Overview
Contents of Appendix A Extended Literature Review 14 B Time Uniform Lasso Analysis 15 C Results on Exploration 18 C.1 ALE
Table 2 compares recent work on sparse linear bandits based on a number of important factors. Some of the mentioned bounds depend on problem-dependent parameters (e.g. Carpentier and Munos [ 2012 ] assume that the action set is a Euclidean ball, and that the noise is directly added to the parameter vector, i.e. In this setting, Carpentier and Munos [ 2012 ] present a O ( d p n) regret bound. Li et al. [ 2022 ] require a stronger condition This is generally not true, but may hold with high probability.
Anytime Model Selection in Linear Bandits
M different samples in parallel from the environment so that the reward for each agent is realized.
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense Kalpesh Krishna Yixiao Song Marzena Karpinska John Wieting Mohit Iyyer
API, looking for sequences that match the candidate text within a certain threshold.
D: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews
Systematic literature reviews (SLRs) play an essential role in summarising, synthe-sising and validating scientific evidence.
ProPILE: Probing Privacy Leakage in Large Language Models Siwon Kim 1, Sangdoo Y un 3 Hwaran Lee 3 Martin Gubri
The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data.
Cross-Scale MAE: AT ale of Multi-Scale Exploitation in Remote Sensing Maofeng T ang
We present Cross-Scale MAE, a self-supervised model built upon the Masked Auto-Encoder (MAE).