The article discusses a computational model designed to mimic how humans infer hidden rules by conducting experiments. The model is based on the principles that even if a rule is…
Continue readingThe article discusses the development of PriorBoost, an adaptive algorithm for learning from aggregate responses. The paper focuses on the construction of aggregation sets (termed ‘bags’) for event-level loss functions….
Continue readingThe paper discusses the role of Bayesian Deep Learning (BDL) in the current deep learning research landscape. It points out that while most research focuses on predictive accuracy in supervised…
Continue readingThe research paper presents an active learning framework for Android malware detection called ActDroid. With the growing popularity of Android, the need for effective malware detection systems has increased. The…
Continue readingThe article discusses the use of Supervised Finetuning (SFT) on instruction datasets for improving the performance of large language models (LLMs). However, the high cost of annotation for quality responses…
Continue readingThe paper discusses the limitations of current active learning approaches and proposes a new method that is model agnostic and doesn’t require an iterative process. The authors aim to use…
Continue readingThe paper by Jon Schneider and Julian Zimmert from Google Research addresses the problem of designing contextual bandit algorithms in cross-learning settings, where the learner observes the loss for the…
Continue readingThis research paper focuses on the convergence rates of loss and uncertainty-based active learning algorithms. The authors first provide conditions for a convergence rate guarantee and apply this to linear…
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