We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailored recommendations to users. Existing works consider either a synchronous framework, necessitating full agent participation and global synchronization, or assume user homogeneity with identical behaviors. We overcome these limitations by considering (1) federated agents operating in an asynchronous communication paradigm, where no mandatory synchronization is required and all agents communicate independently with the server, (2) heterogeneous user behaviors, where users can be stratified into latent user clusters, each exhibiting distinct preferences. For this setting, we propose a UCB-type algorithm with delicate communication protocols. Through theoretical analysis, we give sub-linear regret bounds on par with those achieved in the synchronous framework, while incurring only logarithmic communication costs. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.

Large language models (LLMs) have achieved great success in a variety of natural language understanding tasks. However, domain discrepancies between the downstream task and the pre-training corpora may have hurdled LLMs to excel further in the vertical applications. Contrary to prior computational-heavy methods, we propose a lightweight solution to further bridge the gap in applying LLMs to diverse downstream tasks — a Fast Adaptation method for LLMs via Prompted Data, in short FAvPD. Notably, with FAvPD, we establish an additional adaptive tuning procedure, wherein we integrate downstream text corpora, gold labels as well as external knowledge sources and then envelop them into a form of highly controllable prompt. As a simple, easy-to-use, and versatile solution, FAvPD lies in the intersection of regimes like knowledge-augmented LLMs, fine-tuning, and adaptation techniques. With extensive experiments, we prove that FAvPD excels in both performance efficacy and training efficiency over related prior works. FAvPD is publicly available at https://github.com/Hyatio/FAvPD.

This work proposes an innovative approach for the gating mechanism of RNN class models. A transfer function is embedded into the original sigmoid to form a new gate function called xigmoid. The purpose is to alleviate the gradient amplification problem when the models are trying to learn features at far end of a long time series. Using the xigmoid function, original LSTM and GRU are converted to xLSTM and xGRU, respectively. The initialization method for the trainable parameters of xigmoid is also derived and discussed as a necessary support to the new method. Verification experiments are conducted for xLSTM and xGRU against several baseline models, showing both faster convergency/training and better accuracy of the proposed xigmoid-based models. The code and datasets are available at https://github.com/privateos/xigmoid