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Stepwise Alignment for Constrained Language Model Policy Optimization Akifumi Wachi Thien Q. Tran Rei Sato Takumi Tanabe Y ouhei Akimoto L Y Corporation University of Tsukuba
Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).
Langevin Quasi-Monte Carlo
Sampling from probability distributions is a crucial task in both statistics and machine learning. However, when the target distribution does not permit exact sampling, researchers often rely on Markov chain Monte Carlo (MCMC) methods.
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation Peihua Mai Ran Y an National University of Singapore Y an Pang
To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLP A) based on SecAgg protocol.
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability Ming Xiang
Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial memory/computation overhead.
In-ContextSymmetries: Self-Supervised LearningthroughContextualWorldModels
In this work, drawing insights from world models, we propose to instead learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention tocontext-- a memory module that tracks task-specificstates,actions,andfuturestates.
Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of