(12) Liu, F.; Yang, H.; Hayou, S.; Li, Q. From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. Transactions on Machine Learning Research 2022.

(13) Chen, Z.; Li, Q.; Zhang, Z. Self-Healing Robust Neural Networks via Closed-Loop Control. Journal of Machine Learning Research 2022, 23 (319), 1–54.

(14) Zhao, Z.; Li, Q. Adaptive Sampling Methods for Learning Dynamical Systems. In Proceedings of Mathematical and Scientific Machine Learning; PMLR, 2022; pp 335–350.

(15) Guo, Y.; Dietrich, F.; Bertalan, T.; Doncevic, D. T.; Dahmen, M.; Kevrekidis, I. G.; Li, Q. Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. SIAM J. Sci. Comput. 2022, A1911–A1933. https://doi.org/10.1137/21M1418629.

(16) Lin, B.; Li, Q.; Ren, W. Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. Journal of Scientific Computing 2022, 91 (3), 77. https://doi.org/10.1007/s10915-022-01844-5.

(17) Zou, Y.; Liu, F.; Li, Q. Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate. In International Conference on Learning Representations; 2022.

(18) Lin, B.; Li, Q.; Ren, W. A Data Driven Method for Computing Quasipotentials. In Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference; PMLR, 2022; pp 652–670.

(19) Li, Z.; Jiang, H.; Li, Q. On the Approximation Properties of Recurrent Encoder-Decoder Architectures. In International Conference on Learning Representations; 2022.

(20) Li, Q.; Lin, T.; Shen, Z. Deep Learning via Dynamical Systems: An Approximation Perspective. J. Eur. Math. Soc. 2022. https://doi.org/10.4171/JEMS/1221.

(21) Ren, Z.; Tian, S. I. P.; Noh, J.; Oviedo, F.; Xing, G.; Li, J.; Liang, Q.; Zhu, R.; Aberle, A. G.; Sun, S.; Wang, X.; Liu, Y.; Li, Q.; Jayavelu, S.; Hippalgaonkar, K.; Jung, Y.; Buonassisi, T. An Invertible Crystallographic Representation for General Inverse Design of Inorganic Crystals with Targeted Properties. Matter 2022, 5 (1), 314–335. https://doi.org/10.1016/j.matt.2021.11.032.

(22) Li, Z.; Han, J.; E, W.; Li, Q. Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. Journal of Machine Learning Research 2022, 23 (42), 1–85.

(23) Zhang, C.; Li, Q. Distributed Optimization for Degenerate Loss Functions Arising from Over-Parameterization. Artificial Intelligence 2021, 301, 103575. https://doi.org/10.1016/j.artint.2021.103575.

(24) Yu, H.; Tian, X.; E, W.; Li, Q. OnsagerNet: Learning Stable and Interpretable Dynamics Using a Generalized Onsager Principle. Phys. Rev. Fluids 2021, 6 (11), 114402. https://doi.org/10.1103/PhysRevFluids.6.114402.

(25) Jiang, H.; Li, Z.; Li, Q. Approximation Theory of Convolutional Architectures for Time Series Modelling. In Proceedings of the 38th International Conference on Machine Learning; PMLR, 2021; pp 4961–4970.

(26) Huang, T.; Goh, S. T.; Gopalakrishnan, S.; Luo, T.; Li, Q.; Lau, H. C. QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates. In 2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW); 2021; pp 35–40. https://doi.org/10.1109/ICDCSW53096.2021.00013.

(27) Ye, N.; Tang, J.; Deng, H.; Zhou, X.-Y.; Li, Q.; Li, Z.; Yang, G.-Z.; Zhu, Z. Adversarial Invariant Learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2021; pp 12446–12454.

(28) Ye, N.; Li, Q.; Zhou, X.-Y.; Zhu, Z. Amata: An Annealing Mechanism for Adversarial Training Acceleration. AAAI 2021, 35 (12), 10691–10699.

(29) Mekki-Berrada, F.; Ren, Z.; Huang, T.; Wong, W. K.; Zheng, F.; Xie, J.; Tian, I. P. S.; Jayavelu, S.; Mahfoud, Z.; Bash, D.; Hippalgaonkar, K.; Khan, S.; Buonassisi, T.; Li, Q.; Wang, X. Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis. npj Computational Materials 2021, 7 (1), 55. https://doi.org/10.1038/s41524-021-00520-w.

(30) Ye, N.; Li, Q.; Zhou, X.-Y.; Zhu, Z. An Annealing Mechanism for Adversarial Training Acceleration. IEEE Transactions on Neural Networks and Learning Systems 2021, 1–12. https://doi.org/10.1109/TNNLS.2021.3103528.

(31) Bash, D.; Cai, Y.; Chellappan, V.; Wong, S. L.; Yang, X.; Kumar, P.; Tan, J. D.; Abutaha, A.; Cheng, J. J.; Lim, Y.-F.; Tian, S. I. P.; Ren, Z.; Mekki-Berrada, F.; Wong, W. K.; Xie, J.; Kumar, J.; Khan, S. A.; Li, Q.; Buonassisi, T.; Hippalgaonkar, K. Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites. Advanced Functional Materials 2021, 31 (36), 2102606. https://doi.org/10.1002/adfm.202102606.

(32) Li, Z.; Han, J.; E, W.; Li, Q. On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. In International Conference on Learning Representations; 2020.

(33) Chen, Z.; Li, Q.; Zhang, Z. Towards Robust Neural Networks via Close-Loop Control. In International Conference on Learning Representations; 2020.

(34) Ren, Z.; Oviedo, F.; Thway, M.; Tian, S. I. P.; Wang, Y.; Xue, H.; Dario Perea, J.; Layurova, M.; Heumueller, T.; Birgersson, E.; Aberle, A. G.; Brabec, C. J.; Stangl, R.; Li, Q.; Sun, S.; Lin, F.; Peters, I. M.; Buonassisi, T. Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics. npj Computational Materials 2020, 6 (1), 9. https://doi.org/10.1038/s41524-020-0277-x.

(35) Yang, B.; Li, Q. Dynamics of Taxi-like Logistics Systems: Theory and Microscopic Simulations. Transportmetrica B: Transport Dynamics 2020, 8 (1), 129–149.

(36) Li, Q.; Lin, B.; Ren, W. Computing Committor Functions for the Study of Rare Events Using Deep Learning. The Journal of Chemical Physics 2019, 151 (5), 054112. https://doi.org/10.1063/1.5110439.

(37) Li, Q.; Tai, C.; E, W. Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. Journal of Machine Learning Research 2019, 20 (40), 1–47.

(38) Li, Q.; Staver, A. C.; E, W.; Levin, S. A. Spatial Feedbacks and the Dynamics of Savanna and Forest. Theoretical Ecology 2019, 12 (2), 237–262.

(39) Kumar, J. N.; Li, Q.; Jun, Y. Challenges and Opportunities of Polymer Design with Machine Learning and High Throughput Experimentation. MRS Communications 2019, 9 (2), 537–544.

(40) Kumar, J. N.; Li, Q.; Tang, K. Y. T.; Buonassisi, T.; Gonzalez-Oyarce, A. L.; Ye, J. Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design. npj Computational Materials 2019, 5 (1), 73. https://doi.org/10.1038/s41524-019-0209-9.

(41) E, W.; Han, J.; Li, Q. A Mean-Field Optimal Control Formulation of Deep Learning. Research in the Mathematical Sciences 2019, 6 (1), 10.

(42) Zhang, C.; Li, Q.; Zhao, P. Decentralized Optimization with Edge Sampling. In Proceedings of the twenty-eighth international joint conference on artificial intelligence, {IJCAI-19}; International Joint Conferences on Artificial Intelligence Organization, 2019; pp 658–664. https://doi.org/10.24963/ijcai.2019/93.

(43) Cai, Y.; Li, Q.; Shen, Z. A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. In International Conference on Machine Learning; 2019; pp 882–890.

(44) Zeng, Y.; Li, Q.; Bai, K. Prediction of Interstitial Diffusion Activation Energies of Nitrogen , Oxygen , Boron and Carbon in Bcc , Fcc , and Hcp Metals Using Machine Learning. Computational Materials Science 2018, 144, 232–247. https://doi.org/10.1016/j.commatsci.2017.12.030.

(45) Kemeth, F. P.; Haugland, S. W.; Dietrich, F.; Bertalan, T.; Höhlein, K.; Li, Q.; Bollt, E. M.; Talmon, R.; Krischer, K.; Kevrekidis, I. G. An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning. IEEE Access 2018, 6, 77402–77413.

(46) Bollt, E. M.; Li, Q.; Dietrich, F.; Kevrekidis, I. On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions. SIAM Journal on Applied Dynamical Systems 2018, 17 (2), 1925–1960.

(47) Yang, B.; Li, Q. Turn-by-Turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning. In 2018 IEEE Intelligent Vehicles Symposium (IV); IEEE, 2018; pp 1659–1664.

(48) Li, Q.; Hao, S. An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. In Proceedings of the 35th international conference on machine learning; Dy, J., Krause, A., Eds.; 2018; Vol. 80, pp 2985–2994.

(49) Li, Q.; Tai, C.; E, W. Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. In Proceedings of the 34th International Conference on Machine Learning; PMLR, 2017; pp 2101–2110.

(50) Wang, C.; Li, Q.; E, W.; Chazelle, B. Noisy Hegselmann-Krause Systems: Phase Transition and the 2R-Conjecture. Journal of Statistical Physics 2017, 166 (5), 1209–1225.

(51) Li, Q.; Dietrich, F.; Bollt, E. M.; Kevrekidis, I. G. Extended Dynamic Mode Decomposition with Dictionary Learning: A Data-Driven Adaptive Spectral Decomposition of the Koopman Operator. Chaos: An Interdisciplinary Journal of Nonlinear Science 2017, 27 (10), 103111.

(52) Li, Q.; Chen, L.; Tai, C.; E, W. Maximum Principle Based Algorithms for Deep Learning. The Journal of Machine Learning Research 2017, 18 (1), 5998–6026.

(53) Chazelle, B.; Jiu, Q.; Li, Q.; Wang, C. Well-Posedness of the Limiting Equation of a Noisy Consensus Model in Opinion Dynamics. Journal of Differential Equations 2017, 263 (1), 365–397.

(54) Wang, C.; Li, Q.; E, W.; Chazelle, B. Noisy Hegselmann-Krause Systems: Phase Transition and the 2R-Conjecture. In 2016 IEEE 55th Conference on Decision and Control (CDC); 2016; pp 2632–2637. https://doi.org/10.1109/CDC.2016.7798659.

(55) Li, Q.; E, W. The Free Action of Nonequilibrium Dynamics. J Stat Phys 2015, 161 (2), 300–325. https://doi.org/10.1007/s10955-015-1339-1.

(56) Wang, J.; Li, Q.; E, W. Study of the Instability of the Poiseuille Flow Using a Thermodynamic Formalism. Proceedings of the National Academy of Sciences 2015, 112 (31), 9518–9523.

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