IEEE journal on selected areas in information theory最新文献_第3页

Addressing GAN Training Instabilities via Tunable Classification Losses 通过可调分类损失解决 GAN 训练不稳定性问题

IEEE journal on selected areas in information theory Pub Date : 2024-06-19 DOI: 10.1109/JSAIT.2024.3415670

Monica Welfert;Gowtham R. Kurri;Kyle Otstot;Lalitha Sankar

{"title":"Addressing GAN Training Instabilities via Tunable Classification Losses","authors":"Monica Welfert;Gowtham R. Kurri;Kyle Otstot;Lalitha Sankar","doi":"10.1109/JSAIT.2024.3415670","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3415670","url":null,"abstract":"Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way correspondence between CPE loss GANs and f-GANs which minimize f-divergences. We also show that all symmetric f-divergences are equivalent in convergence. In the finite sample and model capacity setting, we define and obtain bounds on estimation and generalization errors. We specialize these results to \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-GANs, defined using \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-loss, a tunable CPE loss family parametrized by \u0000<inline-formula> <tex-math>$alpha in (0,infty $ </tex-math></inline-formula>\u0000]. We next introduce a class of dual-objective GANs to address training instabilities of GANs by modeling each player’s objective using \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-loss to obtain \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000-GANs. We show that the resulting non-zero sum game simplifies to minimizing an f-divergence under appropriate conditions on \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000. Generalizing this dual-objective formulation using CPE losses, we define and obtain upper bounds on an appropriately defined estimation error. Finally, we highlight the value of tuning \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000 in alleviating training instabilities for the synthetic 2D Gaussian mixture ring as well as the large publicly available Celeb-A and LSUN Classroom image datasets.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"534-553"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Term Fairness in Sequential Multi-Agent Selection With Positive Reinforcement 带正向强化的连续多代理选择中的长期公平性

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416078

Bhagyashree Puranik;Ozgur Guldogan;Upamanyu Madhow;Ramtin Pedarsani

{"title":"Long-Term Fairness in Sequential Multi-Agent Selection With Positive Reinforcement","authors":"Bhagyashree Puranik;Ozgur Guldogan;Upamanyu Madhow;Ramtin Pedarsani","doi":"10.1109/JSAIT.2024.3416078","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416078","url":null,"abstract":"While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing sequential decision-making to positively impact long-term social fairness. In selection processes such as college admissions or hiring, biasing slightly towards applicants from under-represented groups is hypothesized to provide positive feedback that increases the pool of under-represented applicants in future selection rounds, thus enhancing fairness in the long term. In this paper, we examine this hypothesis and its consequences in a setting in which multiple agents are selecting from a common pool of applicants. We propose the Multi-agent Fair-Greedy policy, that balances greedy score maximization and fairness. Under this policy, we prove that the resource pool and the admissions converge to a long-term fairness target set by the agents when the score distributions across the groups in the population are identical. We provide empirical evidence of existence of equilibria under non-identical score distributions through synthetic and adapted real-world datasets. We then sound a cautionary note for more complex applicant pool evolution models, under which uncoordinated behavior by the agents can cause negative reinforcement, leading to a reduction in the fraction of under-represented applicants. Our results indicate that, while positive reinforcement is a promising mechanism for long-term fairness, policies must be designed carefully to be robust to variations in the evolution model, with a number of open issues that remain to be explored by algorithm designers, social scientists, and policymakers.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"424-441"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning 通过重叠分组学习控制隐私泄露传播

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416089

Shahrzad Kiani;Franziska Boenisch;Stark C. Draper

{"title":"Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning","authors":"Shahrzad Kiani;Franziska Boenisch;Stark C. Draper","doi":"10.1109/JSAIT.2024.3416089","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416089","url":null,"abstract":"Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself local. Since workers naturally form groups based on common interests and privacy policies, we are motivated to extend standard FL to reflect a setting with multiple, potentially overlapping groups. In this setup where workers can belong and contribute to more than one group at a time, complexities arise in understanding privacy leakage and in adhering to privacy policies. To address the challenges, we propose differential private overlapping grouped learning (DP-OGL), a novel method to implement privacy guarantees within overlapping groups. Under the honest-but-curious threat model, we derive novel privacy guarantees between arbitrary pairs of workers. These privacy guarantees describe and quantify two key effects of privacy leakage in DP-OGL: propagation delay, i.e., the fact that information from one group will leak to other groups only with temporal offset through the common workers and information degradation, i.e., the fact that noise addition over model updates limits information leakage between workers. Our experiments show that applying DP-OGL enhances utility while maintaining strong privacy compared to standard FL setups.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"442-463"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information Velocity of Cascaded Gaussian Channels With Feedback 带反馈的级联高斯信道的信息速度

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416310

Elad Domanovitz;Anatoly Khina;Tal Philosof;Yuval Kochman

{"title":"Information Velocity of Cascaded Gaussian Channels With Feedback","authors":"Elad Domanovitz;Anatoly Khina;Tal Philosof;Yuval Kochman","doi":"10.1109/JSAIT.2024.3416310","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416310","url":null,"abstract":"We consider a line network of nodes, connected by additive white noise channels, equipped with local feedback. We study the velocity at which information spreads over this network. For transmission of a data packet, we give an explicit positive lower bound on the velocity, for any packet size. Furthermore, we consider streaming, that is, transmission of data packets generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity, and provide an explicit lower bound. Our analysis involves applying pulse-amplitude modulation to the data (successively in the streaming case), and using linear mean-squared error estimation at the network nodes. For general white noise, we derive exponential error-probability bounds. For single-packet transmission over channels with (sub-)Gaussian noise, we show a doubly-exponential behavior, which reduces to the celebrated Schalkwijk–Kailath scheme when considering a single node. Viewing the constellation as an “analog source”, we also provide bounds on the exponential decay of the mean-squared error of source transmission over the network.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"554-569"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Distributed Source Coding 神经分布式源编码

IEEE journal on selected areas in information theory Pub Date : 2024-06-14 DOI: 10.1109/JSAIT.2024.3412976

Jay Whang;Alliot Nagle;Anish Acharya;Hyeji Kim;Alexandros G. Dimakis

引用次数: 0

Secure Source Coding Resilient Against Compromised Users via an Access Structure 安全源代码编码通过访问结构抵御受攻击用户的攻击

IEEE journal on selected areas in information theory Pub Date : 2024-06-10 DOI: 10.1109/JSAIT.2024.3410235

Hassan ZivariFard;Rémi A. Chou

引用次数: 0

Information-Theoretic Tools to Understand Distributed Source Coding in Neuroscience 理解神经科学中分布式源编码的信息论工具

IEEE journal on selected areas in information theory Pub Date : 2024-06-10 DOI: 10.1109/JSAIT.2024.3409683

Ariel K. Feldman;Praveen Venkatesh;Douglas J. Weber;Pulkit Grover

引用次数: 0

On the Fundamental Limit of Distributed Learning With Interchangable Constrained Statistics 论可互换约束统计分布式学习的基本极限

IEEE journal on selected areas in information theory Pub Date : 2024-06-04 DOI: 10.1109/JSAIT.2024.3409426

Xinyi Tong;Jian Xu;Shao-Lun Huang

{"title":"On the Fundamental Limit of Distributed Learning With Interchangable Constrained Statistics","authors":"Xinyi Tong;Jian Xu;Shao-Lun Huang","doi":"10.1109/JSAIT.2024.3409426","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3409426","url":null,"abstract":"In the popular federated learning scenarios, distributed nodes often represent and exchange information through functions or statistics of data, with communicative processes constrained by the dimensionality of transmitted information. This paper investigates the fundamental limits of distributed parameter estimation and model training problems under such constraints. Specifically, we assume that each node can observe a sequence of i.i.d. sampled data and communicate statistics of the observed data with dimensionality constraints. We first show the Cramer-Rao lower bound (CRLB) and the corresponding achievable estimators for the distributed parameter estimation problems, and the geometric insights and the computable algorithms of designing efficient estimators are also presented. Moreover, we consider model parameters training for distributed nodes with limited communicable statistics. We demonstrate that in order to optimize the excess risk, the feature functions of the statistics shall be designed along the largest eigenvectors of a matrix induced by the model training loss function. In summary, our results potentially provide theoretical guidelines of designing efficient algorithms for enhancing the performance of distributed learning systems.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"396-406"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning LightVeriFL：用于联合学习的轻量级可验证安全聚合系统

IEEE journal on selected areas in information theory Pub Date : 2024-04-29 DOI: 10.1109/JSAIT.2024.3391849

Baturalp Buyukates;Jinhyun So;Hessam Mahdavifar;Salman Avestimehr

{"title":"LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning","authors":"Baturalp Buyukates;Jinhyun So;Hessam Mahdavifar;Salman Avestimehr","doi":"10.1109/JSAIT.2024.3391849","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3391849","url":null,"abstract":"Secure aggregation protects the local models of the users in federated learning, by not allowing the server to obtain any information beyond the aggregate model at each iteration. Naively implementing secure aggregation fails to protect the integrity of the aggregate model in the possible presence of a malicious server forging the aggregation result, which motivates verifiable aggregation in federated learning. Existing verifiable aggregation schemes either have a linear complexity in model size or require time-consuming reconstruction at the server, that is quadratic in the number of users, in case of likely user dropouts. To overcome these limitations, we propose \u0000<monospace>LightVeriFL</monospace>\u0000, a lightweight and communication-efficient secure verifiable aggregation protocol, that provides the same guarantees for verifiability against a malicious server, data privacy, and dropout-resilience as the state-of-the-art protocols without incurring substantial communication and computation overheads. The proposed \u0000<monospace>LightVeriFL</monospace>\u0000 protocol utilizes homomorphic hash and commitment functions of constant length, that are independent of the model size, to enable verification at the users. In case of dropouts, \u0000<monospace>LightVeriFL</monospace>\u0000 uses a one-shot aggregate hash recovery of the dropped-out users, instead of a one-by-one recovery, making the verification process significantly faster than the existing approaches. Comprehensive experiments show the advantage of \u0000<monospace>LightVeriFL</monospace>\u0000 in practical settings.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"285-301"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions 通过辅助分布实现学习算法泛化误差边界

IEEE journal on selected areas in information theory Pub Date : 2024-04-25 DOI: 10.1109/JSAIT.2024.3391900

Gholamali Aminian;Saeed Masiha;Laura Toni;Miguel R. D. Rodrigues

{"title":"Learning Algorithm Generalization Error Bounds via Auxiliary Distributions","authors":"Gholamali Aminian;Saeed Masiha;Laura Toni;Miguel R. D. Rodrigues","doi":"10.1109/JSAIT.2024.3391900","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3391900","url":null,"abstract":"Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method, that leads to new upper bounds on expected generalization errors that are appropriate for supervised learning scenarios. We show that our general upper bounds can be specialized under some conditions to new bounds involving the \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon, \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Rényi \u0000<inline-formula> <tex-math>$(0lt alpha lt 1)$ </tex-math></inline-formula>\u0000 information between a random variable modeling the set of training samples and another random variable modeling the set of hypotheses. Our upper bounds based on \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon information are also finite. Additionally, we demonstrate how our auxiliary distribution method can be used to derive the upper bounds on excess risk of some learning algorithms in the supervised learning context and the generalization error under the distribution mismatch scenario in supervised learning algorithms, where the distribution mismatch is modeled as \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon or \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Rényi divergence between the distribution of test and training data samples distributions. We also outline the conditions for which our proposed upper bounds might be tighter than other earlier upper bounds.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"273-284"},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0