Europe
Schema-learning and rebinding as mechanisms of in-context learning and emergence
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs).
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Xenia Miscouridou, Francois Caron, Yee Whye Teh
Neural Information Processing Systems http://nips.cc/
Online Learning with Sublinear Best-Action Queries
In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred.
Recurrently Controlled Recurrent Networks
Yi Tay, Anh Tuan Luu, Siu Cheung Hui
Neural Information Processing Systems http://nips.cc/
Minimal Variance Sampling in Stochastic Gradient Boosting
Differentsamplingapproaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees.
Dual Policy Iteration
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell
Recall therealoptimal n (optimal P) and n isdenoted n( ). Withac0 and solve Eq. 10 exactlyby29].
