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

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...

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

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

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...

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

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

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

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

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...

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

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

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

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...

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

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

03/12/2024. Complexity and Contradiction in Architecture - Robert Venturi. modernism101.com. Retrieved 04/12/2024. WebSample Complexity and Contradiction...

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...

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

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...

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...

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...

case-control sampling is an algorithm used to reduce the complexity of training a logistic regression classifier. The algorithm reduces the training complexity by...

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

a flow of probability distributions with an increasing level of sampling complexity arise (path spaces models with an increasing time horizon, Boltzmann–Gibbs...

Carlo algorithms are considered, and several complexity classes are studied. The most basic randomized complexity class is RP, which is the class of decision...

In numerical analysis and computational statistics, rejection sampling is a basic technique used to generate observations from a distribution. It is also...

simpler classification problem and, consequently, in great reduction of sample complexity of the model. A simple computational experiment illustrates this idea...

not the case which implies that the sample complexity bounds can be further tightened. In terms of time complexity, they showed that AltMinComplete needs...