Accuracy
PrivAuditor: Benchmarking Privacy Vulnerabilities in LLM Adaptation Techniques
Despite these achievements, LLMs often underperform in domain-specific tasks without training on relevant domain data.
Supplementary Material for " Bootstrapping the error of Oja's algorithm "
In this document we provide the detailed proofs of results presented in the main manuscript. We also provide the Hoeffding decomposition for the bootstrap in Proposition A.4. In Section B we provide all results needed for a complete proof of Theorem 1. In Sections B.1, B.2, and B.3 we provide the proof of Theorem 1, the adaptation of high dimensional CLT of [8] to our setting and all supporting lemmas, respectively. In Section C we provide all details of the proof of the Bootstrap consistency, i.e.
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss An Zhang
An Zhang and Leheng Sheng contribute equally to this work. Zhibo Cai is the corresponding author.
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
Our research expands on this area by examining non-smooth weakly-convex FCCO, where the outer function is weakly convex and non-decreasing, and the inner function is weakly-convex.