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This paper proposes PROBE, a perturbed gradient algorithm for bilevel optimization with nonconvex lower levels, using second-order stationarity to achieve finite-time convergence and outperform state-of-the-art methods in experiments on LLM-based tasks and meta-learning.
The paper proposes a fast Bayesian optimization method for de novo discovery by leveraging linear models in latent spaces, achieving over 100x speedup over existing methods while maintaining performance.
The paper introduces a federated stochastic bilevel optimization algorithm that uses only first-order gradients to avoid second-order matrix computations, reducing running time, and includes a novel learning rate mechanism with experimental confirmation.
This paper proposes a 'converge-then-diversify' approach for multi-objective Bayesian optimization that decouples convergence and diversity into two stages, showing improved performance under tight evaluation budgets and in high-dimensional problems.