Variational Optimization is a class of methods that reformulate intractable optimization problems by introducing parameterized approximations, typically over probability distributions, and optimizing a tractable surrogate objective such as the evidence lower bound (ELBO). It underlies variational inference in Bayesian models and is applied in machine learning for training latent variable models, generative models, and neural networks under uncertainty. Research applications include variational autoencoders, variational Bayes, and approximate inference in large-scale probabilistic systems.
NEC Labs America is proud to attend NeurIPS 2024 in Vancouver, Canada from December 10-15. Zachary Izzo will present Subgroup Discovery with the Cox Model, Shaobo Han will present VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks and Jonathan Warrell will present Discrete-Continuous Variational Optimization with Local Gradients.
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