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We provide novel information-theoretic generalization bounds for stochastic gradient Langevin dynamics (SGLD) under the assumptions of smoothness and dissipativity, which are widely used in sampling and non-convex optimization studies. Our…

Machine Learning · Computer Science 2023-11-03 Futoshi Futami , Masahiro Fujisawa

Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (\cite{sugiyama2012machine}). This work takes a distributed density estimation angle to the training setting…

Machine Learning · Computer Science 2025-02-25 Behraj Khan , Behroz Mirza , Tahir Syed

In applications such as free-space optical communication, a signal is often recovered after propagation through a turbulent medium. In this setting, it is common to assume that limited information is known about the turbulent medium, such…

Optics · Physics 2025-10-13 Anjali Nair , Qin Li , Samuel N. Stechmann

Tracking of objects in cellular environments has become a vital tool in molecular cell biology. A particularly important example is single molecule tracking which enables the study of the motion of a molecule in cellular environments and…

Quantitative Methods · Quantitative Biology 2020-02-27 Milad R. Vahid , Bernard Hanzon , Raimund J. Ober

This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is…

Systems and Control · Computer Science 2014-08-20 Daphney-Stavroula Zois , Urbashi Mitra

Dynamical aspects of information-theoretic and entropic measures of quantum systems are studied. First, we show that for the time-dependent harmonic oscillator, as well as for the charged particle in certain time-varying electromagnetic…

Quantum Physics · Physics 2022-03-22 K. Andrzejewski

Classical Fisher-information asymptotics describe the covariance of regular efficient estimators through the local quadratic approximation of the log-likelihood, and thus capture first-order geometry only. In curved models, including…

Statistics Theory · Mathematics 2026-04-15 Malik Amir , Sourangshu Ghosh

In the previous papers (Kui\'{c} et al. in Found Phys 42:319-339, 2012; Kui\'{c} in arXiv:1506.02622, 2015), it was demonstrated that applying the principle of maximum information entropy by maximizing the conditional information entropy,…

Statistical Mechanics · Physics 2016-06-14 Domagoj Kuic

This paper studies the sharp interface limit for a mass conserving Allen-Cahn equation added an external noise and derives a stochastically perturbed mass conserving mean curvature flow in the limit. The stochastic term destroys the precise…

Probability · Mathematics 2016-12-30 Tadahisa Funaki , Satoshi Yokoyama

Information geometry is the application of differential geometry in statistics, where the Fisher-Rao metric serves as the Riemannian metric on the statistical manifold, providing an intrinsic property for parameter sensitivity. In this…

Quantum Physics · Physics 2024-07-25 Wangjun Lu , Zhao-Hui Peng , HongTao

We examine birth--death processes with state dependent transition probabilities and at least one absorbing boundary. In evolution, this describes selection acting on two different types in a finite population where reproductive events occur…

Populations and Evolution · Quantitative Biology 2010-10-12 Philipp M. Altrock , Chaytanya S. Gokhale , Arne Traulsen

The Tsallis entropy and Fisher information entropy (matrix) are very important quantities expressing information measures in nonextensive systems. Stationary and dynamical properties of the information entropies have been investigated in…

Statistical Mechanics · Physics 2009-11-13 Hideo Hasegawa

In open quantum systems, we study the quantum Fisher information of acceleration for a uniformly accelerated two-level atom coupled to fluctuating electromagnetic fields in the Minkowski vacuum. With the time evolution, for the initial atom…

Quantum Physics · Physics 2022-09-27 Mengge Zheng , Baoyuan Yang , Zixu Zhao

We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively…

Information Theory · Computer Science 2012-06-19 Swarnendu Kar , Pramod K. Varshney , Marimuthu Palaniswami

We consider weakly interacting jump processes on time-varying random graphs with dynamically changing multi-color edges. The system consists of a large number of nodes in which the node dynamics depends on the joint empirical distribution…

Probability · Mathematics 2021-07-19 Erhan Bayraktar , Ruoyu Wu

Ecological and evolutionary processes show various population dynamics depending on internal interactions and environmental changes. While crucial in predicting biological processes, discovering general relations for such nonlinear dynamics…

Biological Physics · Physics 2022-06-23 Kyosuke Adachi , Ryosuke Iritani , Ryusuke Hamazaki

Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential…

Disordered Systems and Neural Networks · Physics 2023-11-20 Julian Arnold , Niels Lörch , Flemming Holtorf , Frank Schäfer

Many records in environmental sciences exhibit asymmetric trajectories and there is a need for simple and tractable models which can reproduce such features. In this paper we explore an approach based on applying both a time change and a…

Methodology · Statistics 2015-10-09 Pierre Ailliot , Bernard Delyon , Valérie Monbet , Marc Prevosto

We examine the relationship between the mutual information between the output model and the empirical sample and the generalization of the algorithm in the context of stochastic convex optimization. Despite increasing interest in…

Machine Learning · Computer Science 2024-01-17 Roi Livni

Advanced super-resolution imaging techniques require specific approaches for accurate and consistent estimation of the achievable spatial resolution. Fisher information supplied to Cramer-Rao bound (CRB) has proved to be a powerful and…