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cs.LG2026
Generalized Convexity and Smoothness via Conjugate Duality, with Applications to Deep Network Training
Binchuan Qi
Stochastic gradient descent (SGD) and its variants train deep neural networks (DNNs) effectively in practice, yet classical optimization theory does not fully explain this success:…
stat.ML2026
Conjugate Learning Theory: Uncovering the Mechanisms of Trainability and Generalization in Deep Neural Networks
Binchuan Qi
In this work, we propose a notion of practical learnability grounded in finite sample settings, and develop a conjugate learning theoretical framework based on convex conjugate dua…