- Guang Chengs Trustworthy AI Lab
The Trustworthy AI Lab at UCLA envisions AI 2 0 as being driven by trustworthiness, surpassing mere performance, and being built upon generative data, enhancing raw data
- People - University of California, Los Angeles
Ching-Wei Cheng [Statistical Studies of Genetic Algorithm] (PhD, 2014-2019) Data Scientist in Lowes Botao Hao [Statistical Guarantees in Non-Convex Optimization] (PhD, 2014-2019) Research Scientist in OpenAI Qing Yang [Random Tensor Theory Its Applications] (Postdoc, 2017-2020)
- Guang Cheng - University of California, Los Angeles
Dept of Statistics, UCLA Full Professor, 2021--Now Dept of Statistics, Purdue University Assistant Associate Full Professor, 2008 2013 2016--2021 SAMSI (Statistical and Applied Mathematical Sciences Institute) Postdoc Fellow, Aug, 2007 – July, 2008 Dept of Statistical Science, Duke University Visiting Assistant Professor, Aug, 2006 – July, 2007
- Research - University of California, Los Angeles
Cheng, Zhang and Shang** (2015) Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension, Annals of Institute of Statistical Mathematics, 67, 93-127
- Teaching - University of California, Los Angeles
Generative AI Hackathon, Sponsored by Trustworthy AI Lab and UCLA GES, June, 2024 Generative AI for Healthcare Workshop, UCLA, April 19, 2024 2nd Workshop on Synthetic Data for AI in Finance (ICAIF 23), New York City, NY, Nov 27, 2023 Synthetic Data Workshop, Amazon Machine Learning Conference, Seattle, Oct 6, 2023
- Directions to Guang Chengs Office Boelter Hall 9404
First, we assume you can get to the south-east corner of Boelter Hall If not, you should probably check the official UCLA map here Boelter Hall is attached to the Math Science Building
- Teaching - University of California, Los Angeles
Teaching Generative Data Science (2023-2024) Big Data Theory (2015-2018) Empirical Processes (2008-2010)
- Bootstrap Consistency for General Semiparametric M-Estimation
It takes long time to run Markov chain to get accurate inferences for when has slow convergence rate (Cheng and Kosorok, 2008a, b); 3: The last approach is the Bootstrap Sampling The bootstrap method has already been used before the above approaches were invented
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