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Computational Learning Theory


New Methods to solve NP-Hard problems part1(Computational Complexity)

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Abstract:: NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current Noisy Intermediate Scale Quantum (NISQ) computers are not able to optimize industrially relevant problems.


Proofs and Certificates for Max-SAT

Journal of Artificial Intelligence Research

Current Max-SAT solvers are able to efficiently compute the optimal value of an input instance but they do not provide any certificate of its validity. In this paper, we present a tool, called MS-Builder, which generates certificates for the Max-SAT problem in the particular form of a sequence of equivalence-preserving transformations. To generate a certificate, MS-Builder iteratively calls a SAT oracle to get a SAT resolution refutation which is handled and adapted into a sound refutation for Max-SAT. In particular, we prove that the size of the computed Max-SAT refutation is linear with respect to the size of the initial refutation if it is semi-read-once, tree-like regular, tree-like or semi-tree-like. Additionally, we propose an extendable tool, called MS-Checker, able to verify the validity of any Max-SAT certificate using Max-SAT inference rules. Both tools are evaluated on the unweighted and weighted benchmark instances of the 2020 Max-SAT Evaluation.


Machine Learning For Data Science Using MATLAB

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MATLAB is a widely used programming language for statistical computing. This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it. The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal.


AI is helping materials scientists home in on promising new materials

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AI and automation are speeding up science and chemistry by helping scientists pick which experiments to conduct and home in on promising new materials. Why it matters: There's pressure on these fields to produce new materials faster and cheaper to support and power technologies that could transform industries and economies. The big picture: New materials and molecules are needed for the batteries, drugs and semiconductors envisioned to underpin a green grid, precise medicine, and the next generation of computing and communications. What's happening: It can take decades to get a new material to market in a process that involves an almost "artisanal science," Isayev says. Zoom in: In a new study, researchers combined machine learning, theories and calculations of physical properties and experiments to identify new alloys.


A Survey of Methods for Automated Algorithm Configuration

Journal of Artificial Intelligence Research

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.


Computational Learning Theory: Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings (Lecture Notes in Computer Science, 904): Vitanyi, Paul: 9783540591191: Amazon.com: Books

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Computational Learning Theory: Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings (Lecture Notes in Computer Science, 904) [Vitanyi, Paul] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings (Lecture Notes in Computer Science, 904)


Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375): Kivinen, Jyrki, Sloan, Robert H.: 9783540438366: Amazon.com: Books

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Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375) [Kivinen, Jyrki, Sloan, Robert H.] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375)


An Introduction to Computational Learning Theory (The MIT Press): Kearns, Michael J., Vazirani, Umesh: 9780262111935: Amazon.com: Books

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Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.


Computational Learning Theory: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings (Lecture Notes in Computer Science, 1208): Ben-David, Shai: 9783540626855: Amazon.com: Books

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Computational Learning Theory: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings (Lecture Notes in Computer Science, 1208) [Ben-David, Shai] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings (Lecture Notes in Computer Science, 1208)


Acing Machine Learning Interviews

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Soft skills: Amazon interview preparation guide, principles Amazon expects in their employees, Amazon principles explained, Situation Task Action Result technique, soft skills from a machine learning PhD; Coding: coding interview preparation leetcode, Cracking the Coding interview book, practicing machine learning problems; Machine learning theory: Machine Learning QA book 1, Machine Learning QA book 2, summary from glassdoor, when not to use machine learning, methods section of paperswithcode. If you liked this article share it with a friend! To read more on machine learning and image processing topics press subscribe!