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-- you are using an outdated browser. please upgrade your browser to improve your experience. research scientist, google i am mostly working on federated learning and am currently based in seattle. previously, i completed my phd at the university of edinburgh under supervision of peter richtárik and i enjoyed support through google phd fellowship in optimization algorithms. social profiles -- -- -- news resume publications blog (slovak) contact uw visit jan 30, 2018 i gave a talk at university of washington, seattle about our team's work on federated learning. my slides from the talk. nips 2017 dec 3, 2017 i am present at nips 2017 , let me know if you want to meet. google jul 6, 2017 in my next steps, i am going to move to google seattle in role of research scientist at the end of august. i am going to work mainly on federated learning to which i contributed during my two summer internships and since january 2017 as visiting researcher. phd complete jul 6, 2017 i successfully defended my phd thesis several days ago. if interested, you can find my thesis here . i thank for my accomplishements to many amazing researchers i had the opportunity to meet and work with during the last four years. thank you! i plan to stay actively involved in research, so i hope to meet you at conferences again! since the last update, i visited peter at kaust , gave a talk at siam conference on optimization in vancouver, and several of our released papers have been accepted for publication in journals. late nips update dec 17, 2016 wow, nips this year was busier than ever. thanks everybody for fruitful discussions and setting up potential collaborations! new paper out nov 25, 2016 we have released a new paper, randomized distributed mean estimation: accuracy vs communication , joint with peter richtárik . we consider the problem of distributed computation of arithmetic average of vectors, under constraints on communication. we propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. federated learning oct 19, 2016 we have recently released two new papers in collaboration with google, focusing on federated learning - a setting where we need to learn from massively decentralized data , such as when the training data reside on phones of users that generated them. the first work, federated optimization: distributed machine learning for on-device intelligence describes the setting in detail, and proposes algorithms with particular focus on convex setting. in federated learning: strategies for improving communication efficiency , we address the communication efficiency of systems for federated learning. in the best case, we were able to train a deep model, while the amount of bits communicated over network was smaller than the original data itself. learning, privacy, mobile data sep 13, 2016 i am attending learning, privacy, and mobile data workshop organized by google in seattle. organized by the team i interned with, the goal is to better understand interactions between the fields of machine learning, optimization, privacy and cryptography to facilitate move to mobile computing devices, and fundamentally change the way machine learning is deployed. ima birmingham sep 7, 2016 together with colleagues from our research group, we organized two minisymposia at the 5th ima conference on numerical linear algebra and optimization in birmingham. i spoke about our recent work, aide: fast and communication efficient distributed optimization . new paper out [aide] aug 29, 2016 in collaboration with sashank reddi , barnabás póczos and alex smola from carnegie mellon university, and peter richtárik , we propose and analyse a new algorithm, aide: fast and communication efficient distributed optimization , in short an accelerated inexact dane algotihm. earlier this summer, i presented a poster on aide at the internalional conference on machine learning 2016 in new york. phd fellowship summit aug 23, 2016 i am speaking at the google phd fellowship summit in mountain view, about my experience of merging research efforts in academia and in google. also, i met many passionate young researchers that enjoy the same support by google during their studies, a very motivating experience. google summer internship may 16, 2016 until end of august, i am at google seattle, doing a summer internship, working mainly with brendan mcmahan . i am going to be working some of the algorithmic chellenges in federated optimization/learning --- decentralized and highly unbalanced datasets. future of humanity institute mar 14, 2016 as of today, i am visiting the future of humanity institute in oxford, trying to better understand where the ideas regarding ai safety stand at the moment and how i might be able to contribute. i highly recommend reading superintelligence , if you haven't yet. optimization without borders feb 7, 2016 i am excited to be attending the optimization without borders in beautiful les houches under mount blanc. the event is dedicated to the 60th birthday of yuri nesterov and his lifelong contributions to the field. late updates jan 11, 2016 thanks to everybody at nips for the amazing atmosphere, inspiring discussions and many new ideas. i presented stop wasting my gradients: practical svrg , which i co-authored with reza babanezhad, mohamed osama ahmed, alim virani, mark schmidt and scott sallinen from university of british columbia. i also presented a poster on federated optimization: distributed optimization beyond the datacenter . in optimization in machine learning workshop. before that (nov 25-27) i spoke at one of the scoping workshops of the alan turing institute , which is being set up, with the topic distributed machine learning and optimization . even before that (nov 23) i briefly visited the future of humanity institute in oxford, and discussed some of the long term dangers arising from technological development. for those of you who haven't noticed it yet, i highly recommend nick's book on superintelligence . the book is free of bold unjustified predictions, and rather summarizes the open problems and discussed current ideas. federated optimization nov 14, 2015 new preliminary paper out! federated optimization: distributed optimization beyond the datacenter , which is based on my summer work in google with brendan mcmahan . we introduce a new important setting, in which data are distributed across a very large number of computers, each having access only to few data points. this is primarily motivated by the setting, where users keep their data on their devices, but the goal is still to train a high quality global model. practical svrg nov 10, 2015 new paper out! stop wasting my gradients: practical svrg , which i co-authored with mark schmidt and his group has been accepted to the nips conference. i am looking forward to meeting you all there! informs annual meeting nov 1, 2015 not many people showed up in halloween costumes at informs annual meeting in philadelphia. never mind, i am feeling overwhelmed by the 80 parallel sessions going on. i am giving a talk on federated optimization on monday. slides are here , paper available soon (available upon request). alma mater oct 21, 2015 i am gave a talk at seminar at comenius university in bratislava, speaking about s2gd and work on federated optimization i have done during summer in google, the next step in large-scale distributed optimization. preliminary paper coming out soon... ismp 2015 jul 17, 2015 i am giving a talk today at the ismp conference in pittsburgh. so far very inspirational event, in a suprisingly pleasant city. google summer internship may 26, 2015 until end of august, i am at google, doing a summer internship, working mainly with brendan mcmahan . hope there's lots of cool stuff to learn! optimization & big data 2015 may 6, 2015 at home, in edinburgh, we are organizing three day workshop - optimization & big data 2015 - focused on l

URL analysis for jakubkonecny.com

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http://www.jakubkonecny.com/files/14_s2gd_chicago.pptx
http://www.jakubkonecny.com/files/15-05_arbitrary_local_solvers.ppsx
http://www.jakubkonecny.com/files/aide_poster.pdf
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Domain Name: JAKUBKONECNY.COM
Registry Domain ID: 1844023127_DOMAIN_COM-VRSN
Registrar WHOIS Server: whois.godaddy.com
Registrar URL: http://www.godaddy.com
Updated Date: 2016-11-27T03:41:34Z
Creation Date: 2014-01-23T22:34:51Z
Registry Expiry Date: 2020-01-23T22:34:51Z
Registrar: GoDaddy.com, LLC
Registrar IANA ID: 146
Registrar Abuse Contact Email: [email protected]
Registrar Abuse Contact Phone: 480-624-2505
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Domain Status: clientUpdateProhibited https://icann.org/epp#clientUpdateProhibited
Name Server: NS1.WEBSUPPORT.SK
Name Server: NS2.WEBSUPPORT.SK
Name Server: NS3.WEBSUPPORT.SK
DNSSEC: unsigned
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  REGISTRAR GoDaddy.com, LLC

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DOMAIN

  NAME jakubkonecny.com

  CHANGED 2016-11-27

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