23,307

edits
# Changes

no edit summary

* [[Amoeba]]

* [[BBChess (SI)#DifferentialEvolution|Differential Evolution in BBChess]]

* [[Deuterium]]

* [[Falcon#GA|Genetic Algorithm in Falcon]]

* [[Stockfish's Tuning Method]]

<span id="Regression"></span>

=Regression=

[[FILE:Linear regression.svg|border|right|thumb|300px|[https://en.wikipedia.org/wiki/Linear_regression Linear Regression] <ref>Random data points and their [https://en.wikipedia.org/wiki/Linear_regression linear regression]. [https://commons.wikimedia.org/wiki/File:Linear_regression.svg Created] with [https://en.wikipedia.org/wiki/Sage_%28mathematics_software%29 Sage] by Sewaqu, November 5, 2010, [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref> ]]

[https://en.wikipedia.org/wiki/Regression_analysis Regression analysis] is a [https://en.wikipedia.org/wiki/Statistics statistical process] with a substantial overlap with machine learning to [https://en.wikipedia.org/wiki/Prediction predict] the value of an [https://en.wikipedia.org/wiki/Dependent_and_independent_variables Y variable] (output), given known value pairs of the X and Y variables. While [https://en.wikipedia.org/wiki/Linear_regression linear regression] deals with continuous outputs, [https://en.wikipedia.org/wiki/Logistic_regression logistic regression] covers binary or discrete output, such as win/loss, or win/draw/loss. Parameter estimation in regression analysis can be formulated as the [https://en.wikipedia.org/wiki/Mathematical_optimization minimization] of a [https://en.wikipedia.org/wiki/Loss_function cost or loss function] over a [https://en.wikipedia.org/wiki/Training_set training set] <ref>[https://en.wikipedia.org/wiki/Loss_function#Use_in_statistics Loss function - Use in statistics - Wkipedia]</ref>, such as [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] or [https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression cross-entropy error function] for [https://en.wikipedia.org/wiki/Binary_classification binary classification] <ref>"Using [https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression cross-entropy error function] instead of [https://en.wikipedia.org/wiki/Mean_squared_error sum of squares] leads to faster training and improved generalization", from [https://en.wikipedia.org/wiki/Sargur_Srihari Sargur Srihari], [http://www.cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.2-Training.pdf Neural Network Training] (pdf)</ref>. The minimization is implemented by [[Iteration|iterative]] optimization [[Algorithms|algorithms]] or [https://en.wikipedia.org/wiki/Metaheuristic metaheuristics] such as [https://en.wikipedia.org/wiki/Iterated_local_search Iterated local search], [https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm Gauss–Newton algorithm], or [https://en.wikipedia.org/wiki/Conjugate_gradient_method conjugate gradient method].

<span id="LinearRegression"></span>

==Linear Regression==

<span id="LogisticRegression"></span>

==Logistic Regression==

==Instances==

* [[Arasan#Tuning|Arasan's Tuning]]

* [[Ethereal]]

* [[Eval Tuning in Deep Thought]]

* [[FabChess]]

* [[Gosu]]

* [[Koivisto]]

* [[Minimax Tree Optimization]] (MMTO or the Bonanza-Method in [[Shogi]])

* [[Point Value by Regression Analysis]]

==1980 ...==

* [[Thomas Nitsche]] ('''1982'''). ''A Learning Chess Program.'' [[Advances in Computer Chess 3]]

* [[Donald H. Mitchell]] ('''1984'''). ''Using Features to Evaluate Positions in Experts' and Novices' Othello Games''. ~~Masters ~~Master thesis, Department of Psychology, [[Northwestern University]], Evanston, IL

==1985 ...==

* [[Tony Marsland]] ('''1985'''). ''Evaluation-Function Factors''. [[ICGA Journal#8_2|ICCA Journal, Vol. 8, No. 2]], [http://webdocs.cs.ualberta.ca/~tony/OldPapers/evaluation.pdf pdf]

* [[Peter Mysliwietz]] ('''1994'''). ''Konstruktion und Optimierung von Bewertungsfunktionen beim Schach.'' Ph.D. thesis (German)

==1995 ...==

* [[Michael Buro]] ('''1995'''). ''[~~http~~https://www.jair.org/~~papers~~index.php/jair/article/~~paper179.html ~~view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3

* [[Chris McConnell]] ('''1995'''). ''Tuning Evaluation Functions for Search''. [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=9B2A0CCA8B1AFB594A879799D974111A?doi=10.1.1.53.9742&rep=rep1&type=pdf pdf]

* [[Chris McConnell]] ('''1995'''). ''Tuning Evaluation Functions for Search'' (Talk), [http://www.cs.cmu.edu/afs/cs.cmu.edu/user/ccm/www/talks/tune.ps ps]

* [[Makoto Miwa]], [[Daisaku Yokoyama]], [[Takashi Chikayama]] ('''2007'''). ''Automatic Generation of Evaluation Features for Computer Game Players''. [http://cswww.essex.ac.uk/cig/2007/papers/2037.pdf pdf]

* [[Johannes Fürnkranz]] ('''2007'''). ''Recent advances in machine learning and game playing''. [http://www.oegai.at/journal.shtml ÖGAI Journal], Vol. 26, No. 2, Computer Game Playing, [https://www.ke.tu-darmstadt.de/~juffi/publications/ogai-07.pdf pdf]

* [https://dblp.org/pid/70/382.html Mohammed Shahid Abdulla], [[Shalabh Bhatnagar]] ('''2007'''). ''[https://link.springer.com/article/10.1007/s10626-006-0003-y Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes]''. [https://www.springer.com/journal/10626 Discrete Event Dynamic Systems], Vol.17, No.1 » [[SPSA]]

'''2008'''

* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2008'''). ''Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization''. [http://www.sigevo.org/gecco-2008/ GECCO '08], [https://arxiv.org/abs/1711.06839 arXiv:1711.06839]

* [[Borko Bošković]], [[Sašo Greiner]], [[Janez Brest]], [[Aleš Zamuda]], [[Viljem Žumer]] ('''2008'''). ''[https://link.springer.com/chapter/10.1007%2F978-3-540-68830-3_12 An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program]''. [https://link.springer.com/book/10.1007/978-3-540-68830-3 Advances in Differential Evolution], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]

'''2009'''

* [[Joel Veness]], [[David Silver]], [[William Uther]], [[Alan Blair]] ('''2009'''). ''[http://papers.nips.cc/paper/3722-bootstrapping-from-game-tree-search Bootstrapping from Game Tree Search]''. [http://nips.cc/ Neural Information Processing Systems (NIPS), 2009], [http://~~books~~jveness.~~nips~~info/publications/nips2009%20-%20bootstrapping%20from%20game%20tree%20search.~~cc~~pdf pdf] » [[Meep]] <ref>[http:/~~papers~~/~~files~~www.talkchess.com/~~nips22~~forum/~~NIPS2009_0508~~viewtopic.~~pdf pdf~~php?start=0&t=31667 A paper about parameter tuning] by [[Rémi Coulom]], [[CCC]], January 12, 2010</ref>

* [[Eli David|Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2009'''). ''Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions''. [http://www.sigevo.org/gecco-2009/ GECCO '09], [https://arxiv.org/abs/1711.06840 arXiv:1711.0684]

* [[Eli David|Omid David]] ('''2009'''). ''Genetic Algorithms Based Learning for Evolving Intelligent Organisms''. Ph.D. Thesis.

* [[Rémi Coulom]] ('''2011'''). ''[http://remi.coulom.free.fr/CLOP/ CLOP: Confident Local Optimization for Noisy Black-Box Parameter Tuning]''. [[Advances in Computer Games 13]] <ref>[http://www.talkchess.com/forum/viewtopic.php?p=421995 CLOP for Noisy Black-Box Parameter Optimization] by [[Rémi Coulom]], [[CCC]], September 01, 2011</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=40987 CLOP slides] by [[Rémi Coulom]], [[CCC]], November 03, 2011</ref>

* [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2011'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-642-31866-5_16 The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search]''. [[Advances in Computer Games 13]] » [[Shogi]]

* [[Mathematician#JCDuchi|John Duchi]], [[Mathematician#EHazan|Elad Hazan]], [[Mathematician#YSinger|Yoram Singer]] ('''2011'''). ''[https://dl.acm.org/doi/10.5555/1953048.2021068 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization]''. [https://en.wikipedia.org/wiki/Journal_of_Machine_Learning_Research Journal of Machine Learning Research], Vol. 12, [https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf pdf] » [https://en.wikipedia.org/wiki/Stochastic_gradient_descent#AdaGrad AdaGrad]

'''2012'''

* [[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]

* [[Akira Ura]], [[Makoto Miwa]], [[Yoshimasa Tsuruoka]], [[Takashi Chikayama]] ('''2013'''). ''[https://link.springer.com/chapter/10.1007/978-3-319-09165-5_18 Comparison Training of Shogi Evaluation Functions with Self-Generated Training Positions and Moves]''. [[CG 2013]], [https://pdfs.semanticscholar.org/6ad0/7167425539cf64e6bf420d7a28a1fc1047d6.pdf slides as pdf]

* [[Yoshikuni Sato]], [[Makoto Miwa]], [[Shogo Takeuchi]], [[Daisuke Takahashi]] ('''2013'''). ''[http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6402 Optimizing Objective Function Parameters for Strength in Computer Game-Playing]''. [http://www.informatik.uni-trier.de/~ley/db/conf/aaai/aaai2013.html#SatoMTT13 AAAI 2013]

* [[Shalabh Bhatnagar]], [~~[~~https://dblp.org/pid/31/10493.html H. L. Prasad~~]~~], [~~[~~https://scholar.google.co.in/citations?user=Q1YXWpoAAAAJ&hl=en L.A. Prashanth~~]~~] ('''2013'''). ''[~~http~~https://~~stochastic.csa.iisc~~link.~~ernet~~springer.~~in~~com/~~~shalabh~~book/~~book~~10.~~html ~~1007/978-1-4471-4285-0 Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods]''. [~~http~~https://www.springer.com/series/642 Lecture Notes in Control and Information Sciences], Vol. 434, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] » [[SPSA]]

* [[Tomáš Hřebejk]] ('''2013'''). ''Arimaa challenge - Static Evaluation Function''. Master Thesis, [https://en.wikipedia.org/wiki/Charles_University_in_Prague Charles University in Prague], [http://arimaa.com/arimaa/papers/ThomasHrebejk/Arimaa.pdf pdf] » [[Arimaa]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=58472 thesis on eval function learning in Arimaa] by [[Jon Dart]], [[CCC]], December 04, 2015</ref>

* [[Yoshikuni Sato]], [[Makoto Miwa]], [[Shogo Takeuchi]], [[Daisuke Takahashi]] ('''2013'''). ''[http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6402 Optimizing Objective Function Parameters for Strength in Computer Game-Playing]''. [http://www.informatik.uni-trier.de/~ley/db/conf/aaai/aaai2013.html#SatoMTT13 AAAI 2013]

* [[Hung-Jui Chang]], [[Jr-Chang Chen]], [[Gang-Yu Fan]], [[Chih-Wen Hsueh]], [[Tsan-sheng Hsu]] ('''2018'''). ''Using Chinese dark chess endgame databases to validate and fine-tune game evaluation functions''. [[ICGA Journal#40_2|ICGA Journal, Vol. 40, No. 2]] » [[Chinese Dark Chess]], [[Endgame Tablebases]]

* [[Wen-Jie Tseng]], [[Jr-Chang Chen]], [[I-Chen Wu]], [[Tinghan Wei]] ('''2018'''). ''Comparison Training for Computer Chinese Chess''. [https://arxiv.org/abs/1801.07411 arXiv:1801.07411] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=52861&start=7 Re: multi-dimensional piece/square tables] by Tony P., [[CCC]], January 28, 2020 » [[Piece-Square Tables]]</ref>

* [[Jeremy Rapin]], [[Olivier Teytaud]] ('''2018'''). ''Nevergrad - A gradient-free optimization platform''. [https://github.com/facebookresearch/nevergrad GitHub - facebookresearch/nevergrad: A Python toolbox for performing gradient-free optimization]

==2020 ...==

* [[Andrew Grant]] ('''2020'''). ''Evaluation & Tuning in Chess Engines''. [https://github.com/AndyGrant/Ethereal/blob/master/Tuning.pdf pdf] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74877 Evaluation & Tuning in Chess Engines] by [[Andrew Grant]], [[CCC]], August 24, 2020</ref>

=Forum Posts=

* [http://www.talkchess.com/forum/viewtopic.php?t=62012 CLOP: when to stop?] by [[Erin Dame]], [[CCC]], November 07, 2016 » [[CLOP]]

: [http://www.talkchess.com/forum/viewtopic.php?t=62012&start=6 Re: CLOP: when to stop?] by [[Álvaro Begué]], [[CCC]], November 08, 2016 <ref>[https://en.wikipedia.org/wiki/Limited-memory_BFGS Limited-memory BFGS from Wikipedia]</ref>

* [http://www.talkchess.com/forum/viewtopic.php?t=62056 C++ code for tuning evaluation function parameters] by [[Álvaro Begué]], [[CCC]], November 10, 2016 » [[RuyTune]] ~~<ref>[https://bitbucket.org/alonamaloh/ruy_tune alonamaloh / ruy_tune — Bitbucket] by [[Álvaro Begué]]</ref>~~

'''2017'''

* [http://www.talkchess.com/forum/viewtopic.php?t=63408 improved evaluation function] by [[Alexandru Mosoi]], [[CCC]], March 11, 2017 » [[Texel's Tuning Method]], [[Zurichess]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=68753 methods for tuning coefficients] by [[Stuart Cracraft]], [[CCC]], October 28, 2018

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69035 Particle Swarm Optimization Code] by [[Erik Madsen]], [[CCC]], November 24, 2018 » [[MadChess]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69207 Gradient Descent Introduction] by [[Michael Hoffmann|Desperado]], [[CCC]], December 09, 2018

'''2019'''

* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=69532 Automated tuning... finally... (Topple v0.3.0)] by [[Vincent Tang]], [[CCC]], January 08, 2019 » [[Topple]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74209 Great input about Bayesian optimization of noisy function methods] by [[Vivien Clauzon]], [[CCC]], June 16, 2020

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74877 Evaluation & Tuning in Chess Engines] by [[Andrew Grant]], [[CCC]], August 24, 2020 » [[Ethereal]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74955 Train a neural network evaluation] by [[Fabio Gobbato]], [[CCC]], September 01, 2020 » [[NNUE]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75012 Speeding Up The Tuner] by [[Dennis Sceviour]], [[CCC]], September 06, 2020

* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75104 Yet another parameter tuner using optuna framework] by [[Ferdinand Mosca]], [[CCC]], September 14, 2020

: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75104&start=15 Re: Yet another parameter tuner using optuna framework] by [[Karlson Pfannschmidt]], [[CCC]], September 16, 2020 <ref>[https://optunity.readthedocs.io/en/latest/user/solvers/TPE.html Tree-structured Parzen Estimator — Optunity 1.1.0 documentation]</ref>

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75234 evaluation tuning - where to start?] by [[Maksim Korzh]], [[CCC]], September 27, 2020

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75267 How to calculate piece weights with logistic regression?] by [[Maksim Korzh]], [[CCC]], October 01, 2020 » [[Automated Tuning#Regression|Regression]], [[Point Value by Regression Analysis]], [[Point Value]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75411 Unsupervised reinforcement tuning from zero] by Madeleine Birchfield, [[CCC]], October 16, 2020 » [[Reinforcement Learning]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76024 Laskas parameter optimizer] by [[Ferdinand Mosca]], [[CCC]], December 09, 2020

'''2021'''

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76227 How to calc the derivative for gradient descent?] by Brian Neal, [[CCC]], January 04, 2021

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76238 Help with Texel's tuning] by [[Maksim Korzh]], [[CCC]], January 05, 2021 » [[Texel's Tuning Method]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76265 Tapered Evaluation and MSE (Texel Tuning)] by [[Michael Hoffmann]], [[CCC]], January 10, 2021 » [[Texel's Tuning Method]], [[Tapered Eval]]

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76288 Training data] by [[Michael Hoffmann]], [[CCC]], January 12, 2021

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76292 Why using the game result instead of evaluation scores] by [[Michael Hoffmann]], [[CCC]], January 12, 2021

* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76294 Using Mini-Batch for tunig] by [[Michael Hoffmann]], [[CCC]], January 12, 2021

=External Links=

: [https://en.wikipedia.org/wiki/Self-tuning Self-tuning from Wikipedia]

==Engine Tuning==

* [https://www.3dkingdoms.com/chess/learning.html Automatic Tuning & Learning for Slow Chess Blitz Classic] by by [[Jonathan Kreuzer]] » [[Slow Chess]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74184 Learning/Tuning in SlowChess Blitz Classic] by [[Jonathan Kreuzer]], [[CCC]], June 15, 2020</ref>

* [https://chess-tuning-tools.readthedocs.io/en/latest/ Chess Tuning Tools] by [[Karlson Pfannschmidt]]

* [http://rebel13.nl/rebel13/pet.html Practical Engine Tuning] by [[Ed Schroder|Ed Schröder]], June 2018 » [[Strategic Test Suite]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=67831 Introducing PET] by [[Ed Schroder|Ed Schröder]], [[CCC]], June 27, 2018</ref>

==Optimization==

* [https://en.wiktionary.org/wiki/optimization optimization - Wiktionary]

: [https://en.wikipedia.org/wiki/Stochastic_approximation Stochastic approximation from Wikipedia]

: [https://en.wikipedia.org/wiki/Stochastic_gradient_descent Stochastic gradient descent from Wikipedia]

: [https://en.wikipedia.org/wiki/Stochastic_gradient_descent#AdaGrad AdaGrad from Wikipedia]

==Machine Learning==

* [https://en.wikipedia.org/wiki/Machine_learning Machine learning from Wikipedia]

* [https://en.wikipedia.org/wiki/Tikhonov_regularization Tikhonov regularization (Ridge regression) from Wikipedia]

==Code==

* [https://~~bitbucket~~github.~~org~~com/~~alonamaloh~~facebookresearch/~~ruy_tune alonamaloh ~~nevergrad GitHub - facebookresearch/ ~~ruy_tune — Bitbucket] » [[RuyTune]] by [[Álvaro Begué]~~nevergrad: A Python toolbox for performing gradient-free optimization]* ~~<span id="Rockstar"></span>~~[https://github.com/~~lantonov~~fsmosca/~~Rockstar Rockstar~~Optuna-Game-Parameter-Tuner GitHub - fsmosca/Optuna-Game-Parameter-Tuner: ~~Implementation of ROCK* algorithm (Gaussian kernel regression + natural gradient descent) for optimisation | GitHub~~A game search and evaluation parameter tuner using optuna framework] by [[~~Lyudmil Antonov]] and [[Joona Kiiski~~Ferdinand Mosca]] » [[~~Automated Tuning#ROCK|ROCK*~~Deuterium]] <ref>[http://www.talkchess.com/~~forum~~forum3/viewtopic.php?f=2&t=~~65045 ROCK* black-box optimizer for chess~~75104 Yet another parameter tuner using optuna framework] by [[~~Jon Dart~~Ferdinand Mosca]], [[CCC]], ~~August 31~~September 14, ~~2017~~2020</ref>* [https://github.com/~~zamar~~fsmosca/~~spsa SPSA Tuner for Stockfish Chess Engine | ~~Lakas GitHub- fsmosca/Lakas: Game parameter optimizer using nevergrad framework] by [[~~Joona Kiiski~~Ferdinand Mosca]]<ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76024 Laskas parameter optimizer] ~~» ~~by [[~~Stockfish~~Ferdinand Mosca]], [[~~Stockfish's Tuning Method~~CCC]]~~* [https:~~, December 09, 2020</~~/github.com/scikit-optimize/scikit-optimize GitHub - scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface]~~ref>

* [https://github.com/kiudee/bayes-skopt GitHub - kiudee/bayes-skopt: A fully Bayesian implementation of sequential model-based optimization] by [[Karlson Pfannschmidt]] » [[Fat Fritz]] <ref>[https://en.chessbase.com/post/fat-fritz-update-and-fat-fritz-jr Fat Fritz 1.1 update and a small gift] by [[Albert Silver]]. [[ChessBase|ChessBase News]], March 05, 2020</ref>

* [https://github.com/kiudee/chess-tuning-tools GitHub - kiudee/chess-tuning-tools] by [[Karlson Pfannschmidt]] » [[Leela Chess Zero]]

* [https://github.com/krasserm/bayesian-machine-learning GitHub - krasserm/bayesian-machine-learning: Notebooks about Bayesian methods for machine learning] by [https://krasserm.github.io/ Martin Krasser] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74209 Great input about Bayesian optimization of noisy function methods] by [[Vivien Clauzon]], [[CCC]], June 16, 2020</ref>

* [https://github.com/thomasahle/noisy-bayesian-optimization GitHub - thomasahle/noisy-bayesian-optimization: Bayesian Optimization for very Noisy functions] by [[Thomas Dybdahl Ahle]] » [[FastChess]]

* [https://github.com/scikit-optimize/scikit-optimize GitHub - scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface]

* <span id="Rockstar"></span>[https://github.com/lantonov/Rockstar Rockstar: Implementation of ROCK* algorithm (Gaussian kernel regression + natural gradient descent) for optimisation | GitHub] by [[Lyudmil Antonov]] and [[Joona Kiiski]] » [[Automated Tuning#ROCK|ROCK*]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=65045 ROCK* black-box optimizer for chess] by [[Jon Dart]], [[CCC]], August 31, 2017</ref>

* [https://github.com/zamar/spsa SPSA Tuner for Stockfish Chess Engine | GitHub] by [[Joona Kiiski]] » [[Stockfish]], [[Stockfish's Tuning Method]]

==Misc==

* [[:Category:The Next Step Quintet|The Next Step Quintet]] feat. [http://www.tivonpennicott.com/ Tivon Pennicott] - [http://www.discogs.com/Next-Step-Quintet-The-Next-Step-Quintet/release/4970720 Regression], [https://el-gr.facebook.com/KerameioBar KerameioBar] [https://en.wikipedia.org/wiki/Athens Athens], [https://en.wikipedia.org/wiki/Greece Greece], September 2014, [https://en.wikipedia.org/wiki/YouTube YouTube] Video