Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
Rank aggregation via nuclear norm minimizationDavid Gleich
The process of rank aggregation is intimately intertwined with
the structure of skew-symmetric matrices.
We apply recent advances in the theory and algorithms of matrix completion
to skew-symmetric matrices. This combination of ideas
produces a new method for ranking a set of items. The essence
of our idea is that a rank aggregation describes a partially
filled skew-symmetric matrix. We extend an algorithm for
matrix completion to handle skew-symmetric data and use that
to extract ranks for each item.
Our algorithm applies to both pairwise comparison and rating data.
Because it is based on matrix completion, it is robust to
both noise and incomplete data. We show a formal
recovery result for the noiseless case and
present a detailed study of the algorithm on synthetic
data and Netflix ratings.
Fast matrix primitives for ranking, link-prediction and moreDavid Gleich
I gave this talk at Netflix about some of the recent work I've been doing on fast matrix primitives for link prediction and also some non-standard uses of the nuclear norm for ranking.
Correlation clustering and community detection in graphs and networksDavid Gleich
We show a new relationship between various community detection objectives and a correlation clustering framework. These enable us to detect communities with good bounds on the solution.
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
Rank aggregation via nuclear norm minimizationDavid Gleich
The process of rank aggregation is intimately intertwined with
the structure of skew-symmetric matrices.
We apply recent advances in the theory and algorithms of matrix completion
to skew-symmetric matrices. This combination of ideas
produces a new method for ranking a set of items. The essence
of our idea is that a rank aggregation describes a partially
filled skew-symmetric matrix. We extend an algorithm for
matrix completion to handle skew-symmetric data and use that
to extract ranks for each item.
Our algorithm applies to both pairwise comparison and rating data.
Because it is based on matrix completion, it is robust to
both noise and incomplete data. We show a formal
recovery result for the noiseless case and
present a detailed study of the algorithm on synthetic
data and Netflix ratings.
Fast matrix primitives for ranking, link-prediction and moreDavid Gleich
I gave this talk at Netflix about some of the recent work I've been doing on fast matrix primitives for link prediction and also some non-standard uses of the nuclear norm for ranking.
Correlation clustering and community detection in graphs and networksDavid Gleich
We show a new relationship between various community detection objectives and a correlation clustering framework. These enable us to detect communities with good bounds on the solution.
Spectral clustering with motifs and higher-order structuresDavid Gleich
I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject.
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
Spacey random walks and higher-order data analysisDavid Gleich
My talk at TMA 2016 (The workshop on Tensors, Matrices, and their Applications) on the relationship between a spacey random walk process and tensor eigenvectors
A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.
Using Local Spectral Methods to Robustify Graph-Based LearningDavid Gleich
This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on.
Spacey random walks and higher order Markov chainsDavid Gleich
My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.
Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.
PageRank Centrality of dynamic graph structuresDavid Gleich
A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.
Big data matrix factorizations and Overlapping community detection in graphsDavid Gleich
In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on.
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
Anti-differentiating Approximation Algorithms: PageRank and MinCutDavid Gleich
We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.
Fast relaxation methods for the matrix exponential David Gleich
The matrix exponential is a matrix computing primitive used in link prediction and community detection. We describe a fast method to compute it using relaxation on a large linear system of equations. This enables us to compute a column of the matrix exponential is sublinear time, or under a second on a standard desktop computer.
Gaps between the theory and practice of large-scale matrix-based network comp...David Gleich
I discuss some runtimes for the personalized PageRank vector and how it relates to open questions in how we should tackle these network based measures via matrix computations.
MapReduce Tall-and-skinny QR and applicationsDavid Gleich
A talk at the SIMONS workshop on Parallel and Distributed Algorithms for Inference and Optimization on how to do tall-and-skinny QR factorizations on MapReduce using a communication avoiding algorithm.
Recommendation and graph algorithms in Hadoop and SQLDavid Gleich
A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.
Relaxation methods for the matrix exponential on large networksDavid Gleich
My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems.
This talk is a new update based on some of our recent results on doing Tall and Skinny QRs in MapReduce. In particular, the "fast" iterative refinement approximation based on a sample is new.
Vertex neighborhoods, low conductance cuts, and good seeds for local communit...David Gleich
My talk from KDD2012 about vertex neighborhoods and low conductance cuts. See the paper here: http://arxiv.org/abs/1112.0031 and http://dl.acm.org/citation.cfm?id=2339628
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Spectral clustering with motifs and higher-order structuresDavid Gleich
I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject.
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
Spacey random walks and higher-order data analysisDavid Gleich
My talk at TMA 2016 (The workshop on Tensors, Matrices, and their Applications) on the relationship between a spacey random walk process and tensor eigenvectors
A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.
Using Local Spectral Methods to Robustify Graph-Based LearningDavid Gleich
This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on.
Spacey random walks and higher order Markov chainsDavid Gleich
My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.
Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.
PageRank Centrality of dynamic graph structuresDavid Gleich
A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.
Big data matrix factorizations and Overlapping community detection in graphsDavid Gleich
In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on.
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
Anti-differentiating Approximation Algorithms: PageRank and MinCutDavid Gleich
We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.
Fast relaxation methods for the matrix exponential David Gleich
The matrix exponential is a matrix computing primitive used in link prediction and community detection. We describe a fast method to compute it using relaxation on a large linear system of equations. This enables us to compute a column of the matrix exponential is sublinear time, or under a second on a standard desktop computer.
Gaps between the theory and practice of large-scale matrix-based network comp...David Gleich
I discuss some runtimes for the personalized PageRank vector and how it relates to open questions in how we should tackle these network based measures via matrix computations.
MapReduce Tall-and-skinny QR and applicationsDavid Gleich
A talk at the SIMONS workshop on Parallel and Distributed Algorithms for Inference and Optimization on how to do tall-and-skinny QR factorizations on MapReduce using a communication avoiding algorithm.
Recommendation and graph algorithms in Hadoop and SQLDavid Gleich
A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.
Relaxation methods for the matrix exponential on large networksDavid Gleich
My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems.
This talk is a new update based on some of our recent results on doing Tall and Skinny QRs in MapReduce. In particular, the "fast" iterative refinement approximation based on a sample is new.
Vertex neighborhoods, low conductance cuts, and good seeds for local communit...David Gleich
My talk from KDD2012 about vertex neighborhoods and low conductance cuts. See the paper here: http://arxiv.org/abs/1112.0031 and http://dl.acm.org/citation.cfm?id=2339628
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Rank aggregation via skew-symmetric matrix completion
1. Rank aggregation via nuclear norm minimization
David F. Gleich
Sandia National Laboratories
Lek-Heng Lim
University of Chicago
Householder Symposium 18
Tahoe City, (Barely) CA
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin
Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.
David F. Gleich (Sandia) Householder 18 1/15
2. Which is a better list of good DVDs?
Lord of the Rings 3: The Return of … Lord of the Rings 3: The Return of …
Lord of the Rings 1: The Fellowship Lord of the Rings 1: The Fellowship
Lord of the Rings 2: The Two Towers Lord of the Rings 2: The Two Towers
Lost: Season 1 Star Wars V: Empire Strikes Back
Battlestar Galactica: Season 1 Raiders of the Lost Ark
Fullmetal Alchemist Star Wars IV: A New Hope
Trailer Park Boys: Season 4 Shawshank Redemption
Trailer Park Boys: Season 3 Star Wars VI: Return of the Jedi
Tenchi Muyo! Lord of the Rings 3: Bonus DVD
Shawshank Redemption The Godfather
Standard
Nuclear Norm
rank aggregation
based rank aggregation
(the mean rating)
David F. Gleich (Sandia) Householder 18 2/15
3. Ranking is really hard
John Kemeny Dwork, Kumar, Naor,
Ken Arrow Sivikumar
All rank aggregations
involve some measure A good ranking is the
of compromise “average” ranking under NP hard to compute
a permutation distance Kemeny’s ranking
David F. Gleich (Sandia) Householder 18 3/15
4. Embody chair
John Cantrell (flickr)
Given a hard problem,
what do you do?
Numerically relax!
It’ll probably be easier.
David F. Gleich (Sandia) Householder 18 4 of 15
5. Suppose we had scores
Let be the score of the ith movie/song/paper/team to rank
Suppose we can compare the ith to jth:
Then is skew-symmetric, rank 2.
Also works for with an extra log.
Numerical ranking is intimately intertwined
with skew-symmetric matrices
Kemeny and Snell, Mathematical Models in Social Sciences (1978)
David F. Gleich (Sandia) Householder 18 5/15
6. Using ratings instead of comparisons
Ratings induce various skew-symmetric matrices.
Arithmetic Mean
Log-odds
David 1988 – The Method of Paired Comparisons
David F. Gleich (Sandia) Householder 18 6/15
7. Extracting the scores
Given with all entries, then 107
is the Borda
Movie Pairs
105
count, the least-squares
solution to
How many do we have? 101
Most.
101 105
Number of Comparisons
Do we trust all ?
Not really. Netflix data 17k movies,
500k users, 100M ratings–
99.17% filled
David F. Gleich (Sandia) Householder 18 7/15
8. Only partial info? Complete it!
Let be known for We trust these scores.
Goal Find the simplest skew-symmetric matrix that matches
the data
NP hard
Heuristic
Convex
best convex underestimator of rank on unit ball.
David F. Gleich (Sandia) Householder 18 8/15
9. Solving the nuclear norm problem
Use a LASSO formulation 1.
2. REPEAT
3. = rank-k SVD of
4.
5.
6. UNTIL
Jain et al. propose SVP for
this problem without
Jain et al. NIPS 2010
David F. Gleich (Sandia) Householder 18 9/15
10. Skew-symmetric SVDs
Let be an skew-symmetric matrix with
eigenvalues ,
where and . Then the SVD of is
given by
for and given in the proof.
Proof Use the Murnaghan-Wintner form and the SVD of a
2x2 skew-symmetric block
This means that SVP will give us the skew-
symmetric constraint “for free”
David F. Gleich (Sandia) Householder 18 10/15
11. Exact recovery results
David Gross showed how to recover Hermitian matrices.
i.e. the conditions under which we get the exact
Note that is Hermitian. Thus our new result!
Gross arXiv 2010.
David F. Gleich (Sandia) Householder 18 11/15
12. Recovery Discussion and Experiments
If , then just look at differences from a
connected set. Constants? Not very good.
Intuition for the truth.
David F. Gleich (Sandia) Householder 18 12/15
13. The Ranking Algorithm
0. INPUT (ratings data) and c
(for trust on comparisons)
1. Compute from
2. Discard entries with fewer than
c comparisons
3. Set to be indices and
values of what’s left
4. = SVP( )
5. OUTPUT
David F. Gleich (Sandia) Householder 18 13/15
14. Synthetic Results
Construct an Item Response Theory model. Vary number of
ratings per user and a noise/error level
Our Algorithm! The Average Rating
David F. Gleich (Sandia) Householder 18 14/15
15. Conclusions and Future Work
1. Compare against others
Rank aggregation with van Dooren (fitting)
the nuclear norm is: Massey (direct least
principled squares for )
easy to compute
The results are much better 2. Noisy recovery! More
than simple approaches. realistic sampling.
3. Skew-symmetric Lanczos
based SVD?
David F. Gleich (Sandia) Householder 18 15/15
