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Related papers: Stochastic Enumeration with Importance Sampling

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In this paper, we investigate the stochastic evolution equations (SEEs) driven by $\log$-Whittle-Mat$\acute{{\mathrm{e}}}$rn (W-M) random diffusion coefficient field and $Q$-Wiener multiplicative force noise. First, the well-posedness of…

Numerical Analysis · Mathematics 2022-07-05 X. Qi , M. Azaiez , C. Huang , C. Xu

In this paper, we consider the numerical approximation of a general second order semilinear stochastic partial differential equation (SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear…

Numerical Analysis · Mathematics 2020-11-19 Jean Daniel Mukam , Antoine Tambue

Supervised deep-embedding methods project inputs of a domain to a representational space in which same-class instances lie near one another and different-class instances lie far apart. We propose a probabilistic method that treats…

Machine Learning · Statistics 2019-09-27 Tyler R. Scott , Karl Ridgeway , Michael C. Mozer

Nowadays new technologies, and especially artificial intelligence, are more and more established in our society. Big data analysis and machine learning, two sub-fields of artificial intelligence, are at the core of many recent breakthroughs…

Machine Learning · Statistics 2021-06-22 Antonio Sutera

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces…

Robotics · Computer Science 2023-05-26 Chenming Li , Fei Meng , Han Ma , Jiankun Wang , Max Q. -H. Meng

We propose a new type of approximate counting algorithms for the problems of enumerating the number of independent sets and proper colorings in low degree graphs with large girth. Our algorithms are not based on a commonly used Markov chain…

Probability · Mathematics 2007-05-23 Antar Bandyopadhyay , David Gamarnik

Stochastic convex optimization is one of the most well-studied models for learning in modern machine learning. Nevertheless, a central fundamental question in this setup remained unresolved: "How many data points must be observed so that…

Machine Learning · Computer Science 2023-11-10 Daniel Carmon , Roi Livni , Amir Yehudayoff

In 2012, Pflug and Pichler proved, under regularity assumptions, that the value function in Multistage Stochastic Programming (MSP) is Lipschitz continuous w.r.t. the Nested Distance, which is a distance between scenario trees (or discrete…

Optimization and Control · Mathematics 2021-07-22 Zheng Qu , Benoît Tran

A classical problem in matrix computations is the efficient and reliable approximation of a given matrix by a matrix of lower rank. The truncated singular value decomposition (SVD) is known to provide the best such approximation for any…

Numerical Analysis · Mathematics 2014-08-12 Ming Gu

This paper revisits the well known single machine scheduling problem to minimize total weighted completion times. The twist is that job sizes are stochastic from unknown distributions, and the scheduler has access to only a single sample…

Data Structures and Algorithms · Computer Science 2023-08-23 Puck te Rietmole , Marc Uetz

Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (soft) size limit. Rather than relying on a single "best"…

Machine Learning · Computer Science 2025-11-06 Elif Arslan , Jacobus G. M. van der Linden , Serge Hoogendoorn , Marco Rinaldi , Emir Demirović

Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle…

Statistics Theory · Mathematics 2023-05-30 Hang Yu , Zhenxing Dou , Zhiwei Chen , Xiaomeng Yan

We describe an adaptive importance sampling algorithm for rare events that is based on a dual stochastic control formulation of a path sampling problem. Specifically, we focus on path functionals that have the form of cumulate generating…

Dynamical Systems · Mathematics 2019-01-30 Omar Kebiri , Lara Neureither , Carsten Hartmann

Stochastic approximation (SA) is a powerful class of iterative algorithms for nonlinear root-finding that can be used for minimizing a loss function, $L(\boldsymbol{\theta})$, with respect to a parameter vector $\boldsymbol{\theta}$, when…

Optimization and Control · Mathematics 2017-07-24 Karla Hernández Cuevas

In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy…

Machine Learning · Computer Science 2020-12-30 Antoine Blanchard , Themistoklis Sapsis

Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble…

Machine Learning · Statistics 2024-06-21 Alexandre Seiller , Éric Gaussier , Emilie Devijver , Marianne Clausel , Sami Alkhoury

Assessing sampling uncertainty in extremum estimation can be challenging when the asymptotic variance is not analytically tractable. Bootstrap inference offers a feasible solution but can be computationally costly especially when the model…

Econometrics · Economics 2020-09-15 Jean-Jacques Forneron , Serena Ng

We study several stochastic combinatorial problems, including the expected utility maximization problem, the stochastic knapsack problem and the stochastic bin packing problem. A common technical challenge in these problems is to optimize…

Data Structures and Algorithms · Computer Science 2013-03-20 Jian Li , Wen Yuan

The automaton constrained tree knapsack problem is a variant of the knapsack problem in which the items are associated with the vertices of the tree, and we can select a subset of items that is accepted by a top-down tree automaton. If the…

Data Structures and Algorithms · Computer Science 2018-09-18 Soh Kumabe , Takanori Maehara , Ryoma Sin'ya

In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in…

Artificial Intelligence · Computer Science 2013-04-05 R. Martin Chavez , Gregory F. Cooper