Momentum-based Gradient Methods in Multi-Objective Recommendation

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Momentum-based Gradient Methods in Multi-Objective Recommendation

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On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Conditional gradient methods for stochastically constrained convex minimization

Polynomial Escape-Time From Saddle Points In Distributed Non-Convex Optimization

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Chemical machine learning with kernels: The impact of loss functions

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Use of Transient Measurements for the Optimization of Steady-State Performance via Modifier Adaptation

Global mixed-integer dynamic optimization