2,585 research outputs found
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
In many real-world classification problems, the labels of training examples
are randomly corrupted. Most previous theoretical work on classification with
label noise assumes that the two classes are separable, that the label noise is
independent of the true class label, or that the noise proportions for each
class are known. In this work, we give conditions that are necessary and
sufficient for the true class-conditional distributions to be identifiable.
These conditions are weaker than those analyzed previously, and allow for the
classes to be nonseparable and the noise levels to be asymmetric and unknown.
The conditions essentially state that a majority of the observed labels are
correct and that the true class-conditional distributions are "mutually
irreducible," a concept we introduce that limits the similarity of the two
distributions. For any label noise problem, there is a unique pair of true
class-conditional distributions satisfying the proposed conditions, and we
argue that this pair corresponds in a certain sense to maximal denoising of the
observed distributions.
Our results are facilitated by a connection to "mixture proportion
estimation," which is the problem of estimating the maximal proportion of one
distribution that is present in another. We establish a novel rate of
convergence result for mixture proportion estimation, and apply this to obtain
consistency of a discrimination rule based on surrogate loss minimization.
Experimental results on benchmark data and a nuclear particle classification
problem demonstrate the efficacy of our approach
Order-Revealing Encryption and the Hardness of Private Learning
An order-revealing encryption scheme gives a public procedure by which two
ciphertexts can be compared to reveal the ordering of their underlying
plaintexts. We show how to use order-revealing encryption to separate
computationally efficient PAC learning from efficient -differentially private PAC learning. That is, we construct a concept
class that is efficiently PAC learnable, but for which every efficient learner
fails to be differentially private. This answers a question of Kasiviswanathan
et al. (FOCS '08, SIAM J. Comput. '11).
To prove our result, we give a generic transformation from an order-revealing
encryption scheme into one with strongly correct comparison, which enables the
consistent comparison of ciphertexts that are not obtained as the valid
encryption of any message. We believe this construction may be of independent
interest.Comment: 28 page
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Average case analysis of empirical and explanation-based learning algorithms
We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to mathematically model the performance of learning algorithms in order to better understand the nature of their empirical behavior. We are interested in how differences in learning algorithms influence the expected accuracy of the concepts learned.We present the Average Case Learning Model and apply the model to three learning algorithms: a purely empirical algorithm (Bruner's Wholist), an algorithm which prefers analytical (explanation-based) learning over empirical learning (EBL-FIRST-TM) and an algorithm integrating both analytical and empirical learning (lOSC-TM). The Average Case Learning Model is unique in that it is able to accurately predict the expected behavior of learning algorithms. We compare average case analysis to Valiant's Probably Approximately Correct (PAC) learning model
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Learning from noisy data and Markovian processes
We discuss more realistic models of computational learning. We extend the existing literature on the Probably Approximately Correct (PAC) framework to finite Markov chains in two directions by considering: (1) the presence of classification noise (specifically assuming that the training data has currupted labelled examples), and (2) real valued function learning. In both cases we address the key issue of determining how many training examples must be presented to the learner in the learning phase for the learning to be successful under the PAC paradigm
Insights into the pulmonary vascular complications of heart failure with preserved ejection fraction
Pulmonary hypertension in the setting of heart failure with preserved ejection fraction (PH-HFpEF) is a growing public health problem that is increasing in prevalence. While PH-HFpEF is defined by a high mean pulmonary artery pressure, high left ventricular end-diastolic pressure and a normal ejection fraction, some HFpEF patients develop PH in the presence of pulmonary vascular remodelling with a high transpulmonary pressure gradient or pulmonary vascular resistance. Ageing, increased left atrial pressure and stiffness, mitral regurgitation, as well as features of metabolic syndrome, which include obesity, diabetes and hypertension, are recognized as risk factors for PH-HFpEF. Qualitative studies have documented that patients with PH-HFpEF develop more severe symptoms than those with HFpEF and are associated with more significant exercise intolerance, frequent hospitalizations, right heart failure and reduced survival. Currently, there are no effective therapies for PH-HFpEF, although a number of candidate drugs are being evaluated, including soluble guanylate cyclase stimulators, phosphodiesterase type 5 inhibitors, sodium nitrite and endothelin receptor antagonists. In this review we attempt to provide an updated overview of recent findings pertaining to the pulmonary vascular complications in HFpEF in terms of clinical definitions, epidemiology and pathophysiology. Mechanisms leading to pulmonary vascular remodelling in HFpEF, a summary of pre-clinical models of HFpEF and PH-HFpEF, and new candidate therapeutic strategies for the treatment of PH-HFpEF are summarized
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