Graphs and Random Walks
A. Directed Graphs
by Nisheeth K. Vishnoi
in Foundations and Trends in Theoretical Computer Science Vol. 8, Nos. 1-2 (2012) 1-141
https://theory.epfl.ch/vishnoi/Lxb-Web.pdf
by F Bonchi, P Esfandiar, DF Gleich, C Greif
note: Katz scores using Lanczos and theory of moments. (refers to Fouss)
https://arxiv.org/pdf/1104.3791
Learning from Labeled and Unlabeled Data on a Directed Graph
by Dengyong Zhou & Jiayuan Huang & Bernhard Schölkopf
in ICML '05:
https://www.semanticscholar.org/paper/Learning-from-labeled-and-unlabeled-data-on-a-graph-Zhou-Huang/df95ae968cb0b722143f6000fa0dc7ce21cc35e2
https://pure.mpg.de/rest/items/item_1791381_2/component/file_3175263/content
by G. Golnari, Z.-L. Zhang, & D. Boley.
in Linear Algebra and Appl., 564:126--158, 2019.
https://www.sciencedirect.com/science/article/abs/pii/S0024379518305470
by Rasmus Kyng, Sushant Sachdeva
2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), 2016.
https://arxiv.org/abs/1605.02353
by Mikhail Belkin & Partha Niyogi
in Neural Computation 15 pp. 1373-1396, 2003
https://www2.imm.dtu.dk/projects/manifold/Papers/Laplacian.pdf
https://www.mitpressjournals.org/doi/pdf/10.1162/089976603321780317
by Jeffrey J. Hunter
https://arxiv.org/abs/1208.4716
B. Graphs: Random Walks on Graphs
by Daniel Jarne Ornia, Pedro J Zufiria, Manuel Mazo Jr.
arXiv:2103.07714
by Boley, D., Ranjan, G., Zhang, Z.-L. ;
in Linear Algebra and Appl., 435, 224-242. (2011). ;
note: mainly showing how certain commutes quantities for undirected graphs work also for digraphs
https://www.sciencedirect.com/science/article/pii/S0024379511000668
https://www-users.cs.umn.edu/~boley/publications/papers/Laplacian10-LAA.pdf
by Rushabh Patel, Andrea Carron, Francesco Bullo
in SIMAX 37(3), 2016
https://epubs.siam.org/doi/10.1137/15M1010737
by Yara Khaluf, Marco Dorigo
in ACM Trans Auto.n and Adapt. Sys. 11(2) 2016
https://dl.acm.org/doi/abs/10.1145/2870637
by Christopher Zhang
May 9, 2017
arXiv:2302.01963
by Y. Khaluf, M. Pace, F. Rammig, M. Dorigo
https://dl.acm.org/doi/abs/10.1145/2870637
https://iridia.ulb.ac.be/~mbiro/paperi/GarBir2016eeee.pdf
C. Graphs: Extensions and Methods
by Lek-Heng Lim
arxiv:1507.05379
by J. F. Lutzeyer, [26]A. T. Walden
https://arxiv.org/abs/1712.03769
by [30]Dongdong She, [31]Abhishek Shah, [32]Suman Jana
https://arxiv.org/abs/2203.12064
by M Littman, A R Cassandra, L P Kaebling
in ICML 1995
https://people.cs.umass.edu/~barto/courses/cs687/Cassandra-etal-POMDP.pdf
by G W Stewart
in SIMAX 2001
https://epubs.siam.org/doi/pdf/10.1137/S0895479800371529
by Haoyu Bai, David Hsu, Wee Sun Lee
in IJRR 2014
https://www.roboticsproceedings.org/rss09/p18.pdf
by Aaron Sidford (slides)
in FOCS 2018 Workshop
(no link)
by Marcel Böhme, Van-Thuan Pham, Abhik Roychoudhury
in ACM SIGSAC Conf on Comp. and Comm Security 2016
https://dl.acm.org/doi/10.1145/2976749.2978428
https://mboehme.github.io/paper/TSE18.pdf
D. Graphs & Linear Algebra: Classic Results
by Shi, J. and Malik, J.
in Pattern Analysis and Machine Intelligence, IEEE Transactions on vol 22#8:88-905, Aug 2000
https://www.cs.berkeley.edu/~malik/papers/SM-ncut.pdf
by Amy N. Langville and Carl D. Meyer
in SIAM Review, Vol. 47, No. 1 (Mar., 2005), pp. 135-161
note: HITS Pagerank SALSA
https://www.jstor.org/stable/pdf/20453606.pdf;
by Fan Chung
note: theory isoparimetric number. mentions expander graphs
https://www.math.ucsd.edu/~fan/wp/cheeger.pdf
by E Estrada, D Higham.
in SIAM Review (2010)
note: centrality+commincability+betweenness; spectral clustering; resolvant vs exponential
https://epubs.siam.org/doi/10.1137/090761070
by Ulrik Brandes & Stephen P.Borgatti & Linton C.Freeman
in Social Networks 44 pp. 153-159, 2016
note: tensor of shortest paths.
DOI: https://doi.org/10.1016/j.socnet.2015.08.003
https://www.sciencedirect.com/science/article/pii/S0378873315000738
by Daniel A. Spielman, Nikhil Srivastava
https://arxiv.org/abs/0803.0929
by Ioannis Koutis & Gary Miller & Richard Peng
in FOCS11
https://arxiv.org/abs/1102.4842
by Michele Benzi & Christine Klymko
in SIMAX 36 2 pp. 686-706, 2015
https://epubs.siam.org/doi/abs/10.1137/130950550
by Inderjit S. Dhillon.
in KDD 2001
http://portal.acm.org/citation.cfm?id=502512.502550
E. Graphs and Machine Learning
by Joan Bruna & Wojciech Zaremba & Arthur Szlam & and Yann LeCun
in Proceedings of the 2nd International Conference on Learning Representations, 2013
https://arxiv.org/abs/1312.6203
by Michaël Defferrard and Xavier Bresson and Pierre Vandergheynst
https://arxiv.org/abs/1606.09375
by Mikael Henaff & Joan Bruna & Yann LeCun, 2015
https://arxiv.org/abs/1506.05163
by Thomas N. Kipf, Max Welling
in ICLR 2017
https://arxiv.org/abs/1609.02907
Machine Learning
F. ML: Deep Neural Networks
by Yan LeCun, Leon Bottou Yoshua Bengio, Patrick Haffner
in Proc. IEEE, November 1998.
https://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
G. ML: Convolutional Neural Nets
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
ImageNet Classification with Deep Convolutional Neural Networks
by Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
in NIPS 2012.
https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
by Chaoyue Liu & Libin Zhu & Mikhail Belkin
https://arxiv.org/abs/2003.00307
Convolutional Networks and Applications in Vision
by Yann LeCun & Koray Kavukvuoglu & Clement Farabet
in Proc. International Symposium on Circuits and Systems (ISCAS'10) (IEEE), 2010
https://yann.lecun.com/exdb/publis/pdf/lecun-iscas-10.pdf
https://ieeexplore.ieee.org/document/5537907 (might require UofM login)
H. ML: Latent
by Aditya Grover & Jure Leskovec
in KDD, 2016
https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf
by Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros
https://arxiv.org/abs/1811.10959
by Sonia Joseph, Praneet Suresh, Ethan Goldfarb, Lorenz Hufe, Yossi Gandelsman, Robert Graham, Danilo
https://arxiv.org/abs/2504.08729
by Ryan Kortvelesy, Steven Morad, Amanda Prorok
in AAMAS 2023, May 29--June 2, 2023, London, United Kingdom
https://arxiv.org/abs/2302.12826
Efficient Estimation of Word Representations in Vector Space
by Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
https://arxiv.org/abs/1301.3781
I. ML: Learning
by Andrey Gromov
https://arxiv.org/abs/2301.02679
by Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis & Pushmeet Kohli
in Nature volume 610, pages 47–53 (2022)
https://www.nature.com/articles/s41586-022-05172-4
J. ML: Transformers
Attention Is All You Need
by Ashish Vaswani, Llion Jones, Noam Shazeer, Niki Parmar, Aidan N. Gomez, Jakob Uszkoreit, \L ukasz Kaiser, Illia Polosukhin
https://arxiv.org/abs/1706.03762
by Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, Joao Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
https://arxiv.org/abs/2212.07677
K. ML: Data Leakage
by Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
https://arxiv.org/abs/2103.06819
by Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani
news article: https://www.bleepingcomputer.com/news/security/new-ai-attack-hides-data-theft-prompts-in-downscaled-images/
https://arxiv.org/abs/2206.07758
by Erwin Quiring, David Klein, Daniel Arp, Martin Johns, and Konrad Rieck.
https://www.usenix.org/conference/usenixsecurity20/presentation/quiring
L. ML: Linear Algebra
by Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen
arXiv:2106.09685
M. ML: Generalization Performance
by Andrew Cotter , Heinrich Jiang , and Karthik Sridharan
arXiv:1804.06500
by Chaoyue Liu , Libin Zhu , and Mikhail Belkin
arXiv:2003.00307
by Gal Vardi
in CACM 2023
https://arxiv.org/abs/2208.12591
https://cacm.acm.org/research/on-the-implicit-bias-in-deep-learning-algorithms/
by Moritz Hardt, Benjamin Recht, Yoram Singer.
in JMLR 2016
https://proceedings.mlr.press/v48/hardt16.pdf
by Taoan Huang , Aaron Ferber , Yuandong Tian , Bistra Dilkina , Benoit Steiner
https://proceedings.mlr.press/v202/huang23g/huang23g.pdf
by Lei Wu, Chao Ma, Weinan E
in NeurIPS 2025
https://proceedings.neurips.cc/paper_files/paper/2018/file/6651526b6fb8f29a00507de6a49ce30f-Paper.pdf
by Arthur Jacot, Franck Gabriel, Clément Hongler
in NeurIPS 2025
https://proceedings.neurips.cc/paper_files/paper/2018/file/5a4be1fa34e62bb8a6ec6b91d2462f5a-Paper.pdf
by Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
in NeurIPS 2020
https://proceedings.neurips.cc/paper_files/paper/2018/file/6651526b6fb8f29a00507de6a49ce30f-Paper.pdf
by Tomer Galanti, Liane Galanti, Mengjia Xu, Tomaso Poggio
in NeurIPS 2023
https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf
by Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Broeck.
in ICML, pages 5502-5511. PMLR, 2018.
https://proceedings.mlr.press/v80/xu18h.html
by Yunwen Lei, Tao Sun, Mingrui Liu
https://arxiv.org/abs/2310.01139
by Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter
https://arxiv.org/abs/2106.13799
by Arwen V Bradley, Carlos A Gomez-Uribe, and Manish Reddy Vuyyuru
https://machinelearning.apple.com/research/shift-curvature
N. ML: Physics Induced Neural Nets
by M. Raissi , P. Perdikaris , G.E. Karniadakis
in Journal of Computational Physics 378 (2019) 686--707
https://www.sciencedirect.com/science/article/pii/S0021999118307125
by Levi D. McClenny* Ulisses Braga-Neto
arXiv:2009.04544
by A. Dener , M.A. Miller , R.M. Churchill , T. Munson , C.S. Chang
arXiv:2009.07330
by Florian Tram , Reza Shokri , Ayrton San Joaquin , Hoang Le , Matthew Jagielski , Sanghyun Hong , Nicholas Carlini
in Proc 2022 ACM SIGSAC Conference on Computer and Communications Security,
arXiv:2204.00032
O. ML: Mechanistic Interpretability -- Sparse Auto Encoders
by Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey
arXiv:2309.08600
Adam Karvonen
BLOG
https://adamkarvonen.github.io/machine_learning/2024/06/11/sae-intuitions.html
by Leonard Bereska, Efstratios Gavves
arXiv:2404.14082
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt
arXiv:2301.05217
by Hanqi Yan, Yanzheng Xiang, Guangyi Chen, Yifei Wang, Lin Gui, Yulan He
arXiv:2406.17969
by Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, Adrià Garriga-Alonso
in NeurIPS 2023
https://proceedings.neurips.cc/paper_files/paper/2023/hash/34e1dbe95d34d7ebaf99b9bcaeb5b2be-Abstract-Conference.html
by Ahmed Abdulaal, Hugo Fry, Nina Montaña-Brown, Ayodeji Ijishakin, Jack Gao, Stephanie Hyland, Daniel C. Alexander, Daniel C. Castro
arXiv:2410.03334
by Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, Ziyu Yao
arXiv:2407.02646
by Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Bloom, Stella Biderman, Adria Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, Stephen Casper, Max Tegmark, William Saunders, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath
arXiv:2501.16496
CS294A Lecture notes
by Andrew Ng
https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf
by Yuxiao Li , Eric J Michaud , David D Baek , Joshua Engels , Xiaoqing Sun , Max Tegmark
in Entropy (Basel). 2025 Mar 27;27(4):344. doi: 10.3390/e27040344
https://pmc.ncbi.nlm.nih.gov/articles/PMC12025678/
by Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu
arXiv:2406.04093
by Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince, Matthew Kowal, Victor Boutin, Isabel Papadimitriou, Binxu Wang, Martin Wattenberg, Demba Ba, Talia Konkle
https://arxiv.org/abs/2502.12892
by Nimrod Berman, Assaf Hallak, Assaf Shocher
https://arxiv.org/abs/2510.08570
P. ML: Reinforcement Learning
by Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, Kristian Hartikainen, Vikash Kumar, Pieter Abbeel, Henry Zhu, George Tucker, Abhishek Gupta, Sergey Levine
arXiv:1812.05905
Q. ML: Diffusion Models
by Calvin Luo
video
https://arxiv.org/abs/2208.11970
by Jonathan Ho, Ajay Jain, Pieter Abbeel
https://arxiv.org/abs/2006.11239
by Jiaming Song, Chenlin Meng, Stefano Ermon
https://arxiv.org/abs/2010.02502
R. ML: Recent Papers
by DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong
https://arxiv.org/abs/2405.04434
by beren, Sid Black (2022)
https://www.lesswrong.com/posts/mkbGjzxD8d8XqKHzA/the-singular-value-decompositions-of-transformer-weight
byNelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah
in Anthropic
https://transformer-circuits.pub/2021/framework/index.html
by Marlène Careil, Yohann Benchetrit, Jean-Rémi King
https://arxiv.org/abs/2505.14556
by Qiang Liu
https://www.cs.utexas.edu/~lqiang/PDF/ksd_short.pdf
by Guan Zhe Hong, Bhavya Vasudeva, Vatsal Sharan, Cyrus Rashtchian, Prabhakar Raghavan, Rina Panigrahy
https://arxiv.org/abs/2506.16975
Optimization
S. Optimization in ML
by Sebastian Ruder
2017
https://arxiv.org/abs/1609.04747
by Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, Can Yaras
https://arxiv.org/abs/2503.19859
by Aditya Sharma
BLOG
https://www.linkedin.com/pulse/gradient-descent-introduction-ankisha-sharma
https://www.udacity.com/blog/2025/02/gradient-descent-optimization-a-simple-guide-for-beginners.html
https://adityashrm21.github.io/All-About-Gradient-Descent/
by Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
https://www.cs.cornell.edu/~sridharan/convex.pdf
by Shai Shalev-Shwartz, Nathan Srebro, Karthik Sridharan
https://home.ttic.edu/~nati/Publications/nonlinearTR.pdf
T. Matrix Sketching
by Edo Liberty
in KDD '13:
https://www.cs.yale.edu/homes/el327/papers/simpleMatrixSketching.pdf
by Joel A. Tropp & Alp Yurtsever & Madeleine Udell & Andvolkan Cevher
in SIMAX Vol. 38, No. 4, pp. 1454â1485, 2017
[38]https://epubs.siam.org/doi/abs/10.1137/17M1111590
https://epubs.siam.org/doi/epdf/10.1137/17M1111590
by Falcone, Roberta & Anderlucci, Laura & Montanari, Angela
in Data Mining and Knowledge Discovery 36 1 pp. 174-208, 2022
[40]https://doi.org/10.1007/s10618-021-00791-3
https://link.springer.com/article/10.1007/s10618-021-00791-3