I've been more than two months without updating the blog. A lot of work with my new company, trying to find funding for starting-up, and developing tons and tons of code lines. Now I'll try to update the blog once per week of once each two weeks, at least, but as my research interests have shifted a little bit to Social Media applications, I've decided to change the name of the blog from "Business Intelligence, Data Mining & Machine Learning" o "Social Media, Data Mining & Machine Learning". Probably the name of the blog is not so important, but working now on Social Media I feel more comfortable with that keywork in the title.
Showing posts with label machine learning. Show all posts
Showing posts with label machine learning. Show all posts
Social Media, Data Mining & Machine Learning
.
Wednesday, April 22, 2009
1 comments
CFP: 21st International Joint Conference on Artificial Intelligence (IJCAI-09)
.
Monday, December 08, 2008
1 comments
The IJCAI-09 Program Committee invites submissions of technical papers for IJCAI-09, to be held in Pasadena, CA, USA, July 11-17, 2009. Submissions are invited on significant, original, and previously unpublished research on all aspects of artificial intelligence.
The theme of IJCAI-09 is "The Interdisciplinary Reach of Artificial Intelligence," with a focus on the broad impact of artificial intelligence on science, engineering, medicine, social sciences, arts and humanities. The conference will include invited talks, workshops, tutorials, and other events dedicated to this theme.
Submission Details
Submitted papers must be formatted according to IJCAI guidelines and submitted electronically through the IJCAI-09 paper submission site. Full instructions for submission, including formatting guidelines and electronic templates for paper submission, are available on the IJCAI-09 website: http://www.ijcai-09.org (see the link titled Submission Details). Submitting authors will be required to register with the IJCAI-09 paper submission software (this will be linked from the IJCAI-09 website during the first week of December, 2008).
Papers may be accepted for either oral or poster presentation; papers accepted for either form of presentation will not be distinguished in the conference proceedings, nor will designation of oral or poster presentation be made on the quality of the contribution. Instead, these distinctions will be made in the interests of overall program coherence and quality.
To facilitate review, the paper title, author names, contact details, and a brief abstract must be submitted electronically by Jan. 7, 2009 (11:59 PST). No paper will be accepted for review unless an accompanying abstract is received by the deadline. Technical papers are due electronically on Jan. 12, 2009 (11:59 PST). Authors bear full responsibility for compliance with submission standards. Submissions received after the deadline or that do not meet the length or formatting requirements will not be accepted for review. No email or fax submissions will be accepted. Notification of receipt of the electronically submitted papers will be emailed to the designated contact author soon after receipt. If there are problems with the electronic submission, the program chair will contact the designated author by email. The last day for inquiries regarding lost submissions is Jan. 19, 2009. Notification of acceptance or rejection of submitted papers will be emailed to the designated author by March 31, 2009. The opportunity to respond to preliminary reviews will be made available to authors prior to this date, during the period March 13-16, 2009.
Guidelines for such responses, along with details of the reviewing process will be posted on the IJCAI-09 website. Camera-ready copy of accepted papers must be received by the publisher by April 14, 2009. Note: at least one author of each accepted paper is required to attend the conference to present the work. Authors will be required to confirm their acceptance of this requirement at the time of submission.
Authors who do not have access to the web should contact the program chair at pcchair09@ijcai.org no later than December 15, 2008 for alternate submission instructions.
Content Areas
To facilitate the reviewing process, authors will be required to choose two to four appropriate content area keywords from the list provided by the IJCAI-09 submission software, which will be part of the online paper registration process. Authors are encouraged to select the most specific keywords that accurately describe the main aspects of their contributions. General categories should only be used if specific categories do not apply or do not accurately reflect the main contributions. Each keyword is placed within one of ten 10 major themes; however, many of the keywords cut across multiple themes, and authors should feel free to select any keyword descriptive of the contribution, even if the major theme within which is it categorized is not the most appropriate. A list of keywords is appended to the end of this call.
The major themes are:
Agent-based and Multi-agent Systems
Constraints, Satisfiability, and Search
Knowledge Representation, Reasoning and Logic
Machine Learning
Multidisciplinary Topics And Applications
Natural Language Processing
Planning and Scheduling
Robotics and Vision
Uncertainty in AI
Web and Knowledge-based Information Systems
Policy on Multiple Submissions
IJCAI will not accept any paper which, at the time of submission, is under review for or has already been published or accepted for publication in a journal or another conference. Authors are also required not to submit their papers elsewhere during IJCAI's review period. These restrictions apply only to journals and conferences, not to workshops and similar specialized presentations with a limited audience and without archival proceedings. Authors will be required to confirm that their submissions conform to these requirements at the time
of submission.
Paper Length and Format
Submitted technical papers must be no longer than six pages, including all figures and references, and must be formatted according to posted IJCAI-09 guidelines. Specifically, papers must be formatted for "letter-size" (8.5" x 11") paper, in double-column format with a 10pt font. Electronic templates for the LaTeX typesetting package, as well as a Word template, that conform to IJCAI-09 guidelines will be made available at the conference website (see above) during the first week of December, as will further details on formatting.
Authors are required to submit their electronic papers in PDF format. Files in Postscript (ps), or any other format will not be accepted.
Submitted papers must not exceed six (6) formatted pages, including references and figures. This six-page limit will be strictly enforced: over-length papers will not be considered for review. Each accepted paper will be allowed six pages in the proceedings; up to two additional pages may be purchased at a price of $275 per page. In order to make blind reviewing possible, authors must omit their names and affiliations from the paper. Also, while the references should include all published literature relevant to the paper, including previous works of the authors, it should not include unpublished works. When referring to one's own work, use the third person rather than the first person. For example, say "Previously, Foo and Bar [7] have shown that...", rather than "In our previous work [7] we have shown that..." For accepted papers, such identifying information can be added to the final camera-ready version for publication.
Review Process
Papers will be subject to blind peer review. Selection criteria include accuracy and originality of ideas, clarity and significance of results and quality of the presentation. Each paper will be assigned to three Program Committee members, one Senior Program Committee member and one Area Chair for review. The reviewing process will include a short period for the authors to view reviews and respond to technical questions on the submitted work raised by the reviewers before final decisions are made. The decision of the Program Committee will be final and cannot be appealed.
Papers accepted for the conference will be scheduled for oral or poster presentation and will be printed in the proceedings. At least one author of each accepted paper will be required to attend the conference to present the work.
Please send inquiries about paper submissions to ijcai09@aaai.org.
Inquiries about the conference program can be directed to:
Craig Boutilier
Program Chair, IJCAI-09
Department of Computer Science
University of Toronto
Toronto, ON, M5S 3H5, CANADA
Email: pcchair09@ijcai.org
For further information please visit the conference web site: http://www.ijcai-09.org
List of keywords:
Agent-based and Multi-agent Systems
Constraints, Satisfiability, and Search
Knowledge Representation, Reasoning and Logic
Machine Learning
Multidisciplinary Topics And Applications
Natural-Language Processing
Planning and Scheduling
Robotics and Vision
Uncertainty in AI
Web and Knowledge-based Information Systems
The theme of IJCAI-09 is "The Interdisciplinary Reach of Artificial Intelligence," with a focus on the broad impact of artificial intelligence on science, engineering, medicine, social sciences, arts and humanities. The conference will include invited talks, workshops, tutorials, and other events dedicated to this theme.
- Important dates for authors of technical papers:
- Electronic abstract submission: January 7, 2009 (11:59PM, PST)
- Electronic paper submission: January 12, 2009 (11:59PM, PST)
- Author feedback period: March 13-16, 2009 (11:59PM, PDT). Please note: Daylight savings time starts on March 8.
- Author notification of acceptance/rejection: March 31, 2009
- Camera-ready copy due: April 14, 2009
- Technical sessions: July 13-17, 2009
Submission Details
Submitted papers must be formatted according to IJCAI guidelines and submitted electronically through the IJCAI-09 paper submission site. Full instructions for submission, including formatting guidelines and electronic templates for paper submission, are available on the IJCAI-09 website: http://www.ijcai-09.org (see the link titled Submission Details). Submitting authors will be required to register with the IJCAI-09 paper submission software (this will be linked from the IJCAI-09 website during the first week of December, 2008).
Papers may be accepted for either oral or poster presentation; papers accepted for either form of presentation will not be distinguished in the conference proceedings, nor will designation of oral or poster presentation be made on the quality of the contribution. Instead, these distinctions will be made in the interests of overall program coherence and quality.
To facilitate review, the paper title, author names, contact details, and a brief abstract must be submitted electronically by Jan. 7, 2009 (11:59 PST). No paper will be accepted for review unless an accompanying abstract is received by the deadline. Technical papers are due electronically on Jan. 12, 2009 (11:59 PST). Authors bear full responsibility for compliance with submission standards. Submissions received after the deadline or that do not meet the length or formatting requirements will not be accepted for review. No email or fax submissions will be accepted. Notification of receipt of the electronically submitted papers will be emailed to the designated contact author soon after receipt. If there are problems with the electronic submission, the program chair will contact the designated author by email. The last day for inquiries regarding lost submissions is Jan. 19, 2009. Notification of acceptance or rejection of submitted papers will be emailed to the designated author by March 31, 2009. The opportunity to respond to preliminary reviews will be made available to authors prior to this date, during the period March 13-16, 2009.
Guidelines for such responses, along with details of the reviewing process will be posted on the IJCAI-09 website. Camera-ready copy of accepted papers must be received by the publisher by April 14, 2009. Note: at least one author of each accepted paper is required to attend the conference to present the work. Authors will be required to confirm their acceptance of this requirement at the time of submission.
Authors who do not have access to the web should contact the program chair at pcchair09@ijcai.org no later than December 15, 2008 for alternate submission instructions.
Content Areas
To facilitate the reviewing process, authors will be required to choose two to four appropriate content area keywords from the list provided by the IJCAI-09 submission software, which will be part of the online paper registration process. Authors are encouraged to select the most specific keywords that accurately describe the main aspects of their contributions. General categories should only be used if specific categories do not apply or do not accurately reflect the main contributions. Each keyword is placed within one of ten 10 major themes; however, many of the keywords cut across multiple themes, and authors should feel free to select any keyword descriptive of the contribution, even if the major theme within which is it categorized is not the most appropriate. A list of keywords is appended to the end of this call.
The major themes are:
Agent-based and Multi-agent Systems
Constraints, Satisfiability, and Search
Knowledge Representation, Reasoning and Logic
Machine Learning
Multidisciplinary Topics And Applications
Natural Language Processing
Planning and Scheduling
Robotics and Vision
Uncertainty in AI
Web and Knowledge-based Information Systems
Policy on Multiple Submissions
IJCAI will not accept any paper which, at the time of submission, is under review for or has already been published or accepted for publication in a journal or another conference. Authors are also required not to submit their papers elsewhere during IJCAI's review period. These restrictions apply only to journals and conferences, not to workshops and similar specialized presentations with a limited audience and without archival proceedings. Authors will be required to confirm that their submissions conform to these requirements at the time
of submission.
Paper Length and Format
Submitted technical papers must be no longer than six pages, including all figures and references, and must be formatted according to posted IJCAI-09 guidelines. Specifically, papers must be formatted for "letter-size" (8.5" x 11") paper, in double-column format with a 10pt font. Electronic templates for the LaTeX typesetting package, as well as a Word template, that conform to IJCAI-09 guidelines will be made available at the conference website (see above) during the first week of December, as will further details on formatting.
Authors are required to submit their electronic papers in PDF format. Files in Postscript (ps), or any other format will not be accepted.
Submitted papers must not exceed six (6) formatted pages, including references and figures. This six-page limit will be strictly enforced: over-length papers will not be considered for review. Each accepted paper will be allowed six pages in the proceedings; up to two additional pages may be purchased at a price of $275 per page. In order to make blind reviewing possible, authors must omit their names and affiliations from the paper. Also, while the references should include all published literature relevant to the paper, including previous works of the authors, it should not include unpublished works. When referring to one's own work, use the third person rather than the first person. For example, say "Previously, Foo and Bar [7] have shown that...", rather than "In our previous work [7] we have shown that..." For accepted papers, such identifying information can be added to the final camera-ready version for publication.
Review Process
Papers will be subject to blind peer review. Selection criteria include accuracy and originality of ideas, clarity and significance of results and quality of the presentation. Each paper will be assigned to three Program Committee members, one Senior Program Committee member and one Area Chair for review. The reviewing process will include a short period for the authors to view reviews and respond to technical questions on the submitted work raised by the reviewers before final decisions are made. The decision of the Program Committee will be final and cannot be appealed.
Papers accepted for the conference will be scheduled for oral or poster presentation and will be printed in the proceedings. At least one author of each accepted paper will be required to attend the conference to present the work.
Please send inquiries about paper submissions to ijcai09@aaai.org.
Inquiries about the conference program can be directed to:
Craig Boutilier
Program Chair, IJCAI-09
Department of Computer Science
University of Toronto
Toronto, ON, M5S 3H5, CANADA
Email: pcchair09@ijcai.org
For further information please visit the conference web site: http://www.ijcai-09.org
List of keywords:
Agent-based and Multi-agent Systems
- Agent/AI Theories and Architectures
- Agent-based Simulation and Emergent Behavior
- Agent Communication
- Argumentation
- Auctions And Market-Based Systems
- Coordination And Collaboration
- Distributed AI
- E-Commerce
- Game Theory
- Information/Mobile/Software Agents
- Multiagent Learning
- Multiagent Planning
- Multiagent Systems (General/other)
- Negotiation And Contract-Based Systems
- Social Choice Theory
Constraints, Satisfiability, and Search
- Applications
- Constraint Optimization
- Constraint Satisfaction (General/other)
- Distributed Search/CSP/Optimization
- Dynamic Programming
- Search, SAT, CSP: Evaluation and Analysis
- Global Constraints
- Heuristic Search
- Search, SAT, CSP: Meta-heuristics
- Meta-Reasoning
- Quantifier Formulations
- Satisfiability (General/other)
- SAT and CSP: Modeling/Formulations
- Search (General/other)
- SAT and CSP: Solvers and Tools
Knowledge Representation, Reasoning and Logic
- Action, Change and Causality
- Automated Reasoning and Theorem Proving
- Belief Change
- Common-Sense Reasoning
- Computational Complexity of Reasoning
- Description Logics and Ontologies
- Diagnosis and Abductive Reasoning
- Geometric, Spatial, and Temporal Reasoning
- Knowledge Representation Languages
- Knowledge Representation (General/other)
- Logic Programming
- Many-Valued And Fuzzy Logics
- Nonmonotonic Reasoning
- Preferences
- Qualitative Reasoning
- Reasoning with Beliefs
Machine Learning
- Active Learning
- Case-based Reasoning
- Classification
- Cost-Sensitive Learning
- Data Mining
- Ensemble Methods
- Evolutionary Computation
- Feature Selection/Construction
- Kernel Methods
- Learning Graphical Models
- Learning Preferences/Rankings
- Learning Theory
- Machine Learning (General/other)
- Neural Networks
- Online Learning
- Reinforcement Learning
- Relational Learning
- Time-series/Data Streams
- Transfer, Adaptation, Multi-task Learning
- Semi-Supervised/Unsupervised Learning
- Structured Learning
Multidisciplinary Topics And Applications
- AI and Natural Sciences
- AI and Social Sciences
- Art And Music
- Autonomic Computing
- Cognitive Modeling
- Computational Biology
- Computer Games
- Computer-Aided Education
- Database Systems
- Philosophical and Ethical Issues
- Human-Computer Interaction
- Intelligent User Interfaces
- Interactive Entertainment
- Personalization and User Modeling
- Real-Time Systems
- Security and Privacy
- Validation and Verification
Natural-Language Processing
- Dialogue
- Discourse
- Information Extraction
- Information Retrieval
- Machine Translation
- Morphology and Phonology
- Natural Language Generation
- Natural Language Semantics
- Natural Language Summarization
- Natural Language Syntax
- Natural Language Processing (General/other)
- Psycholinguistics
- Question Answering
- Speech Recognition And Understanding
- Text Classification
Planning and Scheduling
- Activity and Plan Recognition
- Hybrid Systems
- Markov Decisions Processes
- Model-Based Reasoning
- POMDPs
- Plan Execution And Monitoring
- Plan/Workflow Analysis
- Planning Algorithms
- Planning under Uncertainty
- Planning (General/other)
- Scheduling
- Theoretical Foundations of Planning
Robotics and Vision
- Behavior And Control
- Cognitive Robotics
- Human Robot Interaction
- Localization, Mapping, State Estimation
- Manipulation
- Motion and Path Planning
- Multi-Robot Systems
- Robotics
- Sensor Networks
- Vision and Perception
Uncertainty in AI
- Approximate Probabilistic Inference
- Bayesian Networks
- Decision/Utility Theory
- Exact Probabilistic Inference
- Graphical Models
- Preference Elicitation
- Sequential Decision Making
- Uncertainty Representations
- Uncertainty in AI (General/other)
Web and Knowledge-based Information Systems
- Information Extraction
- Information Integration
- Information Retrieval
- Knowledge Acquisition
- Knowledge Engineering
- Knowledge-based Systems (General/other)
- Ontologies
- Recommender Systems
- Semantic Web
- Social Networks
- Source Wrapping
- Web Mining
- Web Search
- Web Technologies (General/other)
Call for ICML/UAI/COLT 2009 Workshop Proposals
.
Monday, December 01, 2008
0
comments
The ICML, UAI, and COLT conferences will be colocated in Montreal June 14-21 2009. We solict proposals for workshops to be held during a single joint workshop day on June 18. This date lies between ICML (June 14-17) and UAI/COLT (June 19-21). Workshops will be selected on the basis of their interest to the attendees of one or more of the conferences.
The goal of the workshops is to provide an informal forum for researchers to discuss important research questions and challenges. Controversial issues, open problems, and comparisons of competing approaches are encouraged. Representation of alternative viewpoints and panel-style discussions are also encouraged.
Organization
The format, style, and content of accepted workshops is under the control of the workshop organizers and largely autonomous from the main conferences. The workshops will be seven hours long and split into morning and afternoon sessions. Workshop organizers will be expected to manage the workshop content, specify the workshop format, be present to moderate the discussion and panels, invite experts in the domain, and maintain a website for the workshop. Workshop registration will be handled centrally by the main conferences with a single uniform registration fee and with registrants allowed to attend workshops other than the one they register for.
Submission Instructions
Proposals should specify clearly all of the following:
Please also provide brief CVs of all organizers. This information should be sent by email (in plain text or pdf format) to Icml-uai-colt-workshops09@ssli.ee.washington.edu by 19 Jan 2009.
The goal of the workshops is to provide an informal forum for researchers to discuss important research questions and challenges. Controversial issues, open problems, and comparisons of competing approaches are encouraged. Representation of alternative viewpoints and panel-style discussions are also encouraged.
Organization
The format, style, and content of accepted workshops is under the control of the workshop organizers and largely autonomous from the main conferences. The workshops will be seven hours long and split into morning and afternoon sessions. Workshop organizers will be expected to manage the workshop content, specify the workshop format, be present to moderate the discussion and panels, invite experts in the domain, and maintain a website for the workshop. Workshop registration will be handled centrally by the main conferences with a single uniform registration fee and with registrants allowed to attend workshops other than the one they register for.
Submission Instructions
Proposals should specify clearly all of the following:
- the workshop's title (what is it called?)
- topic (what is it about?)
- motivation (why a workshop on this topic?)
- impact and expected outcomes (what will having the workshop do?)
- potential invited speakers (who might come?)
- a list of related publications (where can we learn more?)
- main workshop organizer (who is making it happen?)
- other organizers (who else is making it happen?)
- workshop URL (where will interested parties get more information?)
- relevant conferences (which of ICML, UAI, and COLT would it appeal to?)
Please also provide brief CVs of all organizers. This information should be sent by email (in plain text or pdf format) to Icml-uai-colt-workshops09@ssli.ee.washington.edu by 19 Jan 2009.
ICML 2008 Call for Papers
.
Thursday, November 27, 2008
0
comments
The 26th International Conference On Machine Learning (ICML-2009)
June 14-18, 2009, Montreal, Canada
June 14-18, 2009, Montreal, Canada
This call for papers extends the preliminary call by including the conference website, icml2009.org, and the list of area chairs and topic descriptors, www.cs.rutgers.edu/~mlittman/icml09/ac.html . Please browse the list of area chairs to get a sense of the scope and coverage of this year's conference. We encourage a broad range of submissions!
ICML 2009 invites submission of engagingly written papers on substantial, original, and previously unpublished research in *all* aspects of machine learning. We welcome submissions of innovative work on systems that are self adaptive, systems that improve their own performance, or systems that apply logical, statistical, probabilistic or other formalisms to the analysis of data, to the learning of predictive models, or to interaction with the environment. We welcome innovative applications, theoretical contributions, carefully evaluated empirical studies, and we particularly welcome work that combines all of these elements. We also encourage submissions that bridge the gap between machine learning and other fields of research. ICML 2009 will be held in Montreal, Canada, June 14-18, 2009, and will be co-located with the Uncertainty in Artificial Intelligence Conference (UAI), and the Conference on Learning Theory (COLT), and Multidisciplinary Symposium on Reinforcement Learning (MSRL).
DATES (Note slightly earlier schedule than 2008):
- January 26: Full paper submissions due (no separate abstract date)
- February 27: First round reviews available
- March 10: Author responses due
- April 6: Acceptance notification
- April 20: Final camera-ready version due
- June 14: ICML Tutorials
- June 15-17 ICML Conference
- June 18: Joint Workshops Day, ICML/UAI/COLT; MSRL
The conference will include three days of technical presentations, one day of tutorials and one day of workshops. Accepted papers will each have an oral presentation as well as a poster in an evening poster session. There will also be talks by several invited speakers and a banquet.
Awards
Awards will be given for Best Paper(s), Best Student Paper(s) (first-authored by a student), Best Application Paper, 10-year Best Paper (most influential paper of ICML 1999).
Submission
Submission format, details and style files will soon be available on the ICML 2009 website (icml2009.org). Submission of papers and the management of the paper reviewing process will be entirely electronic.
Review Process (New for 2009!)
Our review process this year will be slightly different from previous years to further encourage innovative papers on a variety of topics. Authors will indicate a preference for an area chair to handle their papers via an inverse bidding process. It is crucial for authors to familiarize themselves with the 2009 area chairs and their topic descriptiors (http://www.cs.rutgers.edu/~mlittman/icml09/ac.html). The goal is to ensure each submission is considered by reviewers appropriate to the paper's intended contribution. Each submitted paper will receive two first round reviews. As in recent years, authors will have the opportunity to see and respond to the reviews before a final decision is made. Papers that receive at least one positive review in the first round will receive one or more additional reviews. Final decisions will be made using the input from all reviewers, the author feedback, the assigned area chair, and programme co-chairs. Reviewing for ICML 2009 will be blind to the identities of the authors. No conditional accepts will be granted this year.
ICML 2009 will not accept any paper that is substantially similar to another paper that is currently under review or has already been accepted for publication in a journal or another conference. The programme co-chairs will consider making an exception for papers published in substantially disjoint communities (application conferences, for example), as long as the submitted papers are themselves clearly targeted to a machine-learning audience. Please clearly indicate which contributions are novel and which are previous work, either by the authors or others. If a paper submitted to ICML 2009 and another already published or already submitted paper contain substantial overlap in content and the content is not clearly indicated (anonymously) as being previous work, then the ICML submission may be rejected on the grounds of being a dual submission.
Similarly, authors must withdraw their papers if they submit an overlapping paper elsewhere during ICML's review period.
With your help, we expect another excellent conference!
-The ICML2009 Organizational Team
General Chair:
Andrea Danyluk (Williams College)
Programme co-chairs:
Leon Bottou (NEC Research)
Michael Littman (Rutgers University)
Local Arrangements Chair:
Doina Precup (McGill University)
Workshop on Machine Learning Open Source 2008
.
Wednesday, September 10, 2008
0
comments
I like Open Access and Open Software, in fact, I'm meber of the local LUG of my University (GLUEM) and some of my posts are refered to these topics. In ML-News list, I've seen a call for submissiones for the a Workshop on Machine Learning Open Source (MLOSS), that will be held at NIPS, December 12th. I this this kind of workshops are a very good idea to promote the use of Open Software in ML, and give extra benefits to those developers that let the community use their software, allowing other researchers a faster development of their experiments.
The NIPS workshop on Workshop on Machine Learning Open Source Software (MLOSS) will held in Whistler (B.C.) on the 12th of December, 2008.
Important Dates
===============
* Submission Date: October 1st, 2008
* Notification of Acceptance: October 14th, 2008
* Workshop date: December 12 or 13th, 2008
Call for Contributions
======================
The organizing committee is currently seeking abstracts for talks at MLOSS 2008. MLOSS is a great opportunity for you to tell the community about your use, development, or philosophy of open source software in machine learning. This includes (but is not limited to) numeric packages (as e.g. R,octave,numpy), machine learning toolboxes and implementations of ML-algorithms. The committee will select several submitted abstracts for 20-minute talks. The submission process is very simple:
* Tag your mloss.org project with the tag nips2008
* Ensure that you have a good description (limited to 500 words)
* Any bells and whistles can be put on your own project page, and of course provide this link on mloss.org
On 1 October 2008, we will collect all projects tagged with nips2008 for review.
Note: Projects must adhere to a recognized Open Source License (cf. http://www.opensource.org/licenses/ ) and the source code must have been released at the time of submission. Submissions will be reviewed based on the status of the project at the time of the
submission deadline.
Description
===========
We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of machine learning. The goal of this workshop is to further support the current developments in this area and give new impulses to it. Following the success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the Journal of Machine Learning Research (JMLR) has started a new track for machine learning open source software initiated by the workshop's organizers. Many prominent machine learning researchers have co-authored a position paper advocating the need for open source software in machine learning. Furthermore, the workshop's organizers have set up a community website mloss.org where people can register
their software projects, rate existing projects and initiate discussions about projects and related topics. This website currently lists 123 such projects including many prominent projects in the area of machine learning.
The main goal of this workshop is to bring the main practitioners in the area of machine learning open source software together in order to initiate processes which will help to further improve the development of this area. In particular, we have to move beyond a mere collection of more or less unrelated software projects and provide a common foundation to stimulate cooperation and interoperability between different projects. An important step in this direction will be a common data exchange format such that different methods can exchange their results more easily.
This year's workshop sessions will consist of three parts.
* We have two invited speakers: John Eaton, the lead developer of Octave and John Hunter, the lead developer of matplotlib.
* Researchers are invited to submit their open source project to present it at the workshop.
* In discussion sessions, important questions regarding the future development of this area will be discussed. In particular, we will discuss what makes a good machine learning software project and how to improve interoperability between programs. In addition, the question of how to deal with data sets and reproducibility will also be addressed.
Taking advantage of the large number of key research groups which attend NIPS, decisions and agreements taken at the workshop will have the potential to significantly impact the future of machine learning software.
Invited Speakers
================
* John D. Hunter - Main author of matplotlib.
* John W. Eaton - Main author of Octave.
Tentative Program
=================
The 1 day workshop will be a mixture of talks (including a mandatory demo of the software) and panel/open/hands-on discussions.
Morning session: 7:30am - 10:30am
* Introduction and overview
* Octave (John W. Eaton)
* Contributed Talks
* Discussion: What is a good mloss project?
o Review criteria for JMLR mloss
o Interoperable software
o Test suites
Afternoon session: 3:30pm - 6:30pm
* Matplotlib (John D. Hunter)
* Contributed Talks
* Discussion: Reproducible research
o Data exchange standards
o Shall datasets be open too? How to provide access to data sets.
o Reproducible research, the next level after UCI datasets.
Program Committee
=================
* Jason Weston (NEC Princeton, USA)
* Gunnar Rätsch (FML Tuebingen, Germany)
* Lieven Vandenberghe (University of California LA, USA)
* Joachim Dahl (Aalborg University, Denmark)
* Torsten Hothorn (Ludwig Maximilians University, Munich, Germany)
* Asa Ben-Hur (Colorado State University, USA)
* William Stafford Noble (Department of Genome Sciences Seattle, USA)
* Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
* Geoff Holmes (University of Waikato, New Zealand)
* Alain Rakotomamonjy (University of Rouen, France)
Organizers
==========
* Soeren Sonnenburg
Fraunhofer FIRST Kekuléstr. 7, 12489 Berlin, Germany
* Mikio Braun
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587
Berlin, Germany
* Cheng Soon Ong
ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland
Funding
=======
The workshop is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
The NIPS workshop on Workshop on Machine Learning Open Source Software (MLOSS) will held in Whistler (B.C.) on the 12th of December, 2008.
Important Dates
===============
* Submission Date: October 1st, 2008
* Notification of Acceptance: October 14th, 2008
* Workshop date: December 12 or 13th, 2008
Call for Contributions
======================
The organizing committee is currently seeking abstracts for talks at MLOSS 2008. MLOSS is a great opportunity for you to tell the community about your use, development, or philosophy of open source software in machine learning. This includes (but is not limited to) numeric packages (as e.g. R,octave,numpy), machine learning toolboxes and implementations of ML-algorithms. The committee will select several submitted abstracts for 20-minute talks. The submission process is very simple:
* Tag your mloss.org project with the tag nips2008
* Ensure that you have a good description (limited to 500 words)
* Any bells and whistles can be put on your own project page, and of course provide this link on mloss.org
On 1 October 2008, we will collect all projects tagged with nips2008 for review.
Note: Projects must adhere to a recognized Open Source License (cf. http://www.opensource.org/licenses/ ) and the source code must have been released at the time of submission. Submissions will be reviewed based on the status of the project at the time of the
submission deadline.
Description
===========
We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of machine learning. The goal of this workshop is to further support the current developments in this area and give new impulses to it. Following the success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the Journal of Machine Learning Research (JMLR) has started a new track for machine learning open source software initiated by the workshop's organizers. Many prominent machine learning researchers have co-authored a position paper advocating the need for open source software in machine learning. Furthermore, the workshop's organizers have set up a community website mloss.org where people can register
their software projects, rate existing projects and initiate discussions about projects and related topics. This website currently lists 123 such projects including many prominent projects in the area of machine learning.
The main goal of this workshop is to bring the main practitioners in the area of machine learning open source software together in order to initiate processes which will help to further improve the development of this area. In particular, we have to move beyond a mere collection of more or less unrelated software projects and provide a common foundation to stimulate cooperation and interoperability between different projects. An important step in this direction will be a common data exchange format such that different methods can exchange their results more easily.
This year's workshop sessions will consist of three parts.
* We have two invited speakers: John Eaton, the lead developer of Octave and John Hunter, the lead developer of matplotlib.
* Researchers are invited to submit their open source project to present it at the workshop.
* In discussion sessions, important questions regarding the future development of this area will be discussed. In particular, we will discuss what makes a good machine learning software project and how to improve interoperability between programs. In addition, the question of how to deal with data sets and reproducibility will also be addressed.
Taking advantage of the large number of key research groups which attend NIPS, decisions and agreements taken at the workshop will have the potential to significantly impact the future of machine learning software.
Invited Speakers
================
* John D. Hunter - Main author of matplotlib.
* John W. Eaton - Main author of Octave.
Tentative Program
=================
The 1 day workshop will be a mixture of talks (including a mandatory demo of the software) and panel/open/hands-on discussions.
Morning session: 7:30am - 10:30am
* Introduction and overview
* Octave (John W. Eaton)
* Contributed Talks
* Discussion: What is a good mloss project?
o Review criteria for JMLR mloss
o Interoperable software
o Test suites
Afternoon session: 3:30pm - 6:30pm
* Matplotlib (John D. Hunter)
* Contributed Talks
* Discussion: Reproducible research
o Data exchange standards
o Shall datasets be open too? How to provide access to data sets.
o Reproducible research, the next level after UCI datasets.
Program Committee
=================
* Jason Weston (NEC Princeton, USA)
* Gunnar Rätsch (FML Tuebingen, Germany)
* Lieven Vandenberghe (University of California LA, USA)
* Joachim Dahl (Aalborg University, Denmark)
* Torsten Hothorn (Ludwig Maximilians University, Munich, Germany)
* Asa Ben-Hur (Colorado State University, USA)
* William Stafford Noble (Department of Genome Sciences Seattle, USA)
* Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
* Geoff Holmes (University of Waikato, New Zealand)
* Alain Rakotomamonjy (University of Rouen, France)
Organizers
==========
* Soeren Sonnenburg
Fraunhofer FIRST Kekuléstr. 7, 12489 Berlin, Germany
* Mikio Braun
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587
Berlin, Germany
* Cheng Soon Ong
ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland
Funding
=======
The workshop is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
Dashboards, pointing the way of Business Intelligence
.
Monday, September 08, 2008
3
comments
I use to read an interesting spanish blog related to BI world, called TodoBI, which pointed me to an article about the importance of dashboards in Business Intelligence, written by Tom Gonzalez. In this article, Tom exposes his vision about the future of Business Intelligence. Tom believes that BI should focus on dashboards, adopting a user-centric approach instead of a more data-centric one. In Tom's words
So where does that leave us today, and what does this all mean for the future of BI? I think dashboards represent just the first step for the next major phase in BI both from a technology and a methodology perspective. For lack of a better term I will label this next phase the "BI user experience" as represented by user interfaces that information workers and business executives interact with to "experience" their data [...] Your ability to process that information and the inherent relationships within that data is exponentially higher and faster with the bar chart. This is one area where the human brain still far exceeds the power of technology-driven computation in its ability to recognize and process patterns composed of large volumes of information.
I totally agree with Tom's vision, which fits in my vision of the connection between Machine Learning, Data Mining and Business Intelligence. For me, ML, DM and BI can be seen as 3 different areas, but they can also be seen as a chain where each one plays an important role. DM is data centric as it focuses on data, BI is user centric as it should deal with users needs and ML is the intelligence behind the process (althought not every need needs an intelligent process).
In the figure, ML is represented inside DM and DM inside BI. From the BI point of view, DM is like glacé cherry, a turn of the screw from the statistical processes behind BI. ML is inside DM as it is the engine for processing all the data in DM processes.
JMLR: Workshop and Conference Proceedings
.
Tuesday, July 15, 2008
20
comments
The Journal of Machine Learning Research (JMLR) is one of the leading journals in Machine Learning. Ranked the 7th in "Computer Science, Artificial Intelligence" category from the JCR, its impact factor is 2.682.
Beyond the quality of the journal and the papers published there, JMLR has represented a great initiative as the first quality Open Access journal in the Machine Learning field. From two years ago until now, JMLR tries to innovate with new initiatives like the support to the development of Open Source Machine Learning software or the recent creation of a special "Conference and Workshop Proceedings" series that aims publishing the work presented at Machine Learning Workshops and Conferences in an Open Access manner. These series have a ISSN (1938-7228) and is described by JMLR as follows
Beyond the quality of the journal and the papers published there, JMLR has represented a great initiative as the first quality Open Access journal in the Machine Learning field. From two years ago until now, JMLR tries to innovate with new initiatives like the support to the development of Open Source Machine Learning software or the recent creation of a special "Conference and Workshop Proceedings" series that aims publishing the work presented at Machine Learning Workshops and Conferences in an Open Access manner. These series have a ISSN (1938-7228) and is described by JMLR as follows
The JMLR: Workshop and Conference Proceedings series is a new series aimed specifically at publishing work presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference and will be pulished online on the JMLR web site. Authors will retain copyright and individual volume editors are free to make additional hardcopy publishing arrangments, but JMLR will not produce hardcopies of these volumes.
AUC as Performance Metric in ML
.
Friday, July 04, 2008
11
comments
ROC analysis is a classic methodology from signal detection theory used to depict the tradeoff between hit rates and false alarm rates of classifiers (Egan 1975, Swets 2000). ROC graphs has also been commonly used on medical diagnosis for visualizing and analyzing the behavior of diagnostic systems (Swets 1998). Spackman (Spackman 1989) was one of the first machine learning researchers to show interest in using ROC curves. Since then, the interest of the machine learning community in ROC analysis has increased, due in part to the realization that simple classification accuracy is often a poor metric for measuring performance (Provost 1997, Provost 1998).
The ROC curve compares the classifier's performance accross the entire range of class distributions and error costs (Provost 1997, Provost 1998). A ROC curve is a two-dimensional representation of classifier performance, which can be useful to represent some characteristics of the classifiers, but makes difficult to compare versus other classifiers. A common method to transform ROC performance to a scalar value, that is easier to manage, consists on calculate the area under the ROC curve (AUC) (Fawcett 2005). As the ROC curve is represented in a unit square, the AUC value will always be between 0.0 and 1.0, being the best classifiers the ones with a higher AUC value. As random guessing produces the diagonal line between (0,0) and (1,1), which has an area of 0.5, no real classifier should have an AUC less than 0.5.
The ROC curve compares the classifier's performance accross the entire range of class distributions and error costs (Provost 1997, Provost 1998). A ROC curve is a two-dimensional representation of classifier performance, which can be useful to represent some characteristics of the classifiers, but makes difficult to compare versus other classifiers. A common method to transform ROC performance to a scalar value, that is easier to manage, consists on calculate the area under the ROC curve (AUC) (Fawcett 2005). As the ROC curve is represented in a unit square, the AUC value will always be between 0.0 and 1.0, being the best classifiers the ones with a higher AUC value. As random guessing produces the diagonal line between (0,0) and (1,1), which has an area of 0.5, no real classifier should have an AUC less than 0.5.
Fig. 1. Example of ROC graphs, figure extracted from (Fawcett 2005). Subfigure a shows the AUC of two different classifiers. Subfigure b compares the graph of a scoring classifier B, and a discrete simplification of the same classifier, A.Figure 1a shows two ROC curves representing two classifiers, A and B. Classifier B obtains higher AUC than classifier A and, therefore, it is supposed to behave better. Figure 1b shows a comparison between a scoring classifier (B) and a binary version of this classifier (A). Classifier A represents the performance of B when it is used with a fixed threshold. Though they represent almost the same classifier, A's performance measured by AUC is inferior to B. As we have seen, it can not be generated a full ROC curve from a discrete classifier, resulting in a less accurate performance analysis. Regarding this problem, in this paper we focus on scoring classifiers, but there are some attempts to create scoring classifiers from discrete ones (Domingos 2000, Fawcett 2001).
Hand and Till (Hand2001) present a simple approach to calculating the AUC of a given classifier.
REFERENCES
- (Domingos 2000) P. Domingos, F. Provost, Well-trained PETs: Improving Probability Estimation Trees, 2000.
- (Egan 1975) J. P. Egan, Signal Detection Theory and ROC Analysis. Series in Cognition and Perception. Academic Press, 1975.
- (Fawcett 2001) T. Fawcett. Using rule sets to Maximize ROC performance. In IEEE International Conference on Data Mining, pp. 131-138, 2001.
- (Fawcett 2005) T. Fawcett. An Introduction to ROC Analysis. Pattern Recognition Letters, 27:861-874, 2005.
- (Hand 2001) D. J. Hand, R. J. Tiller, A Simple Generalization of the Area under the ROC Curve to Multiple Class Classification Problems. Machine Learning, 45(2), pp. 171-186, 2001.
- (Provost 1997) F. Provost, T. Fawcett, Analysis and Visualization of Classifier Performance. In Proceedings of the 13th Intenational Conference on Knowledge Discovery and Data Mining, pp. 43-48. AAAI Press, 1997.
- (Provost 1998) F. Provost, T. Fawcett, R. Kohavi. The Case Against Accuracy Estimation for Comparing Induction Algorithms. In Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445-453.
- (Spackman 1989) K. A. Spackman. Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning. In Proceedings of the Sixth International Workshop on Machine Learning, pp. 160-163. 1989.
- (Swets 1998) J. A. Swets, Measuring the Accuracy of Diagnosis Systems. Science (240):1285-1293, 1988.
- (Swets 2000) J. A. Swets, R. M. Dawes, J. Monahan, Better Decision Through Science, Scientific American Magazine, October 2000.
The Need for Open Source Software in Machine Learning
.
Wednesday, June 25, 2008
3
comments
Reading Undirect Grad blog, I found an interesting paper about the need of more Open Software in Machine Learning. The abstract:
The same happens with publications. Open Access should be a neccesary condition for every public funded research. Luckily, there are several iniciatives all around the globe trying to spread the benefits of the Open Access model, as Harvard's addoption of Open Access or the support of the Comunidad de Madrid (a Spanish region) to several Open Access iniciatives (sorry for the link in Spanish).
In recent years, the ML community has improve in this aspects. We count on a very good Open Source ML framework as Weka, we have a top Open Access Journal as JMLR that also supports ML Open Source software and a very good Open Source software repository like MLOSS.
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.I think this paper addresses a very interesting problem, not only for the ML community. As said in the paper, "Open Source model allows better reproducibility of the results, quicker detection errors, innovative applications, faster adoption of ML methods in other disciplines", but it also avoids a constant reinvention of the wheel, and is a fairer model because if most of the researchs are funded by public money, why should researchers stop the access to the code?
The same happens with publications. Open Access should be a neccesary condition for every public funded research. Luckily, there are several iniciatives all around the globe trying to spread the benefits of the Open Access model, as Harvard's addoption of Open Access or the support of the Comunidad de Madrid (a Spanish region) to several Open Access iniciatives (sorry for the link in Spanish).
In recent years, the ML community has improve in this aspects. We count on a very good Open Source ML framework as Weka, we have a top Open Access Journal as JMLR that also supports ML Open Source software and a very good Open Source software repository like MLOSS.
Automated Microarray Classification Challenge
.
Tuesday, June 24, 2008
0
comments
The diagnosis of cancer on the basis of gene expression profiles is well established, so much so that micro-array classification has become one of the classic applications of machine learning in
computational biology. The field has now reached the stage where a large scale evaluation exercise is warranted to determine the advantages and disadvantages of competing approaches. We have therefore organized a challenge for ICMLA'08, the aim of which is to determine the best fully automated approach to micro-array classification. An unusual feature of the competition is that instead of submitting predictions on test cases, the competitors submit a MATLAB implementation of their algorithm (R and Java interfaces are also in development), which is then tested off-line by the challenge organizers. This will test the true operational value of the method, in the hands of an end user who is not necessarily an expert in a given technique. The winner of the challenge will receive a free registration to ICMLA'08.
Further details and background information regarding the competition are available from the challenge website, http://theoval.cmp.uea.ac.uk/~gcc/projects/amcc. If you have any questions, please feel free to contact the challenge organizers (g...@cmp.uea.ac.uk).
The results of the challenge will be presented at a special session at ICMLA'08. Competitors are encouraged to participate in the special session and are invited to submit a technical paper describing their technique. Submissions should be made electronically in PDF format using the central ICMLA'08 website. The deadline for submissions is June 15, 2008. All accepted papers must be presented by one of the authors in order to be published in the conference proceeding.
Important Dates
Challenge opens March 10, 2008
Challenge closes Julu 15, 2008
Paper submission due July 15, 2008
Notification of acceptance September 1,
2008
Camera-ready papers & pre-registration October 1, 2008
ICMLA'08 conference December 11-13, 2008
Special Session Chair
Dr Wenjia Wang, University of East Anglia, Norwich, U.K.
Special Session Organizers
Dr Gavin Cawley, University of East Anglia, Norwich, U.K.
Dr Wenjia Wang, University of East Anglia, Norwich, U.K.
Mr Geoffrey Guile, University of East Anglia, Norwich, U.K.
computational biology. The field has now reached the stage where a large scale evaluation exercise is warranted to determine the advantages and disadvantages of competing approaches. We have therefore organized a challenge for ICMLA'08, the aim of which is to determine the best fully automated approach to micro-array classification. An unusual feature of the competition is that instead of submitting predictions on test cases, the competitors submit a MATLAB implementation of their algorithm (R and Java interfaces are also in development), which is then tested off-line by the challenge organizers. This will test the true operational value of the method, in the hands of an end user who is not necessarily an expert in a given technique. The winner of the challenge will receive a free registration to ICMLA'08.
Further details and background information regarding the competition are available from the challenge website, http://theoval.cmp.uea.ac.uk/~gcc/projects/amcc. If you have any questions, please feel free to contact the challenge organizers (g...@cmp.uea.ac.uk).
The results of the challenge will be presented at a special session at ICMLA'08. Competitors are encouraged to participate in the special session and are invited to submit a technical paper describing their technique. Submissions should be made electronically in PDF format using the central ICMLA'08 website. The deadline for submissions is June 15, 2008. All accepted papers must be presented by one of the authors in order to be published in the conference proceeding.
Important Dates
Challenge opens March 10, 2008
Challenge closes Julu 15, 2008
Paper submission due July 15, 2008
Notification of acceptance September 1,
2008
Camera-ready papers & pre-registration October 1, 2008
ICMLA'08 conference December 11-13, 2008
Special Session Chair
Dr Wenjia Wang, University of East Anglia, Norwich, U.K.
Special Session Organizers
Dr Gavin Cawley, University of East Anglia, Norwich, U.K.
Dr Wenjia Wang, University of East Anglia, Norwich, U.K.
Mr Geoffrey Guile, University of East Anglia, Norwich, U.K.
The Discipline of Machine Learning
.
Tuesday, June 17, 2008
0
comments
Tom Mitchell is one of the key personalities of Machine Learning discipline. He has been working in this area since the end of the 70's, published some reference ML textbooks and, first of all, he is the head of the first Machine Learning department all around the world.
In 2006, when he was "fighting" for the creation of the ML department at the Carnegie Mellon University, he was said that "you can only have a department if you have a discipline that is going to be here in one hundred years otherwise you can not have a department". For stating that ML would last more that a hundred years, he wrote a white paper, "The Discipline of Machine Learning", that is a real must-read paper for all the people interested in ML. The abstract of the paper states
Tom also gave a speech related to this matter at the Carnegie Mellon University School of Computer Science's Machine Learning Department in March 2007. You can watch Mitchell's speech in this video.
In 2006, when he was "fighting" for the creation of the ML department at the Carnegie Mellon University, he was said that "you can only have a department if you have a discipline that is going to be here in one hundred years otherwise you can not have a department". For stating that ML would last more that a hundred years, he wrote a white paper, "The Discipline of Machine Learning", that is a real must-read paper for all the people interested in ML. The abstract of the paper states
Over the past 50 years the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of Statistics that largely ignored computational considerations, to a broad discipline that has produced fundamental statistical-computational theories of learning processes, has designed learning algorithms that are routinely used in commercial systems for speech recognition, computer vision, and a variety of other tasks, and has spun off an industry in data mining to discover hidden regularities in the growing volumes of online data. This document provides a brief and personal view of the discipline that has emerged as Machine Learning, the fundamental questions it addresses, its relationship to other sciences and society, and where it might be headed.
Tom also gave a speech related to this matter at the Carnegie Mellon University School of Computer Science's Machine Learning Department in March 2007. You can watch Mitchell's speech in this video.
Machine Learning OnLine Lectures
.
Monday, March 19, 2007
12
comments
Pierre Dangauthier posts about an interesting topic: online Machine Learning videos (lectures, research talks, etc.). There's a lot of interesting stuff and I list some I've found
- Feature Selection Through Lasso
- Sparse and large-scale learning with heterogeneous data
- Privacy Preserving Data Mining
- Applications of Artificial Intelligence
- Semisupervised Learning Approaches (Tom Mitchell)
- Machine Learning, Probability and Graphical Models
- Statistical Learning Theory
- Statistical Learning Theory (Olivier Bousquet)
- Support Vector Machines
- When Training and Test Distributions are Different: Characterising Learning Transfer
- Learning with Kernels
- Machine Learning Reductions (John Langford)
- Kernel Methods
- Random projection, margins, kernels, and feature-selection
- Kernel Methods in Statistical Learning
- Pattern Analysis with Graphs and Trees
- Tutorial on Statistical Machine Learning with Applications to Multimodal Processing
- Introduction to the Machine Learning over Text & Images - Autumn School by Eric Xing
- Statistical Learning Theory - Graph Models
