5 accepted papers
Federated Learning (FL) often suffers from a trade-off between global model performance and client-level fairness due to data heterogeneity, which often leads to inconsistent performance of the globally trained models, resulting in unfair outcomes among users. Existing fair FL algorithms face a trad…
High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have emerged as powerful engines for generating it. However, exis…
Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has shown that models initially deemed ``aligned'' can exhibit misaligned behavior after fine-tuning, such as forgetting jailbreak safety features or re-surfacing knowledge tha…
We investigate the fundamental optimization question of minimizing a \emph{target} function $f(x)$, whose gradients are expensive to compute or have limited availability, given access to some \emph{auxiliary} side function $h(x)$ whose gradients are cheap or more available. This formulation captures…
First-order optimization methods comprise two important primitives: i) the computation of gradient information and ii) the computation of the update that leads to the next iterate. In practice there is often a wide mismatch between the time required for the two steps, leading to underutilization of…