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We consider uniform random permutations in proper substitution-closed classes and study their limiting behavior in the sense of permutons. The limit depends on the generating series of the simple permutations in the class. Under a mild…

We consider a notion of uniform thinning for a finite sequence of random variables $(X_1,...,X_n)$ obtained by removing one random variable, uniformly at random. If a triangular array of random variables $(X_{n,k} : n \in \mathbb{N}_+, 1…

Probability · Mathematics 2007-05-23 Shannon Starr

Given a basic compact semi-algebraic set $\K\subset\R^n$, we introduce a methodology that generates a sequence converging to the volume of $\K$. This sequence is obtained from optimal values of a hierarchy of either semidefinite or linear…

Optimization and Control · Mathematics 2015-05-13 Didier Henrion , Jean Bernard Lasserre , Carlo Savorgnan

Quantum mechanics can speed up a range of search applications over unsorted data. For example imagine a phone directory containing N names arranged in completely random order. To find someone's phone number with a probability of 50%, any…

Quantum Physics · Physics 2009-10-05 Lov K. Grover

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications…

Machine Learning · Computer Science 2022-09-20 Yanhang Zhang , Junxian Zhu , Jin Zhu , Xueqin Wang

We consider connected components in $k$-uniform hypergraphs for the following notion of connectedness: given integers $k\ge 2$ and $1\le j \le k-1$, two $j$-sets (of vertices) lie in the same $j$-component if there is a sequence of edges…

Combinatorics · Mathematics 2018-03-08 Oliver Cooley , Mihyun Kang , Christoph Koch

Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks. Although many algorithms have been proposed to find biclusters, existing methods suffer…

Machine Learning · Statistics 2020-02-11 Michelle N. Ngo , Dustin S. Pluta , Alexander N. Ngo , Babak Shahbaba

Modern datasets span billions of samples, making training on all available data infeasible. Selecting a high quality subset helps in reducing training costs and enhancing model quality. Submodularity, a discrete analogue of convexity, is…

Machine Learning · Computer Science 2025-04-04 Maximilian Böther , Abraham Sebastian , Pranjal Awasthi , Ana Klimovic , Srikumar Ramalingam

Motivated by practical generalizations of the classic $k$-median and $k$-means objectives, such as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce a meta-theorem for designing coresets for…

Data Structures and Algorithms · Computer Science 2022-09-20 Vladimir Braverman , Vincent Cohen-Addad , Shaofeng H. -C. Jiang , Robert Krauthgamer , Chris Schwiegelshohn , Mads Bech Toftrup , Xuan Wu

We present efficient counting and sampling algorithms for random $k$-SAT when the clause density satisfies $\alpha \le \frac{2^k}{\mathrm{poly}(k)}.$ In particular, the exponential term $2^k$ matches the satisfiability threshold…

Data Structures and Algorithms · Computer Science 2024-11-06 Zongchen Chen , Aditya Lonkar , Chunyang Wang , Kuan Yang , Yitong Yin

Let F be a uniformly distributed random k-SAT formula with n variables and m clauses. We present a polynomial time algorithm that finds a satisfying assignment of F with high probability for constraint densities m/n<(1-eps_k)2^k\ln(k)/k,…

Combinatorics · Mathematics 2017-11-17 Amin Coja-Oghlan

A sum where each of the $N$ summands can be independently chosen from two choices yields $2^N$ possible summation outcomes. There is an $\mathcal{O}(K^2)$-algorithm that finds the $K$ smallest/largest of these sums by evading the…

Data Structures and Algorithms · Computer Science 2017-04-20 Torsten Gross , Nils Blüthgen

We study universal compression of sequences generated by monotonic distributions. We show that for a monotonic distribution over an alphabet of size $k$, each probability parameter costs essentially $0.5 \log (n/k^3)$ bits, where $n$ is the…

Information Theory · Computer Science 2007-07-13 Gil I. Shamir

We survey a new area of parameter-free similarity distance measures useful in data-mining, pattern recognition, learning and automatic semantics extraction. Given a family of distances on a set of objects, a distance is universal up to a…

Information Retrieval · Computer Science 2007-05-23 Paul Vitanyi

We investigate the generalisation of quantum search of unstructured and totally ordered sets to search of partially ordered sets (posets). Two models for poset search are considered. In both models, we show that quantum algorithms can…

Quantum Physics · Physics 2009-06-18 Ashley Montanaro

In this paper, we develop efficient randomized algorithms for estimating probabilistic robustness margin and constructing robustness degradation curve for uncertain dynamic systems. One remarkable feature of these algorithms is their…

Optimization and Control · Mathematics 2008-05-13 Xinjia Chen , Kemin Zhou , Jorge L. Aravena

We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of our transformation…

Machine Learning · Statistics 2023-03-31 Guy Blanc , Jane Lange , Ali Malik , Li-Yang Tan

We present quantum circuits with a brick wall structure using the optimal number of parameters and two-qubit gates to parametrize $SU(2^n)$, and provide evidence that these circuits are universal for $n\leq 5$. For this, we successfully…

Quantum Physics · Physics 2025-11-24 David Wierichs , Korbinian Kottmann , Nathan Killoran

In this paper, we investigate the probabilistic set covering problem (PSCP) in which the right-hand side is a binary random vector and the covering constraint is required to be satisfied with a prespecified probability. We consider the case…

Optimization and Control · Mathematics 2026-03-24 Wei Lv , Wei-Kun Chen , Yi-Long Chen , Yu-Hong Dai

Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This paper introduces a…

Machine Learning · Statistics 2024-10-16 Yijia Zhou , Kyle A. Gallivan , Adrian Barbu