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We analyze parallel algorithms in the context of exhaustive search over totally ordered sets. Imagine an infinite list of "boxes", with a "treasure" hidden in one of them, where the boxes' order reflects the importance of finding the…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-11-03 Pierre Fraigniaud , Amos Korman , Yoav Rodeh

The easy-hard-easy pattern in the difficulty of combinatorial search problems as constraints are added has been explained as due to a competition between the decrease in number of solutions and increased pruning. We test the generality of…

Artificial Intelligence · Computer Science 2008-02-03 D. L. Mammen , T. Hogg

We present a novel method for approximately equilibrating a matrix $A \in {\bf R}^{m \times n}$ using only multiplication by $A$ and $A^T$. Our method is based on convex optimization and projected stochastic gradient descent, using an…

Optimization and Control · Mathematics 2016-02-23 Steven Diamond , Stephen Boyd

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a…

Systems and Control · Electrical Eng. & Systems 2020-09-29 Yunchuan Li , Michael C. Fu , Jie Xu

This paper describes a new and purely functional implementation technique of binary heaps. A binary heap is a tree-based data structure that implements priority queue operations (insert, remove, minimum/maximum) and guarantees at worst…

Data Structures and Algorithms · Computer Science 2013-12-18 Vladimir Kostyukov

In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is either a summation, a multiplication, or the application of one of the $q$ basis…

Machine Learning · Statistics 2019-05-28 Yixi Xu , Jean Honorio , Xiao Wang

The condition number of a diagonally scaled matrix, for appropriately chosen scaling matrices, is often less than that of the original. Equilibration scales a matrix so that the scaled matrix's row and column norms are equal. Scaling can be…

Numerical Analysis · Mathematics 2012-06-21 Andrew M. Bradley , Walter Murray

In observational causal inference, in order to emulate a randomized experiment, weights are used to render treatments independent of observed covariates. This property is known as balance; in its absence, estimated causal effects may be…

Methodology · Statistics 2020-07-16 David Arbour , Drew Dimmery , Arjun Sondhi

We define a new balance index for rooted phylogenetic trees based on the symmetry of the evolutive history of every set of 4 leaves. This index makes sense for multifurcating trees and it can be computed in time linear in the number of…

Populations and Evolution · Quantitative Biology 2019-03-25 Tomás M. Coronado , Arnau Mir , Francesc Rosselló , Gabriel Valiente

Despite the many recent practical and theoretical breakthroughs in computational game theory, equilibrium finding in extensive-form team games remains a significant challenge. While NP-hard in the worst case, there are provably efficient…

Computer Science and Game Theory · Computer Science 2022-01-19 Brian Hu Zhang , Tuomas Sandholm

A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems. This is because…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Shounak Datta , Sayak Nag , Swagatam Das

Binary search finds a given element in a sorted array with an optimal number of $\log n$ queries. However, binary search fails even when the array is only slightly disordered or access to its elements is subject to errors. We study the…

Data Structures and Algorithms · Computer Science 2017-02-21 Yann Disser , Stefan Kratsch

We present a new approach to the global fairness verification of tree-based classifiers. Given a tree-based classifier and a set of sensitive features potentially leading to discrimination, our analysis synthesizes sufficient conditions for…

Machine Learning · Computer Science 2022-09-28 Stefano Calzavara , Lorenzo Cazzaro , Claudio Lucchese , Federico Marcuzzi

In this paper, we propose a stochastic search algorithm for solving general optimization problems with little structure. The algorithm iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized…

Optimization and Control · Mathematics 2013-01-08 Enlu Zhou , Jiaqiao Hu

Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated as a weighted low-rank approximation problem and solved by non-convex optimization…

Machine Learning · Computer Science 2016-12-09 Yuanzhi Li , Yingyu Liang , Andrej Risteski

The decision tree is one of the most fundamental programming abstractions. A commonly used type of decision tree is the alphabetic binary tree, which uses (without loss of generality) ``less than'' versus ''greater than or equal to'' tests…

Performance · Computer Science 2007-07-13 Michael B. Baer

Line search (or backtracking) procedures have been widely employed into first-order methods for solving convex optimization problems, especially those with unknown problem parameters (e.g., Lipschitz constant). In this paper, we show that…

Optimization and Control · Mathematics 2024-08-20 Tianjiao Li , Guanghui Lan

In this paper, we introduce a new method for querying triadic concepts through partial or complete matching of triples using an inverted index, to retrieve already computed triadic concepts that contain a set of terms in their extent,…

Databases · Computer Science 2024-01-22 Pedro Henrique B. Ruas , Rokia Missaoui , Mohamed Hamza Ibrahim

We re-examine previous constructions of infinite binary words containing few distinct squares with the goal of finding the "simplest", in a certain sense. We exhibit several new constructions. Rather than using tedious case-based arguments…

Formal Languages and Automata Theory · Computer Science 2020-07-17 Daniel Gabric , Jeffrey Shallit

Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they exhibit large variance and subsequently suffer…

Machine Learning · Computer Science 2022-09-29 Gal Dalal , Assaf Hallak , Shie Mannor , Gal Chechik
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