[PDF] Top 20 The Sample Complexity of Dictionary Learning
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The Sample Complexity of Dictionary Learning
... The Babel function is one of several metrics defined in the sparse representations literature to quan- tify an ”almost orthogonality” property that dictionaries may enjoy. Such properties have been shown to imply ... See full document
23
Learning Factor Graphs in Polynomial Time and Sample Complexity
... the complexity of structure learning of Bayesian networks fall largely into two ...infinite sample limit ...infinite sample limit setting is critical in their analysis since it allows for ... See full document
46
Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
... spectral learning algorithms for hidden Markov models (HMMs) and related ...L-PCFG learning algo- rithms described in this ...spectral learning algorithm for HMMs which recovers an HMM’s param- ... See full document
51
Metric to determine Language Complexity using Dictionary Method Percentage Retrieval
... language complexity such as lexical density, morphological density, semantics ...the complexity of a language which is consistent and with proven ...using dictionary method, the percentage retrieval ... See full document
5
Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... We note an important connection to an earlier study about the learnability of probabilistic automata and hidden Markov models by Abe and Warmuth (1992). In that study, the authors provided positive results for the ... See full document
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Combined Dictionary Learning in Facial Expression Recognition
... Recognizing facial expressions from facial images is an important part of these efforts. Gabor features have been used in many works because of its insensitivity to face registration and good performance in facial ... See full document
5
Distribution-Dependent Sample Complexity of Large Margin Learning
... Consider learning using MEM, when the distribution is uniform over [0,1], and all the labels are − ...a sample of size m is γ-shattered at the origin with probability ...any learning algorithm that ... See full document
31
Characterizing the Sample Complexity of Pure Private Learners
... Recently, Alon et al. (2018) showed that a similar lower bound holds also for improper learners. This means that learning threshold functions over an infinite domain is impossible with approximate privacy. In ... See full document
33
Smooth Boosting and Learning with Malicious Noise
... Gentile and Littlestone (1999) have given mistake bounds for the online p-norm algorithms when run on examples which are not linearly separable, thus generalizing previous bounds on noise tolerance for Perceptron (Freund ... See full document
16
Refined Error Bounds for Several Learning Algorithms
... of learning linear separators under a uniform distribu- tion by Balcan, Broder, and Zhang (2007) (see also Dasgupta, Kalai, and Monteleoni, 2005, for related ...consistent learning algorithm achieves the ... See full document
55
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
... agnostic learning of a concept class C, and the sanitization task for the same class ...privately learning a concept class C to the sanitization task of C, where the sanitizer’s access to the database is ... See full document
61
Optimal Quantum Sample Complexity of Learning Algorithms
... to learning classical objects such as Boolean functions, one may also study the learnability of quantum ...PAC learning the error of the learner’s hypothesis is evaluated under the same distribution D that ... See full document
36
The Optimal Sample Complexity of PAC Learning
... correct learning (or PAC learning; Valiant, 1984) is a classic cri- terion for supervised learning, which has been the focus of much research in the past three ...PAC learning is to produce a ... See full document
15
On the Sample Complexity of Reinforcement Learning
... same sample complexity notion of the last sub section applies — though now the samples are “real” and not “computed” (ie information theoretically the samples provides us with more information about the ... See full document
143
Fast dictionary learning from incomplete data
... The dictionary learning community does not directly address the problem under ...computational complexity but a much higher con- sistency between the dictionaries learned from corrupted and ... See full document
21
Local Identification of Overcomplete Dictionaries
... the dictionary is recoverable as the local maximum of a new maximization criterion that generalizes the K-means ...finite sample size recovery results with high probability as long as the sample size ... See full document
32
The Sample Complexity of Learning Linear Predictors with the Squared Loss
... below). However, when we deal with the hypothesis class of norm-bounded predictors, then the excess risk can be larger by an arbitrary factor 1 . Therefore, upper bounds on these measures do not imply upper bounds on the ... See full document
12
Learning Latent Tree Graphical Models
... Another popular class of reconstruction methods used in the phylogenetic community is the family of quartet-based distance methods (Bandelth and Dress, 1986; Erd˝os et al., 1999; Jiang et al., 2001). 3 Quartet-based ... See full document
42
Fingerprint Compression Based On Online Dictionary Learning and Orthogonal Matching Pursuit
... machine learning and other important applications are in sensing, communications [13] and control like medical imaging, Radar Imaging, sparsity-based event detection and classification in sensor ... See full document
8
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
... The sample complexity of the policy presented in Section ...pathwise sample complexity of t, if the policy terminates after at most t trials, with probability ...pathwise sample ... See full document
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