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On the Exploitability of Instruction Tuning
In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior.
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers Jinsong Chen
These limitations hinder GNNs' ability to capture long-range dependencies in graphs, ultimately
Hierarchical Multi-Agent Skill Discovery Mingyu Yang 1, Yaodong Yang
Skill discovery has shown significant progress in unsupervised reinforcement learning.
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a "dual drift" caused by dual
Improving the Knowledge Gradient Algorithm
The knowledge gradient (KG) algorithm is a popular policy for the best arm identification (BAI) problem. It is built on the simple idea of always choosing the measurement that yields the greatest expected one-step improvement in the estimate of the best mean of the arms.
IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing
In recent years, a large number of algorithms for label integration and noise correction have been proposed to infer the unknown true labels of instances in crowdsourcing.