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Epigraph Based Multilevel Optimization (EMO) for Enhancing Chain-of-Thought Reasoning Capabilities

https://doi.org/10.1109/ICASSP49660.2025.10889376

Lu, Songtao; Ding, Yanna; Horesh, Lior; Gao, Jianxi; Magdon-Ismail, Malik (April 2025, IEEE)

Free, publicly-accessible full text available April 6, 2026
Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares

https://doi.org/10.1103/PhysRevE.109.034301

Ding, Yanna; Gao, Jianxi; Magdon-Ismail, Malik (March 2024, Physical Review E)
Learning Network Dynamics from Noisy Steady States

https://doi.org/10.1145/3625007.3631184

Ding, Yanna; Gao, Jianxi; Magdon-Ismail, Malik (November 2023, 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM))

We present efficient algorithms to learn the pa- rameters governing the dynamics of networked agents, given equilibrium steady state data. A key feature of our methods is the ability to learn without seeing the dynamics, using only the steady states. A key to the efficiency of our approach is the use of mean-field approximations to tune the parameters within a nonlinear least squares (NLS) framework. Our results on real networks demonstrate the accuracy of our approach in two ways. Using the learned parameters, we can: (i) Recover more accurate estimates of the true steady states when the observed steady states are noisy. (ii) Predict evolution to new equilibrium steady states after perturbations to the network topology.
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