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Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP [article]

Satyen Kale, Zohar Karnin, Tengyuan Liang, Dávid Pál
2017 arXiv   pre-print
Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a real-valued prediction,  ...  The goal is to design an online learning algorithm with sublinear regret to the best sparse linear predictor in hindsight.  ...  Therefore, we can formulate the original online sparse regression problem into online weakly supermodular minimization problem.  ... 
arXiv:1706.04690v1 fatcat:ugrzmhbqpjhcvi6agga2mrx76a

Advances and Open Problems in Federated Learning [article]

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D'Oliveira, Hubert Eichner (+47 others)
2021 arXiv   pre-print
Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.  ...  Open problems in this area include characterizing the sparsity regimes associated with sub-model training problems of practical interest and developing of sparse secure aggregation techniques that are  ...  This section raises open problems in both categories.  ... 
arXiv:1912.04977v3 fatcat:efkbqh4lwfacfeuxpe5pp7mk6a

Computational Intelligence in Electrophysiology: Trends and Open Problems [chapter]

Cengiz Günay, Tomasz G. Smolinski, William W. Lytton, Thomas M. Morse, Padraig Gleeson, Sharon Crook, Volker Steuber, Angus Silver, Horatiu Voicu, Peter Andrews, Hemant Bokil, Hiren Maniar (+7 others)
2008 Studies in Computational Intelligence  
problems.  ...  Finally, Sections 14.7 by Gloster Aaron and 14.8 by Jean-Marc Fellous present some interesting open problems in electrophysiology with examples of analysis techniques, including CI-motivated approaches  ... 
doi:10.1007/978-3-540-78534-7_14 fatcat:c3rim2lvx5hwpak5rqons53uku

Autonomous agents modelling other agents: A comprehensive survey and open problems

Stefano V. Albrecht, Peter Stone
2018 Artificial Intelligence  
The article concludes with a discussion of open problems which may form the basis for fruitful future research.  ...  Note also that regression problems can be transformed into classification problems via a finite discretisation of values, albeit with an exponential growth of class labels if multiple regression variables  ...  Open Problems We conclude our survey by discussing nine open problems which we believe have not been sufficiently addressed in the literature and may provide fruitful avenues of future research.  ... 
doi:10.1016/j.artint.2018.01.002 fatcat:ie7e6q3pufhelcdxxbruatprw4

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems [article]

Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim
2021 arXiv   pre-print
Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches.  ...  However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own.  ...  In this paper, we described the STDM problems and open gaps.  ... 
arXiv:2103.17128v1 fatcat:ci5pt5bytndr5inolznjsaizpi

Machine learning for email spam filtering: review, approaches and open research problems

Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafi'i Muhammad Abdulhamid, Adebayo Olusola Adetunmbi, Opeyemi Emmanuel Ajibuwa
2019 Heliyon  
Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering.  ...  Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering.  ...  We also revealed some open research problems associated with spam filters.  ... 
doi:10.1016/j.heliyon.2019.e01802 pmid:31211254 pmcid:PMC6562150 fatcat:n7qiq4tgnzh7xi6j5c2ah335hy

Online Collaborative Learning for Open-Vocabulary Visual Classifiers

Hanwang Zhang, Xindi Shang, Wenzhuo Yang, Huan Xu, Huanbo Luan, Tat-Seng Chua
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.  ...  collaboratively decomposing the sparse image-label matrix.  ...  To address the dynamic nature of the ever-evolving Web images, we develop a computationally efficient online algorithm to solve the proposed collaborative learning problem.  ... 
doi:10.1109/cvpr.2016.307 dblp:conf/cvpr/ZhangSYXLC16 fatcat:pprisiktbrdivibhx6ts26adyi

Groupyr: Sparse Group Lasso in Python

Adam Richie-Halford, Manjari Narayan, Noah Simon, Jason Yeatman, Ariel Rokem
2021 Journal of Open Source Software  
Groupyr: Sparse Group Lasso in Python. Journal of Open Source Software, 6(58), 3024. https://doi.org/10. 21105/joss.03024  ...  See the online documentation for a detailed description of the API and examples in both classification and regression settings.  ... 
doi:10.21105/joss.03024 fatcat:lzntzkq2izfgtljz4qppivwyce

The Sparse Regression Cube: A Reliable Modeling Technique for Open Cyber-Physical Systems

Hossein Ahmadi, Tarek Abdelzaher, Jiawei Han, Nam Pham, Raghu K. Ganti
2011 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems  
Our new modeling technique, called the Sparse Regression Cube, simultaneously (i) partitions sparse, high-dimensional measurements into subspaces within which reliable linear regression models apply and  ...  This paper develops a hierarchical modeling methodology for open cyber-physical systems that combines techniques in estimation theory with those in data mining to reliably capture complex system behavior  ...  Finally, we evaluate the scalability and efficiency of sparse regression cubes. A.  ... 
doi:10.1109/iccps.2011.20 dblp:conf/iccps/AhmadiAHPG11 fatcat:3aaqi2fvqjewphyybuylyvje34

OpenMVG: Open Multiple View Geometry [chapter]

Pierre Moulon, Pascal Monasse, Romuald Perrot, Renaud Marlet
2017 Lecture Notes in Computer Science  
open source algorithms to the community to compute sparse and dense detailed models (see some dense reconstructions from Fig. 7) .  ...  Open Source solutions.  ... 
doi:10.1007/978-3-319-56414-2_5 fatcat:2zhvzgnk4fcs5foju5p2gm3txq

QuTiP: An open-source Python framework for the dynamics of open quantum systems

J.R. Johansson, P.D. Nation, Franco Nori
2012 Computer Physics Communications  
We present an object-oriented open-source framework for solving the dynamics of open quantum systems written in Python.  ...  We give an overview of the basic structure for the framework before detailing the numerical simulation of open system dynamics.  ...  For the Landau-Zener problem this corresponds to Fig. 10 . 10 (Color online.) Bloch sphere representation of the Landau-Zener transition presented in Fig. 11 . 11 (Color online.)  ... 
doi:10.1016/j.cpc.2012.02.021 fatcat:5z3dfsseefdsxfogtqzgfht72a

Ontology Sparse Vector Learning Based on Accelerated First-Order Method

Yun Gao, Wei Gao
2015 Open Cybernetics and Systemics Journal  
In this article, we present a sparse vector learning algorithm for ontology similarity measure and ontology mapping by virtue of accelerated first-order technology.  ...  The simulation experimental results show that the new proposed algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in plant science and university application  ...  The general regression obtains an estimate of the sparse vector by solving the following optimization problem: min °p l( ) + 1 (3) where l( ) = 1 2 y V 2 2 is the loss term, 1 = 1 p i i = is the 1 l -norm  ... 
doi:10.2174/1874110x20150610e009 fatcat:ejkdkplzgva5fgytr5i3ug6aeq

OpenEDS2020: Open Eyes Dataset [article]

Cristina Palmero, Abhishek Sharma, Karsten Behrendt, Kapil Krishnakumar, Oleg V. Komogortsev, Sachin S. Talathi
2020 arXiv   pre-print
The dataset is available for download upon request at http://research.fb.com/programs/openeds-2020-challenge/.  ...  We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted  ...  Our gaze prediction approach relies on linear regression.  ... 
arXiv:2005.03876v1 fatcat:vzws3c3ddrfhvbfqgfwuob6f6q

JaTeCS an open-source JAva TExt Categorization System [article]

Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez
2017 arXiv   pre-print
JaTeCS is an open source Java library that supports research on automatic text categorization and other related problems, such as ordinal regression and quantification, which are of special interest in  ...  These libraries provide optimized data representation and fast operations on dense/sparse arrays and matrices, together with efficient algorithms for linear algebra.  ...  problems.  ... 
arXiv:1706.06802v1 fatcat:nxfaa5vsgvhrdarrrfczdhikuq

Exploring Big Data Analysis: Fundamental Scientific Problems

Zongben Xu, Yong Shi
2015 Annals of Data Science  
The paper outlines six open research problems on Big Data. It also reports some advances on current Big Data research, particularly in high-dimensional data and non-structured data processing.  ...  This paper tries to address some fundamental scientific problems in Big Data analysis, such as opportunities, challenges, and difficulties encountered in the analysis.  ...  The open research questions for HD problems are how to add priors so that a HD problem can be well defined; and how to find effective sparse modeling and etc.  ... 
doi:10.1007/s40745-015-0063-7 fatcat:5b76hoxjqvb2jaa5eiauib63zm
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