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Related papers: Reusing Samples in Variance Reduction

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Sequential minimum optimization is a machine-learning global search training algorithm. It is applicable when the functional dependence of the cost function on a tunable parameter given the other parameters can be cheaply determined. This…

Quantum Physics · Physics 2023-03-03 Wojciech Roga , Takafumi Ono , Masahiro Takeoka

The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems…

Machine Learning · Statistics 2019-06-24 Robin Vogel , Aurélien Bellet , Stephan Clémençon , Ons Jelassi , Guillaume Papa

Generalized singular values (GSVs) play an essential role in the comparative analysis. In the real world data for comparative analysis, both data matrices are usually numerically low-rank. This paper proposes a randomized algorithm to first…

Numerical Analysis · Mathematics 2024-04-16 Weiwei Xu , Weijie Shen , Wen Li , Weiguo Gao , Yingzhou Li

Recently, Sidford, Wang, Wu and Ye (2018) developed an algorithm combining variance reduction techniques with value iteration to solve discounted Markov decision processes. This algorithm has a sublinear complexity when the discount factor…

Optimization and Control · Mathematics 2019-09-16 Marianne Akian , Stéphane Gaubert , Zheng Qu , Omar Saadi

We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom-up approach is based on a meta-topology in which each link…

Optimization and Control · Mathematics 2023-02-17 Ion Matei , Maksym Zhenirovskyy , John Maxwell , Johan de Kleer

Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a…

Computer Vision and Pattern Recognition · Computer Science 2025-08-22 Gaurav Parmar , Or Patashnik , Daniil Ostashev , Kuan-Chieh Wang , Kfir Aberman , Srinivasa Narasimhan , Jun-Yan Zhu

Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned…

Computer Vision and Pattern Recognition · Computer Science 2022-04-01 Zhi Hou , Baosheng Yu , Dacheng Tao

Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent the prohibitive sample cost, many recent work focus on…

Machine Learning · Computer Science 2019-12-03 San Gultekin , John Paisley

Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random…

Computational Complexity · Computer Science 2010-03-09 Guy Bresler , Elchanan Mossel , Allan Sly

With appropriately chosen sampling probabilities, sampling-based random projection can be used to implement large-scale statistical methods, substantially reducing computational cost while maintaining low statistical error. However,…

Machine Learning · Statistics 2026-01-13 Yifan Chen , Yun Yang

Optimization software enables the solution of problems with millions of variables and associated parameters. These parameters are, however, often uncertain and represented with an analytical description of the parameter's distribution or…

Optimization and Control · Mathematics 2025-01-17 John R. Birge

We introduce an optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations. The effectiveness of our approach is shown by obtaining…

We consider the problem of model multiplicity in downstream decision-making, a setting where two predictive models of equivalent accuracy cannot agree on the best-response action for a downstream loss function. We show that even when the…

Machine Learning · Computer Science 2024-05-31 Ally Yalei Du , Dung Daniel Ngo , Zhiwei Steven Wu

Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. Methods for variational inequalities are therefore universal approaches for many applied tasks, including…

Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning. While behavior cloning is…

Machine Learning · Computer Science 2024-11-05 Jonathan Pirnay , Dominik G. Grimm

Variational inequalities are a universal optimization paradigm that is interesting in itself, but also incorporates classical minimization and saddle point problems. Modern realities encourage to consider stochastic formulations of…

Optimization and Control · Mathematics 2024-03-27 Alexander Pichugin , Maksim Pechin , Aleksandr Beznosikov , Alexander Gasnikov

This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e.,…

Systems and Control · Electrical Eng. & Systems 2024-01-18 Geethu Joseph , Shana Moothedath , Jiabin Lin

We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where…

Machine Learning · Computer Science 2024-05-16 Stefan Pricopie , Richard Allmendinger , Manuel Lopez-Ibanez , Clyde Fare , Matt Benatan , Joshua Knowles

Neural Combinatorial Optimization aims to learn to solve a class of combinatorial problems through data-driven methods and notably through employing neural networks by learning the underlying distribution of problem instances. While, so far…

Machine Learning · Computer Science 2025-08-05 Daniela Thyssens , Tim Dernedde , Wilson Sentanoe , Lars Schmidt-Thieme

We investigate in this paper an alternative method to simulation based recursive importance sampling procedure to estimate the optimal change of measure for Monte Carlo simulations. We propose an algorithm which combines (vector and…

Probability · Mathematics 2011-09-20 Noufel Frikha , Abass Sagna
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