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arXiv 2023年

作者： Wang, Guanghui Hu, Zihao Gentile, Claudio Muthukumar, Vidya Abernethy, Jacob College of Computing Georgia Institute of Technology United States Google Research New York United States School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States Google Research Atalanta Brazil

First-order optimization methods tend to inherently favor certain solutions over others when minimizing an underdetermined training objective that has multiple global optima. This phenomenon, known as implicit bias, plays a critical role in understanding the generalization capabilities of optimization algorithms. Recent research has revealed that in separable binary classification tasks gradient-descent-based methods exhibit an implicit bias for the 2-maximal margin classifier. Similarly, generic optimization methods, such as mirror descent and steepest descent, have been shown to converge to maximal margin classifiers defined by alternative geometries. While gradient-descent-based algorithms provably achieve fast implicit bias rates, corresponding rates in the literature for generic optimization methods are relatively slow. To address this limitation, we present a series of state-of-the-art implicit bias rates for mirror descent and steepest descent algorithms. Our primary technique involves transforming a generic optimization algorithm into an online optimization dynamic that solves a regularized bilinear game, providing a unified framework for analyzing the implicit bias of various optimization methods. Our accelerated rates are derived by leveraging the regret bounds of online learning algorithms within this game framework. We then show the flexibility of this framework by analyzing the implicit bias in adversarial training, and again obtain significantly improved convergence rates. Copyright © 2023, The Authors. All rights reserved.

关键词： Mirrors

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Value of Information in the Mean-Square Case and its Application to the Analysis of Financial Time-Series Forecast

arXiv

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arXiv 2024年

作者： Belavkin, Roman V. Pardalos, Panos Principe, Jose Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom Department of Industrial and Systems Engineering University of Florida P.O. Box 116595 GainesvilleFL32611-6595 United States Department of Electrical & Computer Engineering University of Florida P.O. Box 116130 GainesvilleFL32611-6130 United States

The advances and development of various machine learning techniques has lead to practical solutions in various areas of science, engineering, medicine and finance. The great choice of algorithms, their implementations and libraries has resulted in another challenge of selecting the right algorithm and tuning their parameters in order to achieve optimal or satisfactory performance in specific applications. Here we show how the value of information (V (I)) can be used in this task to guide the algorithm choice and parameter tuning process. After estimating the amount of Shannon’s mutual information between the predictor and response variables, V (I) can define theoretical upper bound of performance of any algorithm. The inverse function I(V ) defines the lower frontier of the minimum amount of information required to achieve the desired performance. In this paper, we illustrate the value of information for the mean-square error minimization and apply it to forecasts of cryptocurrency *** Codes 94A15, 94A17, 94A34, 62J12, 62M45, 60G25 Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy systems

arXiv

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arXiv 2022年

作者： Wu, Jiaxin Chen, Xin Badakhshan, Sobhan Zhang, Jie Wang, Pingfeng Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Engineering Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included, the complexity of the hybrid energy system becomes more significant. And the complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience. Among different strategies to ensure a reliable system, intentional islanding is commonly applied in practical applications for power systems and attracts abundant interest in the literature. In this study, we propose a hierarchical spectral clustering-based intentional islanding strategy at the transmission level with renewable generations. To incorporate the renewable generation that relies on the inverter technology, the frequency measurements are considered to represent the transient response. And it has been further used as embedded information in the clustering algorithm along with other important electrical information from the system to enrich the modeling capability of the proposed framework. To demonstrate the effectiveness of the islanding strategy, the modified IEEE-9 bus and IEEE-118 bus systems coupled with wind farms are considered as the test cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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Repairing with Zero Skip Cost

Repairing with Zero Skip Cost

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IEEE International Symposium on Information Theory

作者： Yeow Meng Chee Son Hoang Dau Tuvi Etzion Han Mao Kiah Yuan Luo Wenqin Zhang Dept. of Industrial Systems Engineering and Management National University of Singapore School of Computing Technologies RMIT University Melbourne VIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio... 详细信息

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

关键词： Measurement Costs Codes Maintenance engineering

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PRIMAL-DUAL FIRST-ORDER METHODS FOR AFFINELY CONSTRAINED MULTI-BLOCK SADDLE POINT PROBLEMS

arXiv

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arXiv 2021年

作者： Zhang, Junyu Wang, Mengdi Hong, Mingyi Zhang, Shuzhong Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical and Computer Engineering Princeton University United States Department of Electrical and Computer Engineering University of Minnesota United States Department of Industrial and Systems Engineering University of Minnesota United States

We consider the convex-concave saddle point problem minx maxy Φ(x, y), where the decision variables x and/or y are subject to certain multi-block structure and affine coupling constraints, and Φ(x, y) possesses certain separable structure. Although the minimization counterpart of this problem has been widely studied under the topics of ADMM, this minimax problem is rarely investigated. In this paper, a convenient notion of ∈-saddle point is proposed, under which the convergence rate of several proposed algorithms are analyzed. When only one of x and y has multiple blocks and affine constraint, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness, O(1/T) or O(1/√T) convergence rates are derived for our algorithms. When both x and y have multiple blocks and affine constraints, a new algorithm called Extra-Gradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in x and y. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the advantage of the EGMM. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Impact of Aviation Electrification on Airports: Flight Scheduling and Charging

SSRN

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SSRN 2022年

作者： Hou, Boya Bose, Subhonmesh Marla, Lavanya Haran, Kiruba Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States College of Engineering Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Electrification can help to reduce the carbon footprint of aviation. The transition away from the jet fuel-powered conventional airplane towards battery-powered electrified aircraft will impose extra charging requirements on airports. In this paper, we first quantify the increase in energy demands at several airports across the United States (US), when commercial airline carriers partially deploy hybrid electric aircraft (HEA). We then illustrate that smart charging and minor modifications to flight schedules can substantially reduce peak power demands, and in turn the needs for grid infrastructure upgrade. Motivated by our data analysis, we then formulate an optimization problem for flight rescheduling that incorporates HEA charging considerations. This problem jointly decides flight schedules and charging profiles to manage airport congestion and peak power demands. We further consider mechanisms via which airlines and airports can negotiate HEA assignments using said optimization problem. Finally, we illustrate the efficacy of our formulation through a case study on the John F. Kennedy International Airport. © 2022, The Authors. All rights reserved.

关键词： Airports

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Impact of Aviation Electrification on Airports: Flight Scheduling and Charging

SSRN

引用

SSRN 2022年

作者： Hou, Boya Bose, Subhonmesh Marla, Lavanya Haran, Kiruba College of Engineering Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

关键词： Electric utilities

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Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

arXiv

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arXiv 2022年

作者： Huo, Zepeng Qian, Xiaoning Huang, Shuai Wang, Zhangyang Mortazavi, Bobak J. Computer Science & Engineering Department Texas A&M University College StationTX United States Electrical & Computer Engineering Department Texas A&M University College StationTX United States Industrial & Systems Engineering Department University of Washington SeattleWA United States Electrical & Computer Engineering Department University of Texas AustinTX United States

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy;2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain. © 2022, CC BY.

关键词： Medical computing

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Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Integrated Modeling and Optimal Operation of Multi-Energy Sy...

引用

IEEE International Conference on Electro-Information Technology

作者： Yang Chen Jun Chen Chenang Liu Guodong Liu Maximiliano Ferrari Aditya Sundararajan Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University Greensboro USA Department of Electrical and Computer Engineering Oakland University Rochester MI USA School of Industrial Engineeering & Management Oklahoma State University Stillwater USA Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak Ridge TN USA

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands.

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Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

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