#sample_complexity search results
Determining the number of clusters using property testing algorithm - Visit programmatic.solutions/mxcnkz/determi… for the solution. #clustering #property_testing #sample_complexity #cstheory #computerscience
When drawing conclusions from your data, it's important to understand the variance of your estimators, especially when using complex sampling designs such as clustering or stratification. The design effect measures how much these designs increase the variance of your estimates…
*** Simplicity and Interestingness in Statistics *** The relationship between simplicity and interestingness in statistical modeling encompasses various interrelated themes crucial for practical analysis and decision-making. Theoretical Foundations **Occam's Razor**: This…
sampling isnt just one easily definable discipline its a cavalcade of dozens of different use cases stretched across dozens of genres all full of what-ifs and potential ideas. dont write stuff off because you think its "lazy". sometimes the hardest thing to do is play less.
Towards Comprehensive Sampling of SMT Solutions. arxiv.org/abs/2511.10326
Sample Complexity of Quadratically Regularized Optimal Transport arxiv.org/abs/2511.09807
Sanz, et al.: Sample Complexity of Quadratically Regularized Optimal Transport arxiv.org/abs/2511.09807 arxiv.org/pdf/2511.09807 arxiv.org/html/2511.09807
Sampling Bias: Samples across countries are NEVER apples to apples . Countries with more internal diversity (like India) produce samples that are less representative than homogeneous ones like the US
That's a very valid point, chastronomic! It's true, focusing on sample difficulty is key, not everything is equal, you know.
There is also the extremely pedantic point that "random variation in the sampling process" is something else altogether (speed of convergence to the limiting distribution) and doesn't require/explain "size" components at all.
Oh, the sampling alone makes it totally un-generalizable. They also identify multiple "study limitations", a couple of which are genuinely concerning. Their results apply to a very narrow subset of people in a very particular place during a specific time period. Nothing more.
Provably Efficient Sample Complexity for Robust CMDP. arxiv.org/abs/2511.07486
By choosing a large sample, one can ensure that in most samples, the sample proportion and true proportion differ by a small quantity. If large sample is selected at random, probability that sample proportion nd true proportion differ by small quantity is close to 1.
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Provably Efficient Sample Complexity for Robust CMDP ift.tt/dMijeyG
Patrick Scharpfenecker, Tobias Windisch. [stat.ME]. SAT-sampling for statistical significance testing in sparse contingency tables. arxiv.org/abs/2511.05709
Patrick Scharpfenecker, Tobias Windisch. [stat.ME]. SAT-sampling for statistical significance testing in sparse contingency tables. arxiv.org/abs/2511.05709…
The variation here comes from the data we collect, not from the algorithms. In this formulation it doesn't matter if our algorithm has a closed form solution or requires iterative psuedo-randomness, the true randomness comes from how we generate our datasets.
Nonconvex optimization can be hard. Sampling, as a stochastic generalization, is not always easier. What about a case further complicated by nonconvex ineq & equality constraints? arxiv.org/abs/2510.22044 (#NeurIPS2025) introduces a new tool, and samples exponentially fast!
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction. arxiv.org/abs/2511.05396
Random sampling is also too expensive! So we need to carefully design a lookup cache to answer sampling questions, and round it up to a power of 2 so that we can avoid modulo operations. Just moving bytes alone is 16% of a high-end CPU's compute at the speed we want!
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