Contemporary Islamic education faces the challenge of a dichotomy between classical scholarly traditions (naqliyah) and modern science (aqliyah). This study aims to analyse curriculum management at Pesantren Raudhatut Tholabah Setail, which uniquely integrates the study of classical Islamic texts (kitab kuning) by Imam...
Mohamad Hisyam; Universitas KH Mukhtar Syafaat Banyuwangi Syafa’at, Muhammad Hadi; Universitas KH Mukhtar Syafaat Banyuwangi Lukman· Hijri· 0 citations
The improvement of literacy and numeracy quality is a major challenge in primary education in Indonesia, including in madrasah ibtidaiyah. Various international reports such as the Programme for International Student Assessment (PISA) and the National Assessment (AN) show the low achievement of Indonesian students in r...
Afriantoni; UIN Raden Fatah Palembang Afriantoni, Rima Tahira, Reza Shakila Azzahra et al.· Hijri· 0 citations
We develop a class of general diffusion-based stochastic particle optimisation methods for loss functions with intractable gradients. Specifically, we consider problems in which the loss gradient is an integral with respect to a parameter-dependent distribution, a structure that includes training generative models, fin...
Jiechen Jackie Zhang, O. Deniz Akyildiz· 0 citations
Fine-tuning a pretrained language model on a curated dataset can produce spurious correlations between the fine-tuning task and unintended latent factors---such as misaligned personas or political slant---that the curation procedure has entangled with the task. The model can latch onto these spurious correlations, lead...
Ciar\'an M. Gilligan-Lee, Joseph Egan, Yuchen Zhu et al.· 0 citations
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The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam's razor, enabling hyperparameter selection that avoids overfitting. The evidence lower bound (ELBO) objective from variational inference has also been used for similar purposes. In this paper, we define a signal-to-no...
Ethan Harvey, Geoff Pleiss, Michael C. Hughes· 0 citations
We study parametric change-point detection under local differential privacy, focusing on the offline localization of a single change point in a sequence of independent observations.~In the non-private setting, we derive improved finite-sample accuracy guarantees for a generalized likelihood-ratio estimator using martin...
Anuj Kumar Yadav, Cemre Cadir, Yanina Y. Shkel et al.· 0 citations
Weighted empirical risk minimization is a common approach to prediction under distribution drift. This article studies its out-of-sample prediction error under nonstationarity. We provide a general decomposition of the excess risk into a learning term and an error term associated with distribution drift, and prove orac...
Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environment is often unsafe or impractical, motivating offline reinforcement learning from historical data. However, there is limited statistical unde...
Nicolas Hoischen, Petar Bevanda, Max Beier et al.· 0 citations
Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remains limited in industries that demand interpretability. To mitigate this gap, surrogate models that e...
Bayesian optimal experimental design provides a principled framework for selecting experimental configurations that maximize obtained information. In this work, we consider settings where the likelihood entropy is independent of the design or can be evaluated explicitly, which applies, e.g. to additive noise models wit...
Chuntao Chen, Tapio Helin, Nuutti Hyv\"onen et al.· 0 citations
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are unknown, while only a control penalty function and constraints are provided. To this...
Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski et al.· 0 citations
Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solutions that represent diverse user preferences, ideally with uniform coverage of the Pareto front (PF). However, uniformly sampling scalarization weights usually induces...
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
