Provably Efficient Methods For Large Scale Learning

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Provably Efficient Methods For Large Scale Learning

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Author : Shuo Yang (Ph. D.)
language : en
Publisher:
Release Date : 2023

Provably Efficient Methods For Large Scale Learning written by Shuo Yang (Ph. D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

The scale of machine learning problems grows rapidly in recent years and calls for efficient methods. In this dissertation, we propose simple and efficient methods for various large-scale learning problems. We start with a standard supervised learning problem of solving quadratic regression. In Chapter 2, we show that by utilizing the quadratic structure and a novel gradient estimation algorithm, we can solve sparse quadratic regression with sub-quadratic time complexity and near-optimal sample complexity. We then move to online learning problems. In Chapter 3, we identify a weak assumption and theoretically prove that the standard UCB algorithm efficiently learns from inconsistent human preferences with nearly optimal regret; in Chapter 4 we propose an approximate maximum inner product search data structure for adaptive queries and present two efficient algorithms that achieve sublinear time complexity for linear bandits, which is especially desirable for extremely large and slowly changing action sets. In Chapter 5, we study how to efficiently use privileged features with deep learning models. We present an efficient learning algorithm to exploit privileged features that are not available during testing time. We conduct comprehensive empirical evaluations and present rigorous analysis for linear models to build theoretical insights. It provides a general algorithmic paradigm that can be integrated with many other machine learning methods

Large Scale Optimization Methods For Metric And Kernel Learning

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Author : Prateek Jain
language : en
Publisher:
Release Date : 2009

Large Scale Optimization Methods For Metric And Kernel Learning written by Prateek Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.

A large number of machine learning algorithms are critically dependent on the underlying distance/metric/similarity function. Learning an appropriate distance function is therefore crucial to the success of many methods. The class of distance functions that can be learned accurately is characterized by the amount and type of supervision available to the particular application. In this thesis, we explore a variety of such distance learning problems using different amounts/types of supervision and provide efficient and scalable algorithms to learn appropriate distance functions for each of these problems. First, we propose a generic regularized framework for Mahalanobis metric learning and prove that for a wide variety of regularization functions, metric learning can be used for efficiently learning a kernel function incorporating the available side-information. Furthermore, we provide a method for fast nearest neighbor search using the learned distance/kernel function. We show that a variety of existing metric learning methods are special cases of our general framework. Hence, our framework also provides a kernelization scheme and fast similarity search scheme for such methods. Second, we consider a variation of our standard metric learning framework where the side-information is incremental, streaming and cannot be stored. For this problem, we provide an efficient online metric learning algorithm that compares favorably to existing methods both theoretically and empirically. Next, we consider a contrasting scenario where the amount of supervision being provided is extremely small compared to the number of training points. For this problem, we consider two different modeling assumptions: 1) data lies on a low-dimensional linear subspace, 2) data lies on a low-dimensional non-linear manifold. The first assumption, in particular, leads to the problem of matrix rank minimization over polyhedral sets, which is a problem of immense interest in numerous fields including optimization, machine learning, computer vision, and control theory. We propose a novel online learning based optimization method for the rank minimization problem and provide provable approximation guarantees for it. The second assumption leads to our geometry-aware metric/kernel learning formulation, where we jointly model the metric/kernel over the data along with the underlying manifold. We provide an efficient alternating minimization algorithm for this problem and demonstrate its wide applicability and effectiveness by applying it to various machine learning tasks such as semi-supervised classification, colored dimensionality reduction, manifold alignment etc. Finally, we consider the task of learning distance functions under no supervision, which we cast as a problem of learning disparate clusterings of the data. To this end, we propose a discriminative approach and a generative model based approach and we provide efficient algorithms with convergence guarantees for both the approaches.

Machine Learning Ecml 2006

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Author : Johannes Fürnkranz
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-19

Machine Learning Ecml 2006 written by Johannes Fürnkranz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-19 with Computers categories.

This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.

Statistics In Precision Health

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Author : Yichuan Zhao
language : en
Publisher: Springer Nature
Release Date :

Statistics In Precision Health written by Yichuan Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Computer Vision Eccv 2018

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Author : Vittorio Ferrari
language : en
Publisher: Springer
Release Date : 2018-10-05

Computer Vision Eccv 2018 written by Vittorio Ferrari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-05 with Computers categories.

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Proceedings Of The International Congress Of Mathematicians 2018 Icm 2018 In 4 Volumes

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Author : Sirakov Boyan
language : en
Publisher: World Scientific
Release Date : 2019-02-27

Proceedings Of The International Congress Of Mathematicians 2018 Icm 2018 In 4 Volumes written by Sirakov Boyan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-27 with Mathematics categories.

The Proceedings of the ICM publishes the talks, by invited speakers, at the conference organized by the International Mathematical Union every 4 years. It covers several areas of Mathematics and it includes the Fields Medal and Nevanlinna, Gauss and Leelavati Prizes and the Chern Medal laudatios.

Machine Learning With Provable Robustness Guarantees

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Author : Huan Zhang
language : en
Publisher:
Release Date : 2020

Machine Learning With Provable Robustness Guarantees written by Huan Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Although machine learning has achieved great success in numerous complicated tasks, many machine learning models lack robustness under the presence of adversaries and can be misled by imperceptible adversarial noises. In this dissertation, we first study the robustness verification problem of machine learning, which gives provable guarantees on worst case performance under arbitrarily strong adversaries. We study two popular machine learning models, deep neural networks (DNNs) and ensemble trees, and design efficient and effective algorithms to provably verify the robustness of these models. For neural networks, we develop a linear relaxation based framework, CROWN, where we relax the non-linear units in DNNs using linear bounds, and propagate linear bounds through the network. We generalize CROWN into a linear relaxation based perturbation analysis (LiRPA) algorithm on any computational graphs and general network architectures to handle irregular neural networks used in practice, and released an open source software package, auto_LiRPA, to facilitate the use of LiRPA for researchers in other fields. For tree ensembles, we reduce the robustness verification algorithm to a max-clique finding problem on a specially created graph, which is very efficient compared to existing approaches and can produce high quality lower or upper bounds for the output of a tree ensemble based classifier. After developing our robustness verification algorithms, we utilize them to create a certified adversarial defense for neural networks, where we explicitly optimize the bounds obtained from verification to greatly improve network robustness in a provable manner. Our LiRPA based training method is very efficient: it can scale to large datasets such as downscaled ImageNet and modern computer vision models such as DenseNet. Lastly, we study the robustness of reinforcement learning (RL), which is more challenging than the problem in supervised learning settings. We focus on the robustness of state observations for a RL agent, and develop the state-adversarial Markov decision process (SA-MDP) to characterize the behavior of a RL agent under adversarially perturbed observations. Based on SA-MDP, we develop two orthogonal approaches to improve the robustness of RL: a state-adversarial regularization helping to improve the robustness of function approximators, and alternating training with learned adversaries (ATLA) to mitigate the intrinsic weakness in a policy. Both approaches are evaluated in various simulated environments and they significantly improve the robustness of RL agents under strong adversarial attacks, including a few novel adversarial attacks proposed by us.

Learning For Decision And Control In Stochastic Networks

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Author : Longbo Huang
language : en
Publisher: Springer Nature
Release Date : 2023-07-21

Learning For Decision And Control In Stochastic Networks written by Longbo Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-21 with Technology & Engineering categories.

This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges.

Understanding Machine Learning

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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Distributed Optimization In Networked Systems

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Author : Qingguo Lü
language : en
Publisher: Springer Nature
Release Date : 2023-02-08

Distributed Optimization In Networked Systems written by Qingguo Lü and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-08 with Computers categories.

This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.