- For other papers predating the explosion of LLMs, see also
- https://www-users.cse.umn.edu/~boley/8363-22f/PaperList.htm (from 3 years ago)
Graphs and Random Walks
A. Directed Graphs
- Lx = b Laplacian Solvers and Their Algorithmic Applications
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 - Fast matrix computations for pairwise and columnwise commute times and Katz scores
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 - 09/15 Monte Mahlum________________________
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 - Markov fundamental tensor and its applications to network analysis.
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 - Approximate Gaussian Elimination for Laplacians: Fast, Sparse, and Simple
by Rasmus Kyng, Sushant Sachdeva
2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), 2016.
https://arxiv.org/abs/1605.02353 - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
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 - The Role of Kemeny's Constant in Properties of Markov Chains
![[new]](new.gif)
by Jeffrey J. Hunter
https://arxiv.org/abs/1208.4716
B. Graphs: Random Walks on Graphs
- Mean Field Behaviour of Collaborative Multi-Agent Foragers
by Daniel Jarne Ornia, Pedro J Zufiria, Manuel Mazo Jr.
arXiv:2103.07714 - Commute times for a directed graph using an asymmetric Laplacian. ;
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 - The Hitting Time of Multiple Random Walks
by Rushabh Patel, Andrea Carron, Francesco Bullo
in SIMAX 37(3), 2016
https://epubs.siam.org/doi/10.1137/15M1010737 - Modeling Robot Swarms Using Integrals of Birth-Death Processes
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 - Formulas for Hitting Times and Cover Times for Random Walks on Groups
by Christopher Zhang
May 9, 2017
arXiv:2302.01963 - Integrals of Markov Processes with Application to Swarm Robotics Modelling
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
- Hodge Laplacian of Graphs
by Lek-Heng Lim
arxiv:1507.05379 - Comparing Graph Spectra of Adjacency and Laplacian Matrices
by J. F. Lutzeyer, [26]A. T. Walden
https://arxiv.org/abs/1712.03769 - Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
by [30]Dongdong She, [31]Abhishek Shah, [32]Suman Jana
https://arxiv.org/abs/2203.12064 - Learning policies for partially observable environments: Scaling up
by M Littman, A R Cassandra, L P Kaebling
in ICML 1995
https://people.cs.umass.edu/~barto/courses/cs687/Cassandra-etal-POMDP.pdf - A Krylov-Schur Algorithm for Large Eigenproblems
by G W Stewart
in SIMAX 2001
https://epubs.siam.org/doi/pdf/10.1137/S0895479800371529 - Integrated perception and planning in the continuous space: A POMDP approach
by Haoyu Bai, David Hsu, Wee Sun Lee
in IJRR 2014
https://www.roboticsproceedings.org/rss09/p18.pdf - Linear Algebra Primitves Beyond Laplacian Solvers
by Aaron Sidford (slides)
in FOCS 2018 Workshop
(no link) - Coverage-based Greybox Fuzzing as Markov Chain
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
- Normalized cuts and image segmentation
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 - A Survey of Eigenvector Methods for Web Information Retrieval
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; - Laplacians of graphs and Cheeger inequalities
by Fan Chung
note: theory isoparimetric number. mentions expander graphs
https://www.math.ucsd.edu/~fan/wp/cheeger.pdf - Network Properties Revealed through Matrix Functions.
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 - Maintaining the duality of closeness and betweenness centrality
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 - Graph Sparsification by Effective Resistances
by Daniel A. Spielman, Nikhil Srivastava
https://arxiv.org/abs/0803.0929 - A nearly-mlogn time solver for SDD linear systems
by Ioannis Koutis & Gary Miller & Richard Peng
in FOCS11
https://arxiv.org/abs/1102.4842 - On the Limiting Behavior of Parameter-Dependent Network Centrality Measures
by Michele Benzi & Christine Klymko
in SIMAX 36 2 pp. 686-706, 2015
https://epubs.siam.org/doi/abs/10.1137/130950550 - Co-clustering documents and words using bipartite spectral graph partitioning.
by Inderjit S. Dhillon.
in KDD 2001
http://portal.acm.org/citation.cfm?id=502512.502550
E. Graphs and Machine Learning
- Spectral networks and deep locally connected networks on graphs
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 - Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
by Michaël Defferrard and Xavier Bresson and Pierre Vandergheynst
https://arxiv.org/abs/1606.09375 - Deep Convolutional Networks on Graph-Structured Data
by Mikael Henaff & Joan Bruna & Yann LeCun, 2015
https://arxiv.org/abs/1506.05163 - Semi-Supervised Classification with Graph Convolutional Networks
by Thomas N. Kipf, Max Welling
in ICLR 2017
https://arxiv.org/abs/1609.02907
Machine Learning
F. ML: Deep Neural Networks
- Gradient-Based Learning Applied to Document Recognition
by Yan LeCun, Leon Bottou Yoshua Bengio, Patrick Haffner
in Proc. IEEE, November 1998.
https://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf - tutorial on NN+ back-prop:
https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
G. ML: Convolutional Neural Nets
- tutorial on CNNs
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ - 09/29 (postponed to 10/01) Aadit Munjal _______________________
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 - Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
by Chaoyue Liu & Libin Zhu & Mikhail Belkin
https://arxiv.org/abs/2003.00307 - 09/29 (postponed to 10/01) Aadit Munjal _______________________
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
- node2vec: Scalable Feature Learning for Networks
by Aditya Grover & Jure Leskovec
in KDD, 2016
https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf - 09/29 (postponed from 09/24): Brian Chung ________
- Dataset Distillation
by Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros
https://arxiv.org/abs/1811.10959 - Steering CLIP's vision transformer with sparse autoencoders
by Sonia Joseph, Praneet Suresh, Ethan Goldfarb, Lorenz Hufe, Yossi Gandelsman, Robert Graham, Danilo
https://arxiv.org/abs/2504.08729 - Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
by Ryan Kortvelesy, Steven Morad, Amanda Prorok
in AAMAS 2023, May 29--June 2, 2023, London, United Kingdom
https://arxiv.org/abs/2302.12826 - 09/17 Adam Imdieke_______________________
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
- Grokking modular arithmetic
by Andrey Gromov
https://arxiv.org/abs/2301.02679 - Discovering faster matrix multiplication algorithms with reinforcement learning
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
- 09/17 Adam Imdieke_______________________
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 - Transformers Learn In-Context by Gradient Descent
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
- TAG: Gradient Attack on Transformer-based Language Models
by Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
https://arxiv.org/abs/2103.06819 - Reconstructing Training Data from Trained Neural Networks
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 - Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning
by Erwin Quiring, David Klein, Daniel Arp, Martin Johns, and Konrad Rieck.
https://www.usenix.org/conference/usenixsecurity20/presentation/quiring
L. ML: Linear Algebra
- LOra: Low-Rank Adaptation Of Large Language Models
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
- Two-Player Games for Efficient Non-Convex Constrained Optimization
by Andrew Cotter , Heinrich Jiang , and Karthik Sridharan
arXiv:1804.06500 - Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
by Chaoyue Liu , Libin Zhu , and Mikhail Belkin
arXiv:2003.00307 - On the Implicit Bias in Deep-Learning Algorithms
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/ - Train faster, generalize better: Stability of stochastic gradient descent
by Moritz Hardt, Benjamin Recht, Yoram Singer.
in JMLR 2016
https://proceedings.mlr.press/v48/hardt16.pdf - Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
by Taoan Huang , Aaron Ferber , Yuandong Tian , Bistra Dilkina , Benoit Steiner
https://proceedings.mlr.press/v202/huang23g/huang23g.pdf - How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective
by Lei Wu, Chao Ma, Weinan E
in NeurIPS 2025
https://proceedings.neurips.cc/paper_files/paper/2018/file/6651526b6fb8f29a00507de6a49ce30f-Paper.pdf - Neural Tangent Kernel: Convergence and Generalization in Neural Networks
by Arthur Jacot, Franck Gabriel, Clément Hongler
in NeurIPS 2025
https://proceedings.neurips.cc/paper_files/paper/2018/file/5a4be1fa34e62bb8a6ec6b91d2462f5a-Paper.pdf - On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
by Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
in NeurIPS 2020
https://proceedings.neurips.cc/paper_files/paper/2018/file/6651526b6fb8f29a00507de6a49ce30f-Paper.pdf - Norm-based Generalization Bounds for Sparse Neural Networks
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 - A semantic loss function for deep learning with symbolic knowledge.
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 - Stability and Generalization for Minibatch SGD and Local SGD
by Yunwen Lei, Tao Sun, Mingrui Liu
https://arxiv.org/abs/2310.01139 - Assessing Generalization of SGD via Disagreement
by Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter
https://arxiv.org/abs/2106.13799 - Shift-Curvature, SGD, and Generalization
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
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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 - Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
by Levi D. McClenny* Ulisses Braga-Neto
arXiv:2009.04544 - Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
by A. Dener , M.A. Miller , R.M. Churchill , T. Munson , C.S. Chang
arXiv:2009.07330 - Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets.
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
- Sparse Autoencoders Find Highly Interpretable Features in Language Models
by Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey
arXiv:2309.08600 - An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability
Adam Karvonen
BLOG
https://adamkarvonen.github.io/machine_learning/2024/06/11/sae-intuitions.html - Mechanistic Interpretability for AI Safety -- A Review
by Leonard Bereska, Efstratios Gavves
arXiv:2404.14082 - Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt
arXiv:2301.05217 - Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective
by Hanqi Yan, Yanzheng Xiang, Guangyi Chen, Yifei Wang, Lin Gui, Yulan He
arXiv:2406.17969 - Towards Automated Circuit Discovery for Mechanistic Interpretability
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 - An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation
by Ahmed Abdulaal, Hugo Fry, Nina Montaña-Brown, Ayodeji Ijishakin, Jack Gao, Stephanie Hyland, Daniel C. Alexander, Daniel C. Castro
arXiv:2410.03334 - A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
by Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, Ziyu Yao
arXiv:2407.02646 - Open Problems in Mechanistic Interpretability
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 - Sparse Autoencoders
CS294A Lecture notes
by Andrew Ng
https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf - The Geometry of Concepts: Sparse Autoencoder Feature Structure
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/ - Scaling and evaluating sparse autoencoders
by Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu
arXiv:2406.04093 - Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
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 - Who Said Neural Networks Aren't Linear?
by Nimrod Berman, Assaf Hallak, Assaf Shocher
https://arxiv.org/abs/2510.08570
P. ML: Reinforcement Learning
- Soft Actor-Critic Algorithms and Applications
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
- Understanding Diffusion Models: A Unified Perspective
by Calvin Luo
video
https://arxiv.org/abs/2208.11970 - Denoising Diffusion Probabilistic Models
by Jonathan Ho, Ajay Jain, Pieter Abbeel
https://arxiv.org/abs/2006.11239 - Denoising Diffusion Implicit Models
by Jiaming Song, Chenlin Meng, Stefano Ermon
https://arxiv.org/abs/2010.02502
R. ML: Recent Papers
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
by DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong
https://arxiv.org/abs/2405.04434- The Singular Value Decompositions of Transformer Weight Matrices are Highly Interpretable
by beren, Sid Black (2022)
https://www.lesswrong.com/posts/mkbGjzxD8d8XqKHzA/the-singular-value-decompositions-of-transformer-weight - The Singular Value Decompositions of Transformer Weight Matrices are Highly Interpretable
- A Mathematical Framework for Transformer Circuits
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 - Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI
by Marlène Careil, Yohann Benchetrit, Jean-Rémi King
https://arxiv.org/abs/2505.14556 - A Short Introduction to Kernelized Stein Discrepancy
by Qiang Liu
https://www.cs.utexas.edu/~lqiang/PDF/ksd_short.pdf - Latent Concept Disentanglement in Transformer-based Language Models
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
- An overview of gradient descent optimization algorithms
by Sebastian Ruder
2017
https://arxiv.org/abs/1609.04747 - An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
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 - Gradient Descent Optimization: A Simple Guide for Beginners
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/ - Stochastic Convex Optimization
by Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
https://www.cs.cornell.edu/~sridharan/convex.pdf - Stochastic Convex Optimization
by Shai Shalev-Shwartz, Nathan Srebro, Karthik Sridharan
https://home.ttic.edu/~nati/Publications/nonlinearTR.pdf
T. Matrix Sketching
- Simple and deterministic matrix sketching
by Edo Liberty
in KDD '13: https://www.cs.yale.edu/homes/el327/papers/simpleMatrixSketching.pdf - Practical Sketching Algorithms for Low-Rank Matrix Approximation
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 - Matrix sketching for supervised classification with imbalanced classes
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