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The fast growing field of compressed sensing is founded on the fact that if a signal is 'simple' and has some 'structure', then it can be reconstructed accurately with far fewer samples than its ambient dimension. Many different plausible…

Information Theory · Computer Science 2011-10-18 Shirin Jalali , Arian Maleki

The direct or algorithmic approach for the Jacobian problem, consisting of the direct construction of the inverse polynomials is proposed. The so called principle and derived Jacobi conditions are proposed and discussed. The algorithmic…

General Mathematics · Mathematics 2016-10-07 Dhananjay P. Mehendale

We propose a new numerical algorithm for computing the tensor rank decomposition or canonical polyadic decomposition of higher-order tensors subject to a rank and genericity constraint. Reformulating this computational problem as a system…

Numerical Analysis · Mathematics 2024-07-02 Simon Telen , Nick Vannieuwenhoven

In subset selection we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint, subset selection is NP-hard and few classes are known to be solvable in polynomial time. Using…

Optimization and Control · Mathematics 2020-02-07 Alberto Del Pia , Santanu S. Dey , Robert Weismantel

This paper reexamines univariate reduction from a toric geometric point of view. We begin by constructing a binomial variant of the $u$-resultant and then retailor the generalized characteristic polynomial to fully exploit sparsity in the…

Algebraic Geometry · Mathematics 2009-09-25 J. Maurice Rojas

We obtain robust and computationally efficient estimators for learning several linear models that achieve statistically optimal convergence rate under minimal distributional assumptions. Concretely, we assume our data is drawn from a…

Machine Learning · Statistics 2020-12-07 Ainesh Bakshi , Adarsh Prasad

The theory of imprecise Markov chains has achieved significant progress in recent years. Its applicability, however, is still very much limited, due in large part to the lack of efficient computational methods for calculating…

Optimization and Control · Mathematics 2022-03-30 Damjan Škulj

Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To…

Machine Learning · Computer Science 2025-07-16 Amr Alkhatib , Roman Bresson , Henrik Boström , Michalis Vazirgiannis

Kolmogorov complexity measures the algorithmic complexity of a finite binary string $\sigma$ in terms of the length of the shortest description $\sigma^*$ of $\sigma$. Traditionally, the length of a string is taken to measure the amount of…

Computational Complexity · Computer Science 2019-06-14 Cameron Fraize , Christopher P. Porter

We construct a family of functions suitable for establishing lower bounds on the oracle complexity of first-order minimization of smooth strongly-convex functions. Based on this construction, we derive new lower bounds on the complexity of…

Optimization and Control · Mathematics 2021-06-16 Yoel Drori , Adrien Taylor

Like all other knot polynomials, the superpolynomials should be defined in arbitrary representation R of the gauge group in (refined) Chern-Simons theory. However, not a single example is yet known of a superpolynomial beyond symmetric or…

High Energy Physics - Theory · Physics 2014-07-24 Anton Morozov

It has by now become a standard approach to use the theory of sparse (or toric) elimination, based on the Newton polytope of a polynomial, in order to reveal and exploit the structure of algebraic systems. This talk surveys compact…

Computational Complexity · Computer Science 2017-10-16 Ioannis Emiris

In our recent publication [1] we presented an exponential series approximation suitable for highly accurate computation of the complex error function in a rapid algorithm. In this Short Communication we describe how a simplified…

Numerical Analysis · Mathematics 2012-05-09 S. M. Abrarov , B. M. Quine

List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list…

Machine Learning · Computer Science 2026-03-24 Steve Hanneke , Shay Moran , Tom Waknine

This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from…

Machine Learning · Computer Science 2015-09-16 Corinna Cortes , Prasoon Goyal , Vitaly Kuznetsov , Mehryar Mohri

Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Shapley values…

Machine Learning · Statistics 2020-02-07 Kjersti Aas , Martin Jullum , Anders Løland

We present a short new proof of Cobham's theorem without using Kronecker's approximation theorem, making it suitable for generalization beyond automatic sequences.

Formal Languages and Automata Theory · Computer Science 2018-01-23 Thijmen J. P. Krebs

A conditional sampling oracle for a probability distribution D returns samples from the conditional distribution of D restricted to a specified subset of the domain. A recent line of work (Chakraborty et al. 2013 and Cannone et al. 2014)…

Data Structures and Algorithms · Computer Science 2016-08-18 Themistoklis Gouleakis , Christos Tzamos , Manolis Zampetakis

In [3] a short proof is given that some strings have maximal plain Kolmogorov complexity but not maximal prefix-free complexity. The proof uses Levin's symmetry of information, Levin's formula relating plain and prefix complexity and Gacs'…

Computational Complexity · Computer Science 2014-05-08 Bruno Bauwens

This paper presents a new derivative parsing algorithm for parsing expression grammars; this new algorithm is both simpler and faster than the existing parsing expression derivative algorithm presented by Moss. This new algorithm improves…

Formal Languages and Automata Theory · Computer Science 2018-08-28 Aaron Moss