Michael Mahoney - Presentations

Talks and Presentations

Recent tutorial presentations:

• Recent and Upcoming Developments in Randomized Numerical Linear Algebra for ML (December 2023, Tutorial at 2023 NeurIPS) (pdf)

• Practical neural network theory: from statistical mechanics basics to heavy-tailed self regularization to working with state of the art models (Apr 2023) (pdf)

Recent seminar presentations:

• Foundational Methods for Foundation Models for Scientific Machine Learning (April 2024) (pdf)

• Model Selection And Ensembling When There Are More Parameters Than Data (November 2023) (pdf)

• Foundations for scientific machine learning (May 2023) (pdf)

• Multiplicative noise and heavy tails in stochastic optimization and machine learning (May 2023) (pdf)

• Algorithmic Methods, Backdoors, and Model Robustness (Apr 2023) (pdf)

• Putting Randomness into LAPACK and Next Generation RandNLA Theory (Mar 2023) (pdf)

• Scientific machine learning: methods to bridge scientific spatial and temporal modeling with machine learning (Jun 2022) (pdf)

• Practice, Theory, and Theorems for Random Matrix Theory in Modern Machine Learning (Jun 2022) (pdf)

• Building foundations for scientific machine learning at scale (Mar 2022) (pdf)

• Continuous Network Models for Sequential Predictions (Jan 2022) (pdf)

• Toward combining principled scientific models and principled machine learning models (Nov 2021) (pdf)

• Column Subset Selection: TCS and NLA ("at" the CMC Seminar Series, Nov 2021) (pdf)

• Putting Randomized Matrix Algorithms in LAPACK, and Connections with Second-order Stochastic Optimization ("at" the NeurIPS 2021 Optimization Workshop) (pdf)

• Least Squares in RandNLA ("at" the CMC Seminar Series, Sept 2021) (pdf)

• Incorporating second order ideas into first class machine learning methods ("at" the MOPTA Meeting, Aug 2021) (pdf)

• Overcoming Inversion Bias in Distributed Newton's Method (May 2021) (pdf)

• Practical Theory and Neural Network Models ("at" TOPML Workshop and elsewhere, April 2021) (pdf)

• ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning ("at" MIT and elsewhere, September 2020) (pdf)

• Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models (September 2020) (pdf)

• Continuous-in-Depth Neural Networks (August 2020) (pdf)

• ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning (older, August 2020) (pdf)

• Determinantal Point Processes and Randomized Numerical Linear Algebra (April 2020) (pdf)

Older tutorial presentations:

• Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks (at ACM-SIGKDD 2019) (pdf)

• Sampling for Linear Algebra, Statistics, and Optimization (Aug 2018, at Simons' Institute 2018 Big Data Bootcamp) (pdf)

• RandNLA: Randomization in Numerical Linear Algebra (5.5 hr version at UW Madison Summer School, July 2018) (pdf)

• Randomization in Numerical Linear Algebra: Theory and Practice (2.0 hr version at SIAM ALA Meeting, October 2015) (pdf)

• Past, Present and Future of Randomized Numerical Linear Algebra: (3.0 hr version at Simons' Institute 2013 Big Data Bootcamp) (Part I: pdf, ppt and Part II: pdf, ppt)

• Theory (and some practice) of Randomized Algorithms for Matrices and Data (tutorial from FOCS 2012 Workshop) (pdf, ppt)

• Geometric Tools for Identifying Structure in Large Social and Information Networks (1.5 hr version at SAMSI Opening Workshop 2010, etc.) (pdf, ppt)

• Geometric Tools for Identifying Structure in Large Social and Information Networks (2 hr version at ICASSP 2011, etc.) (pdf, ppt)

• Geometric Tools for Identifying Structure in Large Social and Information Networks (3 hr version at ICML 2010 and KDD 2010, etc.) (pdf, ppt) (The pdf file in four pieces: here, here, here, and here.)

• Randomized Algorithms for Matrices and Massive Data Sets (at SIAM-SDM06 2006 and VLDB 2006) (ppt)

• Randomized Algorithms for Matrices and Massive Data Sets (at ACM-SIGKDD 2005) (ppt)

Older seminar presentations:

• Using dynamical systems ideas to combine in a principled way data-driven models and domain-driven models (older, April 2020) (pdf)

• Exact expressions for double descent and implicit regularization via surrogate random design (December 2019) (pdf)

• Making the Deep Learning Revolution Practical Through Second Order Methods (at MIT, October 2019) (pdf (big=40MB))

• Newton-MR: Newton's Method Without Smoothness or Convexity (October 2019) (pdf)

• Minimax and Bayesian experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression (at TTIC, Sept 2019) (pdf)

• Why Deep Learning Works: Traditional and Heavy-Tailed Implicit Self-Regularization in Deep Neural Networks (at the ICML 2019 Workshop on Theoretical Physics for Deep Learning, June 2019) (pdf)

• Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks (at the SF Bay Area DM-SIG Meeting, February 2019) (pdf)

• Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks (Sept 2018, at Simons' Institute 2018 Big Data RandNLA meeting) (pdf)

• Large Scale Training of Neural Networks (Sept/Nov 2018) (pdf)

• Alchemist: An Apache Spark <=> MPI Interface (June 2018) (pdf)

• Biomedicine & the Foundations of Data? (May 2018) (pdf)

• Scientific Machine Learning with Alchemist (An Apache Spark <=> MPI Interface) and Beyond (Apr 2018) (pdf)

• Numerically-intensive Machine Learning at Scale (Fall 2017) (pdf)

• Second-order Machine Learning (Fall 2017) (pdf)

• UC Berkeley's FODA Institute: Foundations of Data Analysis (NSF TRIPODS Kickoff, Oct 2017) (pdf)

• Second-order Machine Learning (Pre-fall 2017) (pdf)

• Local graph analytics: beyond characterizing community structure (Spring 2017) (pdf)

• Terabyte-scale Computational Statistics (talk Fall 2016) (pdf)

• Scientific Matrix Factorizations in Spark at Scale: Cross-platform performance, scaling, and comparisons with C+MPI (talk at 2016 Dato Data Science Summit and elsewhere) (pdf)

• Optimization Algorithms for Analyzing Large Datasets (talk at 2016 PCMI Summer School) (pdf)

• Foundations of Data Science (talk at NSF pre-TRIPODS workshop/meeting, Apr 2016) (pdf)

• Sub-Sampled Newton Methods (talk at ITA 2016 and elsewhere) (pdf)

• Challenges in Multiresolution Methods for Graph-based Learning (talk, 3of3, from NIPS15 Workshops) (pdf)

• Using Local Spectral Methods in Theory and in Practice (talk, 2of3, from NIPS15 Workshops) (pdf)

• Column Subset Selection on Terabyte-sized Scientific Data (talk, 1of3, from NIPS15 Workshops) (pdf)

• Linear and Sublinear Linear Algebra Algorithms: Preconditioning Stochastic Gradient Algorithms with Randomized Linear Algebra (DIMACS, Aug 2015) (pdf)

• Overview of RandNLA: Randomized Numerical Linear Algebra (pdf)

• Tree-like structure in social graphs (pdf (big=28MB), ppt (big=48MB))

• Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and network data (pdf, ppt)

• Locally-biased and semi-supervised eigenvectors (talk from MMDS 2014) (pdf, ppt)

• Implicit regularization in sublinear approximation algorithms (pdf, ppt)

• BIG Biomedicine and the Foundations of BIG Data Analysis (at Big Data in Biomedicine at Stanford's Medical School, 5/23/14) (pdf, ppt)

• Revisiting the Nystrom Method for Improved Large-Scale Machine Learning (pdf, ppt)

• Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments (version from Simons Big Data Workshop II) (pdf)

• Input-sparsity Time Algorithms for Embeddings and Regression Problems (talk from Simons Big Data Workshop I) (pdf)

• Randomized Regression in Parallel and Distributed Environments (talk from GraphLab 2013) (pdf)

• Extracting insight from large networks: implications of small-scale and large-scale structure (pdf, ppt)

• Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments (version from MMDS 2012) (pdf)

• Sensors, networks, and massive data (pdf, ppt)

• Randomized Algorithms for Matrices and Data (pdf, ppt)

• Approximate computation and implicit regularization in large-scale data analysis (PODS vsn) (pdf, ppt)

• Approximate computation and implicit regularization in large-scale data analysis (Stats vrsn1) (pdf, ppt)

• Approximate computation and implicit regularization in large-scale data analysis (Short vrsn) (pdf, ppt)

• Looking for clusters in your data ... in theory and in practice (pdf, ppt)

• Fast Approximation of Matrix Coherence and Statistical Leverage (pdf, ppt)

• Implementing regularization implicitly via approximate eigenvector computation (pdf, ppt)

• Linear Algebra and Machine Learning of Large Informatics Graphs (pdf, ppt)

• Geometric Network Analysis Tools (talk from MMDS 2010) (pdf, ppt)

• Algorithmic and Statistical Perspectives on Large-Scale Data Analysis (pdf, ppt)

• Community structure in large social and information networks (newer) (pdf, ppt)

• Statistical leverage and improved matrix algorithms (newer and long) (pdf, ppt)

• Approximation Algorithms as Experimental Probes of Informatics Graphs (pdf, ppt)

• Community structure in large social and information networks (talk from MMDS 2008) (pdf, ppt)

• Community structure in large social and information networks (older) (pdf, ppt)

• Statistical leverage and improved matrix algorithms (older and short) (pdf, ppt)

• Sampling algorithms and core-sets for Lp regression and applications (pdf, ppt)

• CUR Matrix Decompositions for Improved Data Analysis (talk from MMDS 2006) (pdf, ppt)

• A Relative-Error CUR Decomposition for Matrices and Its Data Applications (pdf, ppt)

• Sampling Algorithms for L2 Regression and Applications (talk from SODA 2006) (pdf, ppt)

• Approximating a Gram Matrix for Improved Kernel-Based Learning (talk from COLT 2005) (ps, pdf)

• Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis (newer) (pdf, ppt)

• Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis (older) (pdf)

• CUR Matrix Decomposition with Applications to Algorithm Design and Massive Data Set Analysis (pdf)

• Fast Monte Carlo Algorithms for Massive Data Sets and Approximating Max-Cut (ps, pdf)

The TIP5P Water talk:

• The Computational Statistical Mechanics of Simple Models of Liquid Water (pdf)

Videos of Talks and Presentations

• "Recent and Upcoming Developments in Randomized Numerical Linear Algebra for ML"

  (Tutorial at 2023 NeurIPS, December 2023)

• "Practice, Theory, and Theorems for Random Matrix Theory in Modern Machine Learning"

  ("at" RMT+NLA-UW, June 2022).

• "Continuous Network Models for Sequential Predictions"

  ("at" JHU MIDS, January 2021).

• "Toward combining principled scientific models and principled machine learning models"

  ("at" LLNL, November 2021).

• "Practical Theory and Neural Network Models"

  ("at" IARAI, October 2021).

• "Practical Theory and Neural Network Models"

  ("at" TOPML, April 2021).

• "ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning"

  ("at" MIT (and elsewhere), September 2020).

• "Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models"

  ("at" NJIT (and elsewhere), September 2020).

• "Continuous-in-Depth Neural Networks"

  ("at" Fields Institute, August 2020).

• "Determinantal Point Processes and Numerical Randomized Linear Algebra"

  ("at" Tel Aviv, April 2020).

• "Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks"

  (at ACM-SIGKDD, August 2019).

• "Why Deep Learning Works: Traditional and Heavy-Tailed Implicit Self-Regularization in Deep Neural Networks"

  (at the ICML 2019 Workshop on Theoretical Physics for Deep Learning, June 2019).

• "Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning"

  (June 2019, at UW's Physics Informed ML Meeting; and also at the Oct 2019 IPAM Interpretable Learning in Physical Sciences Workshop).

• "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"

  (at the SF Bay Area DM-SIG Meeting, February 2019).

• "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"

  (Sept 2018, at Simons' Institute 2018 Big Data RandNLA meeting; and also at the Sept 2019 DIMACS RandNLA Workshop).

• "Sampling for Linear Algebra, Statistics, and Optimization I" and "Sampling for Linear Algebra, Statistics, and Optimization II"

  (Aug 2018, at Simons' Institute 2018 Big Data Bootcamp).

• "Biomedicine & the Foundations of Data?"

  (at the BD2K Guide to the Fundamentals of Data Science seminar series).

• "Matrix factorizations at scale: a comparison of scientific data analytics on Spark and MPI"

  (at the 2016 Dato Data Science Summit).

• "Challenges in Multiresolution Methods for Graph-based Learning"

  (at the NIPS Workshops, December 2015).

• "Using Local Spectral Methods in Theory and in Practice"

  (at the NIPS Workshops, December 2015).

• "Linear and Sublinear Linear Algebra Algorithms: Preconditioning Stochastic Gradient Algorithms with Randomized Linear Algebra"

  (at DIMACS, August 2015).

• "Eigenvector localization and implicit regularization for large-scale graphs and networked data"

  (at CIMAT, March 2015).

• "Locally-biased and semi-supervised eigenvectors"

  (at MMDS 2014, June 2014).

• "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"

  (at the SF Bay Area DM-SIG Meeting, June 2, 2014).

• "BIG Biomedicine and the Foundations of BIG Data Analysis"

  (at the Big Data in BioMedicine Conference at Stanford University, May 2014).

• "Randomized Matrix Algorithms and Large-scale Scientific Data Analysis"

  (at the 2014 ASE Conference, May 31, 2014).

• "Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and networked data" (or try also here for another version)

  (at ICERM and IMA, May and April 2014, respectively).

• "Randomized matrix algorithms and large-scale scientific data analysis"

  (at the University of Michigan, April 2014).

• "Recent Results in Randomized Numerical Linear Algebra" (or click here)

  (at the NIPS 2013 Workshops, December 2013).

• "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"

  (at the Simons Big Data Workshop II, October 2013).

• "Input-sparsity Time Algorithms for Embeddings and Regression Problems"

  (at the Simons Big Data Workshop I, September 2013).

• "Past, Present and Future of Randomized Numerical Linear Algebra: Part I (PD) and Part II" (MM)

  (at the Simons Big Data Bootcamp, September 2013).

• "Randomized Regression in Parallel and Distributed Environments"

  (at the GraphLab Workshop, July 2013).

• "Sensors, networks, and massive data" or click here

  (at Kavli FoS, November 2012).

• "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"

  (at MMDS 2012, July 2012).

• "Extracting insight from large networks: implications of small-scale and large-scale structure"

  (at the University of Marlyand, April 2012).

• "Fast Approximation of Matrix Coherence and Statistical Leverage"

  (at the NIPS Workshops, December 2011).

• "Approximate computation and implicit regularization in large-scale data analysis" or click here

  (at the Workshop on Beyond Worst-Case Analysis, September 2011).

• "Linear Algebra and Machine Learning of Large Informatics Graphs"

  (at the NIPS Workshops, December 2010).

• "Geometric Tools for Identifying Structure in Large Social and Information Networks"

  (90 minute version, tutorial at SAMSI Opening workshop, August 2010).

• "Geometric Tools for Graph Mining of Large Social and Information Networks"

  (3 hour version, tutorial at KDD 2010, July 2010).

• "Community Structure in Large Social and Information Networks"

  (at the Newton Institute in Cambridge, June 2010).

• "Algorithmic and Statistical Perspectives on Large-Scale Data Analysis"

  (at the SF Bay Area DM-SIG Meeting, February 2010).

• "Community Structure in Large Social and Information Networks," in avi or mpg (note: slow to download),

  (at the 2009 IIT Kanpur Processing Massive Data Sets Workshop, December 2009).

• "Statistical Leverage and Improved Matrix Algorithms"

  (at the 2009 ICML Workshops, June 2009).

• "Sampling Algorithms and Coresets for Lp Regression and Applications," in mpg (note: slow to download),

  (at the 2006 IIT Kanpur Data Streams Workshop, December 2006).

• "CUR Matrix Decompositions for Improved Data Analysis"

  (at Johns Hopkins University, March 2006).

• "Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis"

  (at the 2005 IPAM Summer School, July 2005).