While" instruction-tuned" generative large language models (LLMs) have demonstrated an
impressive ability to generalize to new tasks, the training phases heavily rely on large …

Due to the often limited communication bandwidth of edge devices, most existing federated
learning (FL) methods randomly select only a subset of devices to participate in training at …

Machine learning models are known to memorize private data to reduce their training loss,
which can be inadvertently exploited by privacy attacks such as model inversion and …

Bayesian neural networks (BNNs) offer uncertainty quantification but come with the
downside of substantially increased training and inference costs. Sparse BNNs have been …

Online optimization has gained increasing interest due to its capability of tracking real-world
streaming data. Although online optimization methods have been widely studied in the …

Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language
models to downstream tasks instead of fine-tuning the full model parameters, has been …

Abstract Particle-based Variational Inference methods (ParVIs), like Stein Variational
Gradient Descent, are nonparametric variational inference methods that optimize a set of …

The recently developed Particle-based Variational Inference (ParVI) methods drive the
empirical distribution of a set of\emph {fixed-weight} particles towards a given target …

Large language models (LLMs) have demonstrated remarkable capabilities, but their
outputs can sometimes be unreliable or factually incorrect. To address this, we introduce …

Deep reinforcement learning (RL) has received great success in playing video games and
strategic board games, where a simulator is well-defined, and massive samples are …