sample complexity
On the Sample Complexity of Reinforcement Learning
... dealing with a restricted class of policies, the policy gradient must take into account the states where it visits frequently in order to avoid making errors at these states, since these errors are more costly and could ...
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Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
... The most common approach for statistical learning of models with latent variables is the expectation-maximization (EM) algorithm (Dempster et al., 1977). Under mild conditions, the EM algorithm is guaranteed to converge ...
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Distribution-Dependent Sample Complexity of Large Margin Learning
... the sample complexity, both governed by the margin-adapted dimen- sion of the data ...true sample complexity of large-margin ...use sample complexity bounds, such as active ...
31
Learning Factor Graphs in Polynomial Time and Sample Complexity
... the sample complexity of learn- ing a fixed structure ...the sample complexity of BN learning that was polynomial in the number of variables ...
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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 ...
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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 ...
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The Sample Complexity of Learning Linear Predictors with the Squared Loss
... We provide a tight sample complexity bound for learning bounded-norm linear predictors with respect to the squared loss. Our focus is on an agnostic PAC-style setting, where no assumptions are made on the ...
12
Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice
... Constant sample complexity and Squared Distortion. Constant sample complexity means that the mechanism (potentially randomized) only uses a con- stant number of ordinal queries regardless of ...
8
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm
... How many cascades do we need to recover the network structure? We will answer this question by providing a sample complexity analysis of the optimization in Eq. 4. Given the conditions spelled out in ...
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A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries
... The sample complexity of the algorithms in (Sun, Qing, and Wright, 2015) is however higher then for the Er-SpUD algorithm; as mentioned by the Authors, numerical simulations suggest that it is at least p = ...
18
Characterizing the Sample Complexity of Pure Private Learners
... The existence of an algorithm with sample complexity O(1) was already proven by Beimel et al. (2014). Moreover, assuming the existence of oneway functions, their learner is effi- cient. Our constructions ...
33
The Sample Complexity of Dictionary Learning
... where η ≥ 0 is sometimes zero, and the bound ε depends on the number of samples and problem pa- rameters. Since efficient algorithms that find the optimal dictionary for a given set of samples (also known as empirical ...
23
The Optimal Sample Complexity of PAC Learning
... the sample-consistent base learner ...a sample-consistent classifier in some hypothesis space H of VC dimension ...a sample-consistent classifier with some probability δ 0 taken sufficiently small ...
15
Optimal Quantum Sample Complexity of Learning Algorithms
... on sample complexity, which is a fundamental information-theoretic ...quantity. Sample complexity concerns a form of “passive” learning: the learner gets a number of examples at the start of ...
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PAC-learnability of Probabilistic Deterministic Finite State Automata
... any sample size N, even exponentially large, all but finitely many automata in the family will give the same sample set with probability greater than ...this sample set, the algorithm will produce a ...
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Learning Optimal Strategies to Commit To
... In this paper, we propose an algorithm for finding the op- timal strategy to commit to for the leader in Stackelberg games. Although our algorithm still has exponential depen- dence on m in the worst case, it makes ...
8
Provably Efficient Learning with Typed Parametric Models
... The sample complexity of the R-max algorithm by Brafman and Tennenholtz (2002) scales with the number of actions and the square of the number of discrete states, since a different dynamics model is learned ...
34
Stochastic Variance-Reduced Cubic Regularization Methods
... the sample complexity of Hessian matrix computation in cubic regularization based methods, we also propose a sample efficient stochastic variance-reduced cubic regularization (Lite-SVRC) algorithm ...
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Distribution testing lower bounds via reductions from communication complexity
... the complexity of this problem in terms of an arguably more intuitive measure and using simpler ...the sample complexity is essentially determined by a fundamental operator in the theory of ...
40
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 ...
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