23 papers · ranked by Valyu relevance
Seo Taek Kong, R. Srikant
This paper derives non-asymptotic error bounds for nonlinear stochastic approximation algorithms in the Wasserstein-p distance. To obtain explicit finite-sample guarantees for the last iterate, we develop a coupling argument that compares the discrete-time process to a limiting Ornstein-Uhlenbeck process. Our analysis…
Shubhada Agrawal, Siva Theja Maguluri, Martin Zubeldia
We establish maximal concentration bounds for the iterates generated by stochastic approximation algorithms with general step sizes, where the noise has a finite-state Markovian component plus a Martingale-difference component. When the Martingale-difference noise is bounded, we show that the tail of the error can be…
Kim Do Hyun, Cetinkaya, Ahemt
We propose a method to approximate continuous-time, continuous-state stochastic processes by a discrete-time Markov chain defined on a nonuniform grid. Our method provides exact moment matching for processes whose first and second moments are linear functions of time. In particular, we show that, under certain…
Hissah Albaqami, Mehdi Mrad, Anis Gharbi, Munevver Mine Subasi + 1 more
This paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and sales under random market conditions such as interest rate…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Alex N Popinga, Jack Forman, Dmitri Svetlov, Huy Vo + 1 more
Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and…
Tom Kimpson, Jennifer Flegg, Matthew J. Simpson
Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of migration to experimental data remains a major challenge. Scratch and barrier assays are widely used to study collective cell spreading, and agent-based random walk…
Martin Kjøllesdal Johnsrud, Navdeep Rana
We present stochastic variants of the exponential time differencing schemes for stiff stochastic differential equations. We derive three explicit schemes that offer better stability compared to Euler-Maruyama and Milstein's method, and achieve strong convergence up to order O(h) in the time step h. We combine these…
Philip de Bruin, Bram Elderhorst, Marjan van den Akker, Han Hoogeveen
In scheduling problems, deterministic task durations are often assumed. This usually does not capture reality and may lead to schedules that are not robust to (small) changes to these task lengths. The use of stochastic task durations therefore seems preferable. Including these in local search, which is the way to find…
Dai Shi, Lequan Lin, Andi Han, Luke Thompson + 3 more
Stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs) are fundamental for modeling stochastic dynamics across the natural sciences and modern machine learning. Learning their solution operators with deep learning models promises fast solvers and new perspectives on classical…
Michael Whitehouse
An assumed density approximate likelihood is derived for a class of partially observed stochastic compartmental models which permit observational over-dispersion. This is achieved by treating time-varying reporting probabilities as latent variables and integrating them out using Laplace approximations within Poisson…
Eriko Kaminishi, Takashi Mori, Michihiko Sugawara, Naoki Yamamoto
Stochastic gradient descent (SGD) is a widely used optimization technique in classical machine learning and the Variational Quantum Eigensolver (VQE). In VQE implementations on quantum hardware, measurement shot noise is inevitable. We analyze how this noise affects optimization dynamics, especially escape from saddle…
Marian Huot, Marco Molari, Rémi Monasson, Simona Cocco
Affinity maturation is a stochastic evolutionary process allowing the adaptive immune system to produce B-cells capable of recognizing antigenic molecules. One of the main factors influencing the quality of the maturation outcome, quantified by the affinity of the produced antibodies to the antigen, is the time-course…
Mehmet Sıddık Çadırcı, Martin Singull, Michel Broniatowski
We study nearest-neighbor-based estimators of Tsallis entropy associated with Poisson and binomial point processes on general metric measure spaces. In this study, by combining existing stabilization methods with the validation of the estimator’s local k-nearest-neighbor structure, we investigate nearest-neighbor-based…
Authors not listed
Exploring the potential energy surface to sample transition state regions is crucial to understand the atomic processes that govern chemical reactivity. Ideally, the exploration does not require any collective variables that are based on prior chemical domain knowledge. With this in mind, we adapt the stochastic saddle…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Damian Clancy
We consider stochastic population processes that are almost surely absorbed at the origin within finite time. Our interest is in the quasistationary distribution, ${\varvec{u}}$, and the expected time, $\tau$, from quasistationarity to extinction, both of which we study via WKB approximation. This approach involves…
Adarshkrishnan Rajakumar, Pascal R. Buenzli, Matthew J. Simpson
Understanding and predicting extinction risk is a central challenge in population biology. Mathematical models incorporating Allee thresholds are commonly used to understand population dynamics and to assess extinction risks. Inaccurate predictions can have serious consequences for conservation management. In this…
Sanjay Kumar Mohanty
The explainable artificial intelligence is used to analyze the stochastic Fredholm integral equations (SFIEs) and stochastic deep neural networks (SDNNs). The neural operator-based stochastic fixed point framework is used to develop SDNNs. The solution of an SFIE is obtained through successive applications of an…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Xiaotong Fang, Payam Piray
Inferring the true cause of noise—distinguishing between volatility (environmental change) and stochasticity (outcome randomness)—is essential for learning in noisy environments. While most studies rely on binary outcomes, previous models are designed for continuous outcome and use ad hoc approximations to handle…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…