Recent work on saddle-point avoidance for nonconvex optimization and machine learning has been accepted to PNAS, one of the most prestigious multidisciplinary journals
Recent work on privacy protection for decentralized stochastic optimization has been accepted to 2024 International Conference on Machine Learning (ICML), a leading international conference in machine learning. Bilevel stochastic optimization is an effective tool for solving many machine learning problems.
Details: Chen, Z, Wang Y Q. Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy. International Conference on Machine Learning (ICML) 2024.
$1.2M CPS project on multi-agent optimization/games recommended for funding by NSF
Recent work on saddle-point avoidance for nonconvex optimization and machine learning has been accepted to PNAS, one of the most prestigious multidisciplinary journals
Recent work on privacy protection for decentralized stochastic optimization has been accepted to 2024 International Conference on Machine Learning (ICML), a leading international conference in machine learning. Bilevel stochastic optimization is an effective tool for solving many machine learning problems.
Details: Chen, Z, Wang Y Q. Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy. International Conference on Machine Learning (ICML) 2024.
$1.2M CPS project on multi-agent optimization/games recommended for funding by NSF