CLion achieves O(1/N) generalization error and O(√d / T^{1/4}) convergence for nonconvex stochastic optimization, improving on Lion's O(1/(N τ^T)) bound.
Mathematical analysis of machine learning algorithms
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Variance-aware neural dueling bandit algorithms achieve sublinear regret of order O(d sqrt(sum sigma_t^2) + sqrt(d T)) for wide networks on nonlinear utilities.
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CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization
CLion achieves O(1/N) generalization error and O(√d / T^{1/4}) convergence for nonconvex stochastic optimization, improving on Lion's O(1/(N τ^T)) bound.
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Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Variance-aware neural dueling bandit algorithms achieve sublinear regret of order O(d sqrt(sum sigma_t^2) + sqrt(d T)) for wide networks on nonlinear utilities.