The international conference on learning representations iclr is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. A complex system with constituent spatiotemporal subsystems. Special issue information theory in machine learning and. Note that entropy does not depend on actual values, but just the probability distribution. An informationtheoretic view on language complexity and. Advances in neural information processing systems 23 nips 2010 pdf bibtex.
Ieee transactions on evolutionary computation, accepted, 2019. Bibsonomy helps you to manage your publications and bookmarks, to collaborate with your colleagues and to find new interesting material for your research. An informationtheoretic toolkit for studying the dynamics of complex systems joseph t. A novel information theoreticinteract algorithm itin.
We tackle this problem under a transfer learning framework. A relative complexity metric for decisiontheoretic applications 283 figure 1. However, the label information may be insufficient in. This document includes links to several papers authored or coauthored by joao pedro hespanha the papers are in pdf or compressed gzip postscript form. It is particularly useful in large scale applications like searching for an image that. How is information theoretic metric learning algorithm abbreviated. We describe an algorithm for general marl, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game theoretic analysis to compute metastrategies for policy selection. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. The link above is to the mit press page that provides a brief description of the book and ordering information. Davis, brian kulis, suvrit sra, and inderjit dhillon informationtheoretic metric learning.
We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. Decision tree learning is a method commonly used in data mining. Itml stands for information theoretic metric learning algorithm. Grounded sensorimotor interaction histories in an information theoretic metric space for robot ontogeny by n. Dec 19, 2012 the basic ingredients of the entropy method are discussed first in the context of logarithmic sobolev inequalities, which underlie the socalled functional approach to concentration of measure, and then from a complementary information theoretic viewpoint based on transportationcost inequalities and probability in metric spaces. We formulate the problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the distance function. Towards an information theoretic metric for anonymity. Our metric learning problem is formulated as a quadratic semidefinite. The arxiv id properly formatted is sufficient to uniquely identify a. Distance metric learning, kernel methods, reproducing kernel. We propose a general information theoretic approach called seraph semisupervised metric learning paradigm with hypersparsity for metric learning that does not rely upon the manifold assumption. The landmark event that established the discipline of information theory and brought it to immediate worldwide attention was the publication of claude e. We focus on the bias in the informationtheoretic error arising from finite sampling effects, and find an appropriate local metric that maximally. Sanjit seshias publications classified by publication type.
Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. Grounded sensorimotor interaction histories in an information theoretic metric space for robot ontogeny. Davis, brian kulis, prateek jain, suvrit sra, inderjit s. Fundamental informationtheoretic limits of machine learning algorithms. Itml is defined as information theoretic metric learning algorithm rarely. Iclr is globally renowned for presenting and publishing cuttingedge research on all aspects of deep learning used in the fields of. Part of the lecture notes in computer science book series lncs, volume. Given a mahalanobis distance parameterized by a, we ex. Virginia estellers casas over the last years, the availability of devices for the acquisition of threedimensional data like laserscanners, rgbd vision or medical imaging devices has dramatically increased.
We introduce a new metric, jointpolicy correlation, to quantify this effect. Davis, brian kulis, suvrit sra, and inderjit dhillon the university of texas at austin december 9, 2006 presenter. Semisupervised metric learning using pairwise constraints. Learning robust distance metric with side information via ratio minimization of orthogonally constrained l21norm distances kai liu, lodewijk brand, hua wang, feiping nie pdf details. Outlineintroduction information dynamicsestimatorsoverview of jidtdemosexercisewrapup jidt. Itl uses descriptors from information theory entropy and divergences estimated directly from the data to substitute the conventional statistical descriptors of variance and covariance. Mutual information mi is used to describe the association between genetic marker and gene expression. Lizier the university of sydney european conference on ariti cal life ecal york, uk july 20, 2015. Dhillon, title informationtheoretic metric learning, booktitle in nips 2006 workshop on learning to compare examples, year 2007. Nov 02, 2017 we introduce a new metric, jointpolicy correlation, to quantify this effect. Request pdf informationtheoretic metric learning in this paper, we present an information theoretic approach to learning a mahalanobis distance function. Russ and gurmeet singh and marc spraragen and garrick staples and karan vahi, url. Information theory studies the quantification, storage, and communication of information.
In recent years, deep metric learning, which provides a better solution for. Itml information theoretic metric learning algorithm. Davis and brian kulis and prateek jain and suvrit sra and inderjit s. In addition, we show that if the law of the process is.
Zimo liu, dong wang, huchuan lu, stepwise metric promotion for unsupervised video person reidentification, iccv2017, pdf lijun wang, huchuan lu, yifan wang, mengyang feng, dong wang, baocai yin and xiang ruan,learning to detect salient objects with imagelevel supervision,cvpr2017,p37963805 pdf. The goal of this article is to develop a framework for large margin classification in metric spaces. Pdf informationtheoretic semisupervised metric learning. Nonlinear metric learning chapter pdf available in advances in neural information processing systems 4. Grounded sensorimotor interaction histories in an information. From the strategy, we derive a measure that establishes a metric in the state space. We describe an algorithm for general marl, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game. Dear colleagues, the field of machine learning and data science is concerned with the design and analysis of algorithms for making decisions, reasoning about the world, and knowledge extraction from massive amounts of data. Classspecific mahalanobis distance metric learning for biological. My group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control. A new simulation metric to determine safe environments and. Baligh alhelali, qi chen, bing xue, and mengjie zhang. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications.
Springer series in statistics springer new york inc. Human reidentification with transferred metric learning. To include other references, use the \nocite command. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and. In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e. We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate gaussians under constraints on the mahalanobis distance function. Itml is a matlab implementation of information theoretic metric learning algorithm. The goal is to create a model that predicts the value of a target variable based on several input variables. Informationtheoretic semisupervised metric learning via. Hydrology, immuno, informatics, information, infrastructures, inorganics. Metric learning involves finding a suitable metric for a given set of datapoints with sideinformation regarding distances between few datapoints. Informationtheoretic methods in statistical machine learning martin wainwright ucberkeley departmentofeecs,anddepartmentofstatistics based on joint work with. A weighted maximum margin metric is online learned and transferred from a generic metric to a candidatesetspecific metric.
Concentration of measure inequalities in information theory. Learning with kernels provides an introduction to svms and related kernel methods. We present an architecture that connects temporally extended individual experience with capacity for action, whereby a robot can develop over ontogeny through interaction. Informationtheoretic methods in statistical machine learning. An information theoretic interpretation to deep neural. Statistical estimation, optimization, and learning under information constraints. Publications empirical inference max planck institute for. Contribute to junjiehuitml development by creating an account on github. A relative complexity metric for decisiontheoretic. See this help page for instructions on obtaining such a link. However, note that while metric spaces play an important role in real analysis, the study of metric spaces is by no means the same thing as real analysis.
It was originally proposed by claude shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper titled a mathematical theory of communication. But i generally just put the arxiv id such as arxiv. As a result, a particular focus of our chapter lies on the choice between modelbased and modelfree as well as between value functionbased and policy search methods. In this paper, we present an information theoretic approach to learning a mahalanobis distance function. Central to this is an information theoretic metric space of sensorimotor experience, which is dynamically constructed and reconstructed as the robot acts. Bruno abrahao research assistant professor of information systems and business analytics.
Robust structural metric learning suppresses noisy features. This is a list of publications, aimed at being a comprehensive bibliography of the field. Related work supervised metric learning is a wellstudied problem, of which some representative methods are informationtheoretic metric learning itml davis et al. Citeseerx an informationtheoretic definition of similarity. Find, read and cite all the research you need on researchgate. On the other hand, descriptor features and metric learning. The model we study can be interpreted as a broad, abstract extension of the wellstudied online prediction model to a general decisiontheoretic setting.
If you have the book and have any questions or comments, please click here to send me mail. Learning when method findings convergeand when they dont csm, pp. More recently, most ear recognition methods have started based on deep learning features that can achieve a good accuracy, but it requires more resources in the training phase and suffer from timeconsuming computational complexity. Generative local metric learning for nearest neighbor classification. Recently, metric learning for semisupervised algorithms has received much attention.
Distance metric has an important role in many machine learning algorithms. Distance metric learning dml is an emerging field of machine learning. Publications scitech information sciences institute. Robust recognition via information theoretic learning. Our work touches on a range of problems including computer vision, state estimation, localization and mapping, highspeed navigation, motion planning, and robotic manipulation. This book is devoted to metric learning, a set of techniques to automatically learn. During my phd at the tnt lab at the leibniz university of hannover i was lucky enough to visit and work with people of many stimulating computer vision and computer graphics labs at mpiinformatik, uoft and microsoft research, cambridge. A decision tree is a simple representation for classifying examples.
An information theoretic interpretation to deep neural networks shaolun huang. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. A decisiontheoretic generalization of online learning. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Special issue on evolutionary computation meets deep learning.
The ear recognition task is known as predicting whether two ear images belong to the same person or not. The basic ingredients of the entropy method are discussed first in the context of logarithmic sobolev inequalities, which underlie the socalled functional approach to concentration of measure, and then from a complementary informationtheoretic viewpoint based on transportationcost inequalities and probability in metric spaces. This brings about the need for efficient algorithms to analyze threedimensional shapes. Informationtheoretic metric learning proceedings of the. Within a universal agentworld interaction framework, based on information theory and causal bayesian networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment.
Although the book begins with the basics, it also includes the latest research. I am a research scientist at the perceiving systems department at the max planck institute for intellifent systems, directed by michael black. Xxxx remember, absent an actual style guide from a journal, the main goal of the bibliography is to make the references findable by readers. Concentration of measure inequalities in information. A stepbystep guide to understanding and applying the metric system. Information theoretic learning itl was initiated in the late 90s at cnel and has been a center piece of the research effort. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decision theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning. Informationtheoretic metric learning videolectures. How to cite an article from arxiv using bibtex tex. Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Dnn learning with the optimization of a particular information.
Bruno abrahao research assistant professor new york. In this paper we look closely at the popular metric of anonymity, the anonymity set, and point out a number of problems associated with it. For semisupervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Should you wish to have your publications listed here, you can either email us your bibtex. We then propose an alternative information theoretic measure of anonymity which takes into account the probabilities of users sending and receiving the messages and show how to calculate it for a message in a standard mixbased anonymity system. A novel information theoreticinteract algorithm it in for feature selection using three machine learning algorithms.
Proceedings of the twentyeigth international joint. Global network assistant professor, new york university. Behaviorist learning theories cognitiveinformation processing learning theories cognitiveconstructivist learning theories the summary ends with a brief discussion of epistemological perspectives that serve as foundations for the various theories. Abstractdistance metric learning dml aims to find an appropriate way. Kernelbased distance metric learning in the output space arxiv. In this study, we propose a novel method by which to identify eqtl associations with information theory and machine learning approaches. Johnduchi,stanforduniversity michaeljordan,ucberkeley mertpilanci,ucberkeley martin wainwright uc berkeley information theory and machine learning november 2014 1 30. Many traditional eqtl methods are based on a linear regression model.
Informationtheoretic metric learning exists a simple bijection up to a scaling function between the set ofmahalanobisdistances andthe set of equalmean multivariate gaussian distributions without loss of generality, we can assume the gaussians have mean. Shannons classic paper a mathematical theory of communication in the bell system technical journal in july and october 1948 prior to this paper, limited informationtheoretic ideas had been developed at bell labs, all implicitly assuming. Similarity is an important and widely used concept. Dagent papers by topic a separate page has the same paper list sorted alphabetically this page lists all of our papers that are relevant to the dagents project. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain. A survey presents an overview of existing research in this topic, including recent progress on scaling to high. An informationtheoretic machine learning approach to. Pdf mathematical analysis on informationtheoretic metric. Allen school of computer science and engineering cse at the university of washington where i direct the uw robot learning laboratory. Given a large training set, the training samples are selected and reweighted according to their visual similarities with the query sample and its candidate set. Mathematical analysis on informationtheoretic metric learning with. Specifically, we minimize the burg divergence of a lowrank kernel to an input kernel, subject to.
When using this code, please cite itml and the relevant paper. Machine learning and knowledge discovery in databases pp 1256 cite as. Metric learning synthesis lectures on artificial intelligence and. Bibtex will select only the quoted references, and arrange them alphabetically if the style is such. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem. Large scale online learning of image similarity through. Informationtheoretic metric learning proceedings of the 24th. In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worstcase online framework. In this paper, we present an informationtheoretic approach to learning a mahalanobis distance function. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video.