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- ICML'23: Proceedings of the 40th International Conference on Machine Learning (4)
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- NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems (2)
- NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems (2)
- STOC 2018: Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (2)
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- FOCS '10: Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science (1)
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- FOCS '13: Proceedings of the 2013 IEEE 54th Annual Symposium on Foundations of Computer Science (1)
- From graphs to matrices, and back (1)
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- research-article
Editing a classifier by rewriting its prediction rules
- Shibani Santurkar
MIT
, - Dimitris Tsipras
MIT
, - Mahalaxmi Elango
MIT
, - David Bau
MIT
, - Antonio Torralba
MIT
, - Aleksander Mądry
MIT
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 1789, pp 23359-23373We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules.1 Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3540261.3542050_supp.pdf
- Shibani Santurkar
- research-article
Unadversarial examples: designing objects for robust vision
- Hadi Salman
MIT
, - Andrew Ilyas
MIT
, - Logan Engstrom
MIT
, - Sai Vemprala
Microsoft Research
, - Aleksander Mądry
MIT
, - Ashish Kapoor
Microsoft Research
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 1170, pp 15270-15284We study a class of computer vision settings wherein one can modify the design of the objects being recognized. We develop a framework that leverages this capability—and deep networks' unusual sensitivity to input perturbations—to design "robust objects,"...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3540261.3541431_supp.pdf
- Hadi Salman
- research-article
MODELDIFF: a framework for comparing learning algorithms
- Harshay Shah
Massachusetts Institute of Technology
, - Sung Min Park
Massachusetts Institute of Technology
, - Andrew Ilyas
Massachusetts Institute of Technology
, - Aleksander Mądry
Massachusetts Institute of Technology
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 1271, pp 30646-30688We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., ...
- 0Citation
MetricsTotal Citations0
- Harshay Shah
- research-article
Raising the cost of malicious AI-powered image editing
- Hadi Salman
MIT
, - Alaa Khaddaj
MIT
, - Guillaume Leclerc
MIT
, - Andrew Ilyas
MIT
, - Aleksander Mądry
MIT
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 1240, pp 29894-29918We present an approach to mitigating the risks of malicious image editing posed by large diffusion models. The key idea is to immunize images so as to make them resistant to manipulation by these models. This immunization relies on injection of ...
- 0Citation
MetricsTotal Citations0
- Hadi Salman
- research-article
TRAK: attributing model behavior at scale
- Sung Min Park
Department of EECS, Massachusetts Institute of Technology, Cambridge, MA
, - Kristian Georgiev
Department of EECS, Massachusetts Institute of Technology, Cambridge, MA
, - Andrew Ilyas
Department of EECS, Massachusetts Institute of Technology, Cambridge, MA
, - Guillaume Leclerc
Department of EECS, Massachusetts Institute of Technology, Cambridge, MA
, - Aleksander Mądry
Department of EECS, Massachusetts Institute of Technology, Cambridge, MA
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 1128, pp 27074-27113The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to force users to choose between computational tractability and efficacy. That is,...
- 0Citation
MetricsTotal Citations0
- Sung Min Park
- research-article
Rethinking backdoor attacks
- Alaa Khaddaj
MIT
, - Guillaume Leclerc
MIT
, - Aleksandar Makelov
MIT
, - Kristian Georgiev
MIT
, - Hadi Salman
MIT
, - Andrew Ilyas
MIT
, - Aleksander Mądry
MIT
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 664, pp 16216-16236In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks involves viewing inserted examples as outliers in the training ...
- 0Citation
MetricsTotal Citations0
- Alaa Khaddaj
- research-articlefree
Do adversarially robust ImageNet models transfer better?
- Hadi Salman
Microsoft Research
, - Andrew Ilyas
MIT
, - Logan Engstrom
MIT
, - Ashish Kapoor
Microsoft Research
, - Aleksander Mądry
MIT
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems•December 2020, Article No.: 298, pp 3533-3545Transfer learning is a widely-used paradigm in which models pre-trained on standard datasets can efficiently adapt to downstream tasks. Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect ...
- 1Citation
- 21
- Downloads
MetricsTotal Citations1Total Downloads21Last 12 Months18Last 6 weeks4- 1
Supplementary Material3495724.3496022_supp.pdf
- Hadi Salman
- research-articlefree
On adaptive attacks to adversarial example defenses
- Florian Tramèr
Stanford University
, - Nicholas Carlini
Google
, - Wieland Brendel
University of Tübingen
, - Aleksander Mądry
MIT
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems•December 2020, Article No.: 138, pp 1633-1645Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML ...
- 0Citation
- 55
- Downloads
MetricsTotal Citations0Total Downloads55Last 12 Months25Last 6 weeks11
- Florian Tramèr
- research-articlefree
From ImageNet to image classification: contextualizing progress on benchmarks
- Dimitris Tsipras
EECS, MIT
, - Shibani Santurkar
EECS, MIT
, - Logan Engstrom
EECS, MIT
, - Andrew Ilyas
EECS, MIT
, - Aleksander Mądry
EECS, MIT
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 892, pp 9625-9635Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet ...
- 0Citation
- 33
- Downloads
MetricsTotal Citations0Total Downloads33Last 12 Months21Last 6 weeks6- 1
Supplementary Material3524938.3525830_supp.pdf
- Dimitris Tsipras
- research-articlefree
Identifying statistical bias in dataset replication
- Logan Engstrom
MIT
, - Andrew Ilyas
MIT
, - Shibani Santurkar
MIT
, - Dimitris Tsipras
MIT
, - Jacob Steinhardt
UC Berkeley
, - Aleksander Mądry
MIT
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 274, pp 2922-2932Dataset replication is a useful tool for assessing whether improvements in test accuracy on a specific benchmark correspond to improvements in models' ability to generalize reliably. In this work, we present unintuitive yet significant ways in which ...
- 1Citation
- 17
- Downloads
MetricsTotal Citations1Total Downloads17Last 12 Months15Last 6 weeks5- 1
Supplementary Material3524938.3525212_supp.pdf
- Logan Engstrom
- research-articlefree
Image synthesis with a single (robust) classifier
- Shibani Santurkar
MIT
, - Dimitris Tsipras
MIT
, - Brandon Tran
MIT
, - Andrew Ilyas
MIT
, - Logan Engstrom
MIT
, - Aleksander Mądry
MIT
NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems•December 2019, Article No.: 114, pp 1262-1273We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf ...
- 0Citation
- 31
- Downloads
MetricsTotal Citations0Total Downloads31Last 12 Months7Last 6 weeks2
- Shibani Santurkar
- research-articlefree
Adversarial examples are not bugs, they are features
- Andrew Ilyas
MIT
, - Shibani Santurkar
MIT
, - Dimitris Tsipras
MIT
, - Logan Engstrom
MIT
, - Brandon Tran
MIT
, - Aleksander Mądry
MIT
NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems•December 2019, Article No.: 12, pp 125-136Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features:...
- 6Citation
- 145
- Downloads
MetricsTotal Citations6Total Downloads145Last 12 Months82Last 6 weeks9
- Andrew Ilyas
- research-article
Round Compression for Parallel Matching Algorithms
SIAM Journal on Computing, Volume 49, Issue 5, pp STOC18-1-STOC18-44 • https://doi.org/10.1137/18M1197655For over a decade now we have been witnessing the success of massive parallel computation frameworks, such as MapReduce, Hadoop, Dryad, or Spark. Compared to the classic distributed algorithms or PRAM models, these frameworks allow for much more local ...
- 1Citation
MetricsTotal Citations1
- Articlefree
Spectral signatures in backdoor attacks
- Brandon Tran
EECS, MIT, Cambridge, MA
, - Jerry Li
Simons Institute, Berkeley, CA
, - Aleksander Mądry
EECS, MIT
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems•December 2018, pp 8011-8021A recent line of work has uncovered a new form of data poisoning: so-called backdoor attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its ...
- 7Citation
- 173
- Downloads
MetricsTotal Citations7Total Downloads173Last 12 Months74Last 6 weeks10
- Brandon Tran
- Articlefree
Adversarially robust generalization requires more data
- Ludwig Schmidt
UC Berkeley
, - Shibani Santurkar
MIT
, - Dimitris Tsipras
MIT
, - Kunal Talwar
Google Brain
, - Aleksander Madry
MIT
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems•December 2018, pp 5019-5031Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. To better ...
- 10Citation
- 164
- Downloads
MetricsTotal Citations10Total Downloads164Last 12 Months54Last 6 weeks7
- Ludwig Schmidt
- Articlefree
How does batch normalization help optimization?
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems•December 2018, pp 2488-2498Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The ...
- 12Citation
- 1,394
- Downloads
MetricsTotal Citations12Total Downloads1,394Last 12 Months219Last 6 weeks34
- research-articlePublic AccessPublished By ACMPublished By ACM
k-server via multiscale entropic regularization
- Sébastien Bubeck
Microsoft Research, USA
, - Michael B. Cohen
Massachusetts Institute of Technology, USA
, - Yin Tat Lee
University of Washington, USA
, - James R. Lee
University of Washington, USA
, - Aleksander Mądry
Massachusetts Institute of Technology, USA
STOC 2018: Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing•June 2018, pp 3-16• https://doi.org/10.1145/3188745.3188798We present an O((logk)2)-competitive randomized algorithm for the k-server problem on hierarchically separated trees (HSTs). This is the first o(k)-competitive randomized algorithm for which the competitive ratio is independent of the size of the ...
- 35Citation
- 838
- Downloads
MetricsTotal Citations35Total Downloads838Last 12 Months116Last 6 weeks5- 1
Supplementary Material1a-1.mp4
- Sébastien Bubeck
- research-articlePublished By ACMPublished By ACM
Round compression for parallel matching algorithms
- Artur Czumaj
University of Warwick, UK
, - Jakub Łącki
Google Research, USA
, - Aleksander Mądry
Massachusetts Institute of Technology, USA
, - Slobodan Mitrović
EPFL, Switzerland
, - Krzysztof Onak
IBM Research, USA
, - Piotr Sankowski
University of Warsaw, Poland
STOC 2018: Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing•June 2018, pp 471-484• https://doi.org/10.1145/3188745.3188764For over a decade now we have been witnessing the success of massive parallel computation (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for their success is the fact that these frameworks are able to accurately capture ...
- 37Citation
- 376
- Downloads
MetricsTotal Citations37Total Downloads376Last 12 Months23Last 6 weeks3- 1
Supplementary Material4a-5.mp4
- Artur Czumaj
- research-article
On the Resiliency of Static Forwarding Tables
- Marco Chiesa
Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
, - Ilya Nikolaevskiy
Department of Computer Science, Aalto University, Espoo, Finland
, - Slobodan Mitrovic
École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
, - Andrei Gurtov
Linköping University, Sweden
, - Aleksander Madry
Massachusetts Institute of Technology, Cambridge, MA, USA
, - Michael Schapira
The Hebrew University of Jerusalem, Jerusalem, Israel
, - Scott Shenker
University of California at Berkeley, Berkeley, CA, USA
IEEE/ACM Transactions on Networking, Volume 25, Issue 2•April 2017, pp 1133-1146 • https://doi.org/10.1109/TNET.2016.2619398Fast reroute and other forms of immediate failover have long been used to recover from certain classes of failures without invoking the network control plane. While the set of such techniques is growing, the level of resiliency to failures that this ...
- 13Citation
- 91
- Downloads
MetricsTotal Citations13Total Downloads91Last 12 Months12Last 6 weeks1
- Marco Chiesa
- research-article
Negative-weight shortest paths and unit capacity minimum cost flow in Õ(m10/7 log W) time: (extended abstract)
- Michael B. Cohen
MIT
, - Aleksander Mądry
MIT
, - Piotr Sankowski
University of Warsaw
, - Adrian Vladu
MIT
SODA '17: Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms•January 2017, pp 752-771In this paper, we study a set of combinatorial optimization problems on weighted graphs: the shortest path problem with negative weights, the weighted perfect bipartite matching problem, the unit-capacity minimum-cost maximum flow problem, and the ...
- 20Citation
- 142
- Downloads
MetricsTotal Citations20Total Downloads142Last 12 Months4
- Michael B. Cohen
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner