The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target...

Daniel J. Russo and Benjamin Van Roy (2013), "Eluder Dimension and the Sample Complexity of Optimistic Exploration", Advances in Neural Information Processing...

quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within...

young vs. old bidders). The sample complexity of a random-sampling mechanism is the number of agents it needs to sample in order to attain a reasonable...

the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly...

total variation distance between two sequences of n {\displaystyle n} samples is at least 1 − 2 δ {\displaystyle 1-2\delta } . This is because the total...

Warmuth Occam learning Data mining Error tolerance (PAC learning) Sample complexity L. Valiant. A theory of the learnable. Communications of the ACM,...

smallest and largest Ws. List of datasets for machine learning research Sample complexity Bayesian Optimization Reinforcement learning Improving Generalization...

Z} . The Rademacher complexity of the function class F {\displaystyle {\mathcal {F}}} with respect to P {\displaystyle P} for sample size m {\displaystyle...

(2013). "Boson-Sampling in the light of sample complexity". arXiv:1306.3995 [quant-ph]. Aaronson, Scott; Arkhipov, Alex (2013). "BosonSampling is far from...

BIC's penalty for model complexity is ln ⁡ ( n ) k {\displaystyle \ln(n)k} , while AIC's is 2 k {\displaystyle 2k} . Large-sample asymptotic theory establishes...

theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer...

the Occam framework can be used to produce tighter bounds on the sample complexity of classical problems including conjunctions, conjunctions with few...

from within each cluster (i.e. two-stage cluster sampling). Complexity. Cluster sampling is more sophisticated and requires more attention with how to...

its policy immediately, have been mathematically studied proving sample complexity bounds for RLHF under different feedback models. In the offline data...

returns a sample from the distribution D {\displaystyle \textstyle D} . Sometimes the interest is, apart from measuring the time complexity, to measure...

2014). He received the best paper award at NeurIPS 2018. for work on sample complexity of distribution learning problems. He was the President of the Association...

of examples needed: for every concept class, classical and quantum sample complexity are the same up to constant factors. However, for learning under some...

Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion...

The NASA-ESA Mars Sample Return is a proposed Flagship-class Mars sample return (MSR) mission to collect Martian rock and soil samples in 43 small, cylindrical...

Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization...

question in PIM design is: what is the sample complexity of the mechanism? I.e, how many agents it needs to sample in order to attain a reasonable approximation...

and Complexity", exploring the diversity of problem types by contrasting problems of simplicity, disorganized complexity, and organized complexity. Weaver...

of sparse (mostly zero) factors. As a result, it manages to reduce the complexity of computing the DFT from O ( n 2 ) {\textstyle O(n^{2})} , which arises...

analysis (see below). For the analysis of complex biological samples, a reduction of sample complexity is required. This may be performed off-line by one-dimensional...

In theoretical computer science, communication complexity studies the amount of communication required to solve a problem when the input to the problem...

not the number of samples N. The incremental method with reduced rounding errors can also be applied, with some additional complexity. A running sum of...

Sample entropy (SampEn) is a modification of approximate entropy (ApEn), used for assessing the complexity of physiological time-series signals, diagnosing...

methods for obtaining random samples from a sequence of probability distributions with an increasing level of sampling complexity. These probabilistic models...

assumptions in the theory of computational complexity have to be invoked. In this sense, quantum random sampling schemes can have the potential to show quantum...