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Results for: Cheng Li

Sparse Spectrum Gaussian Process for Bayesian Optimization

A. Yang, C. Li, S. Rana, S. Gupta, S. Venkatesh

2021 / pp. To appear / Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)

Factor Screening Using Bayesian Active Learning and Gaussian Process Meta-Modelling

C. Li, S. Rana, A. Gill, D. Nguyen, S. Gupta, S. Venkatesh

2020 / pp. in press / Systems Engineering Test and Evaluation Conference

Incorporating Expert Prior in Bayesian Optimisation via Space Warping

A. Ramachandran, S. Gupta, S. Rana, C. Li, S. Venkatesh

2020 / Vol. 195, pp. 105663 / Knowledge-Based Systems

Accelerated Bayesian Optimisation through Weight-Prior Tuning

A. Shilton, S. Gupta, S. Rana, P. Vellanki, C. Li, S. Venkatesh, L. Park, A. Sutti, D. Rubin, T. Dorin, O.

2020 / pp. 635 - 645 / Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

Factor Screening High-Dimensional, Stochastic Combat Simulations

D. Nguyen, C. Li, S. Rana, A. Gill, S. Gupta, S. Venkatesh

2019

Filtering Bayesian Optimization Approach in Weakly Specified Search Space

V. Nguyen, S. Gupta, S. Rana, C. Li, S. Venkatesh

2019 / Vol. 60, pp. 385 - 413 / Knowledge and Information Systems

Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes

D. Rubín, D. Nguyen, P. Vellanki, C. Li, S. Rana, N. Thompson, S. Gupta, K. Pringle, S. Subianto, S. Venkatesh, O.

2019 / Vol. 4, pp. 20571 - 20578 / ACS omega

Optimizing a High-Entropy System: Software-Assisted Development of Highly Hydrophobic Surfaces Using an Amphiphilic Polymer

S. Subianto, C. Li, D. Rubín, S. Rana, S. Gupta, R. He, S. Venkatesh, A. Sutti

2019 / Vol. 4, pp. 15912 - 15922 / ACS omega

Bayesian Optimisation for Objective Functions with Varying Smoothness

A. Kumar, S. Rana, C. Li, S. Gupta, A. Shilton, S. Venkatesh

2019 / pp. 460 - 472 / Australasian Joint Conference on Artificial Intelligence

Efficient Bayesian Optimization for Uncertainty Reduction Over Perceived Optima Locations

V. Nguyen, S. Gupta, S. Rana, M. Thai, C. Li, S. Venkatesh

2019 / pp. 1270 - 1275 / 2019 IEEE International Conference on Data Mining (ICDM)

Explaining Black-box Machine Learning Models using Interpretable Surrogates

D. Kuttichira, S. Gupta, R. Santu, C. Li, S. Venkatesh

2019 / pp. (to appear) / Pacific Rim International Conference on Artificial Intelligence

Accelerating Experimental Design by Incorporating Experimenter Hunches.

C. Li, S. Rana, S. Gupta, V. Nguyen, S. Venkatesh, A. Sutti, C. de, T. Slezak, M. Height, M. Mohammed, O.

2018 / pp. 257 - 266 / 2018 IEEE International Conference on Data Mining (ICDM)

Accelerating Experimental Design by Incorporating Experimenter Hunches

C. Li, S. Rana, S. Gupta, V. Nguyen, S. Venkatesh, A. Sutti, C. de, T. Slezak, M. Height, M. Mohammed, O.

2018 / pp. 257 - 266 / 2018 IEEE International Conference on Data Mining (ICDM)

Sparse Approximation for Gaussian Process with Derivative Observations

A. Yang, C. Li, S. Rana, S. Gupta, S. Venkatesh

2018 / Australasian AI

Filtering Bayesian optimization approach in weakly specified search space

V. Nguyen, S. Gupta, S. Rana, C. Li, S. Venkatesh

2018 / pp. 1 - 29 / Knowledge and Information Systems

Sparse Approximation for {Gaussian} Process with Derivative Observations

A. Yang, C. Li, S. Rana, S. Gupta, S. Venkatesh

2018 / Australasian AI

Efficient Bayesian Optimisation Using Derivative Meta-Model

A. Yang, C. Li, S. Rana, S. Gupta, S. Venkatesh

2018 / pp. 252 - 264 / The 15th Pacific Rim International Conference on Artificial Intelligence