default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKoby Crammer
Person information
- affiliation: Technion, Israel Institute of Technology, Israel
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[c87]Shuxiao Chen, Koby Crammer, Hangfeng He
, Dan Roth, Weijie J. Su:
Weighted Training for Cross-Task Learning. ICLR 2022- 2021
- [i22]Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, Weijie J. Su:
Weighted Training for Cross-Task Learning. CoRR abs/2105.14095 (2021)
2010 – 2019
- 2019
- [i21]Mark Kozdoba, Edward Moroshko, Shie Mannor, Koby Crammer:
Variance Estimation For Online Regression via Spectrum Thresholding. CoRR abs/1906.05591 (2019) - 2018
- [j23]Hadas Benisty, Itamar Katz, Koby Crammer, David Malah:
Discriminative Keyword Spotting for limited-data applications. Speech Commun. 99: 1-11 (2018) - [c86]Yuval Dagan, Koby Crammer:
A Better Resource Allocation Algorithm with Semi-Bandit Feedback. ALT 2018: 268-320 - [c85]Itay Evron, Edward Moroshko, Koby Crammer:
Efficient Loss-Based Decoding on Graphs for Extreme Classification. NeurIPS 2018: 7233-7244 - [i20]Itay Evron, Edward Moroshko, Koby Crammer:
Efficient Loss-Based Decoding On Graphs For Extreme Classification. CoRR abs/1803.03319 (2018) - [i19]Yuval Dagan, Koby Crammer:
A Better Resource Allocation Algorithm with Semi-Bandit Feedback. CoRR abs/1803.10415 (2018) - [i18]Mark Kozdoba, Edward Moroshko, Lior Shani, Takuya Takagi, Takashi Katoh, Shie Mannor, Koby Crammer:
Multi Instance Learning For Unbalanced Data. CoRR abs/1812.07010 (2018) - 2017
- [j22]Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv:
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. IEEE Trans. Pattern Anal. Mach. Intell. 39(9): 1811-1824 (2017) - [c84]Nir Levine, Koby Crammer, Shie Mannor:
Rotting Bandits. NIPS 2017: 3074-3083 - [c83]Edward Moroshko, Koby Crammer:
Online Regression with Controlled Label Noise Rate. ECML/PKDD (2) 2017: 355-369 - [i17]Nir Levine, Koby Crammer, Shie Mannor:
Rotting Bandits. CoRR abs/1702.07274 (2017) - 2016
- [j21]Hadas Benisty, David Malah, Koby Crammer:
Grid-based approximation for voice conversion in low resource environments. EURASIP J. Audio Speech Music. Process. 2016: 3 (2016) - [c82]Boaz Petersil, Avihai Mejer, Idan Szpektor, Koby Crammer:
That's Not My Question: Learning to Weight Unmatched Terms in CQA Vertical Search. SIGIR 2016: 225-234 - [i16]Yonatan Glassner, Koby Crammer:
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache. CoRR abs/1602.00309 (2016) - 2015
- [j20]Edward Moroshko, Nina Vaits, Koby Crammer:
Second-order non-stationary online learning for regression. J. Mach. Learn. Res. 16: 1481-1517 (2015) - [j19]Francesco Orabona, Koby Crammer, Nicolò Cesa-Bianchi:
A generalized online mirror descent with applications to classification and regression. Mach. Learn. 99(3): 411-435 (2015) - [c81]Itamar Katz, Koby Crammer:
Outlier-Robust Convex Segmentation. AAAI 2015: 2701-2707 - [c80]Aviad Barzilai, Koby Crammer:
Convex Multi-Task Learning by Clustering. AISTATS 2015 - [c79]Yuval Cassuto, Koby Crammer:
In-memory hamming similarity computation in resistive arrays. ISIT 2015: 819-823 - [c78]Pedro A. Ortega, Koby Crammer, Daniel D. Lee:
Belief flows for robust online learning. ITA 2015: 70-77 - [c77]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Linear Multi-Resource Allocation with Semi-Bandit Feedback. NIPS 2015: 964-972 - [i15]Pedro A. Ortega, Koby Crammer, Daniel D. Lee:
Belief Flows of Robust Online Learning. CoRR abs/1505.07067 (2015) - [i14]Daniel Barsky, Koby Crammer:
CONQUER: Confusion Queried Online Bandit Learning. CoRR abs/1510.08974 (2015) - [i13]Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv:
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. CoRR abs/1511.01258 (2015) - 2014
- [j18]Edward Moroshko, Koby Crammer:
Weighted last-step min-max algorithm with improved sub-logarithmic regret. Theor. Comput. Sci. 558: 107-124 (2014) - [c76]Koby Crammer:
Doubly Aggressive Selective Sampling Algorithms for Classification. AISTATS 2014: 140-148 - [c75]Edward Moroshko, Koby Crammer:
Selective Sampling with Drift. AISTATS 2014: 651-659 - [c74]Asaf Noy, Koby Crammer:
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions. AISTATS 2014: 678-686 - [c73]Hadas Benisty, David Malah, Koby Crammer:
Non-parallel voice conversion using joint optimization of alignment by temporal context and spectral distortion. ICASSP 2014: 7909-7913 - [c72]Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori:
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. ICML 2014: 280-287 - [c71]Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer:
Concept Drift Detection Through Resampling. ICML 2014: 1009-1017 - [c70]Haim Cohen, Koby Crammer:
Learning Multiple Tasks in Parallel with a Shared Annotator. NIPS 2014: 1170-1178 - [c69]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. UAI 2014: 477-486 - [i12]Edward Moroshko, Koby Crammer:
Selective Sampling with Drift. CoRR abs/1402.4084 (2014) - [i11]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. CoRR abs/1406.3840 (2014) - [i10]Itamar Katz, Koby Crammer:
Outlier-Robust Convex Segmentation. CoRR abs/1411.4503 (2014) - 2013
- [j17]Koby Crammer, Claudio Gentile:
Multiclass classification with bandit feedback using adaptive regularization. Mach. Learn. 90(3): 347-383 (2013) - [j16]Koby Crammer, Alex Kulesza, Mark Dredze:
Adaptive regularization of weight vectors. Mach. Learn. 91(2): 155-187 (2013) - [c68]Edward Moroshko, Koby Crammer:
A Last-Step Regression Algorithm for Non-Stationary Online Learning. AISTATS 2013: 451-462 - [c67]Yevgeny Seldin, Koby Crammer, Peter L. Bartlett:
Open Problem: Adversarial Multiarmed Bandits with Limited Advice. COLT 2013: 1067-1072 - [c66]Noam Slonim, Ehud Aharoni, Koby Crammer:
Hartigan's K-Means Versus Lloyd's K-Means - Is It Time for a Change? IJCAI 2013: 1677-1684 - [c65]Ben Zion Vatashsky, Koby Crammer:
Multi Class Learning with Individual Sparsity. IJCAI 2013: 1729-1735 - [i9]Edward Moroshko, Koby Crammer:
Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic Regret. CoRR abs/1301.6058 (2013) - [i8]Nina Vaits, Edward Moroshko, Koby Crammer:
Second-Order Non-Stationary Online Learning for Regression. CoRR abs/1303.0140 (2013) - [i7]Edward Moroshko, Koby Crammer:
A Last-Step Regression Algorithm for Non-Stationary Online Learning. CoRR abs/1303.3754 (2013) - [i6]Francesco Orabona, Koby Crammer, Nicolò Cesa-Bianchi:
A Generalized Online Mirror Descent with Applications to Classification and Regression. CoRR abs/1304.2994 (2013) - [i5]Yevgeny Seldin, Peter L. Bartlett, Koby Crammer:
Advice-Efficient Prediction with Expert Advice. CoRR abs/1304.3708 (2013) - 2012
- [j15]Axel Bernal, Koby Crammer, Fernando Pereira:
Automated gene-model curation using global discriminative learning. Bioinform. 28(12): 1571-1578 (2012) - [j14]Koby Crammer, Mark Dredze, Fernando Pereira:
Confidence-Weighted Linear Classification for Text Categorization. J. Mach. Learn. Res. 13: 1891-1926 (2012) - [j13]Zhuang Wang, Koby Crammer, Slobodan Vucetic:
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training. J. Mach. Learn. Res. 13: 3103-3131 (2012) - [c64]Edward Moroshko, Koby Crammer:
Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret. ALT 2012: 245-259 - [c63]Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer:
Metric Learning for Graph-Based Domain Adaptation. COLING (Posters) 2012: 255-264 - [c62]Hadas Benisty, David Malah, Koby Crammer:
Modular Global Variance enhancement for voice conversion systems. EUSIPCO 2012: 370-374 - [c61]Koby Crammer, Daniel D. Lee:
Online discriminative learning of phoneme recognition via collections of generalized linear models. ICASSP 2012: 1961-1964 - [c60]Koby Crammer, Alex Kulesza, Mark Dredze:
New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure. ICASSP 2012: 2017-2020 - [c59]Koby Crammer, Gal Chechik:
Adaptive Regularization for Similarity Measures. ICML 2012 - [c58]Avihai Mejer, Koby Crammer:
Training Dependency Parser Using Light Feedback. HLT-NAACL 2012: 488-497 - [c57]Avihai Mejer, Koby Crammer:
Are You Sure? Confidence in Prediction of Dependency Tree Edges. HLT-NAACL 2012: 573-576 - [c56]Koby Crammer, Tal Wagner:
Volume Regularization for Binary Classification. NIPS 2012: 341-349 - [c55]Koby Crammer, Yishay Mansour:
Learning Multiple Tasks using Shared Hypotheses. NIPS 2012: 1484-1492 - [c54]Matan Orbach, Koby Crammer:
Graph-Based Transduction with Confidence. ECML/PKDD (2) 2012: 323-338 - [c53]Yoav Haimovitch, Koby Crammer, Shie Mannor:
More Is Better: Large Scale Partially-supervised Sentiment Classication. ACML 2012: 175-190 - [c52]Roi Livni, Koby Crammer, Amir Globerson:
A Simple Geometric Interpretation of SVM using Stochastic Adversaries. AISTATS 2012: 722-730 - [i4]Koby Crammer, Gal Chechik:
Adaptive Regularization for Weight Matrices. CoRR abs/1206.4639 (2012) - [i3]Koby Crammer, Amir Globerson:
Discriminative Learning via Semidefinite Probabilistic Models. CoRR abs/1206.6815 (2012) - [i2]Yoav Haimovitch, Koby Crammer, Shie Mannor:
More Is Better: Large Scale Partially-supervised Sentiment Classification - Appendix. CoRR abs/1209.6329 (2012) - 2011
- [c51]Nina Vaits, Koby Crammer:
Re-adapting the Regularization of Weights for Non-stationary Regression. ALT 2011: 114-128 - [c50]Koby Crammer, Claudio Gentile:
Multiclass Classification with Bandit Feedback using Adaptive Regularization. ICML 2011: 273-280 - [c49]Noam Slonim, Elad Yom-Tov, Koby Crammer:
Active Online Classification via Information Maximization. IJCAI 2011: 1498-1504 - [c48]Zhuang Wang, Nemanja Djuric, Koby Crammer, Slobodan Vucetic:
Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification. KDD 2011: 24-32 - [i1]Avihai Mejer, Koby Crammer:
Confidence Estimation in Structured Prediction. CoRR abs/1111.1386 (2011) - 2010
- [j12]Mark Dredze, Alex Kulesza, Koby Crammer:
Multi-domain learning by confidence-weighted parameter combination. Mach. Learn. 79(1-2): 123-149 (2010) - [j11]Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan:
A theory of learning from different domains. Mach. Learn. 79(1-2): 151-175 (2010) - [c47]Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer:
Learning Better Data Representation Using Inference-Driven Metric Learning. ACL (2) 2010: 377-381 - [c46]Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jennifer Wortman Vaughan:
Regret Minimization With Concept Drift. COLT 2010: 168-180 - [c45]Avihai Mejer, Koby Crammer:
Confidence in Structured-Prediction Using Confidence-Weighted Models. EMNLP 2010: 971-981 - [c44]Koby Crammer:
Efficient online learning with individual learning-rates for phoneme sequence recognition. ICASSP 2010: 4878-4881 - [c43]Zhuang Wang, Koby Crammer, Slobodan Vucetic:
Multi-Class Pegasos on a Budget. ICML 2010: 1143-1150 - [c42]Koby Crammer, Daniel D. Lee:
Learning via Gaussian Herding. NIPS 2010: 451-459 - [c41]Francesco Orabona, Koby Crammer:
New Adaptive Algorithms for Online Classification. NIPS 2010: 1840-1848 - [c40]Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira:
Exploiting Feature Covariance in High-Dimensional Online Learning. AISTATS 2010: 493-500
2000 – 2009
- 2009
- [c39]Koby Crammer, Mark Dredze, Alex Kulesza:
Multi-Class Confidence Weighted Algorithms. EMNLP 2009: 496-504 - [c38]Ted Sandler, Lyle H. Ungar, Koby Crammer:
Resolving Identity Uncertainty with Learned Random Walks. ICDM 2009: 457-465 - [c37]Hui Lin, Jeff A. Bilmes, Koby Crammer:
How to loose confidence: probabilistic linear machines for multiclass classification. INTERSPEECH 2009: 2559-2562 - [c36]Kedar Bellare, Koby Crammer, Dayne Freitag:
Loss-Sensitive Discriminative Training of Machine Transliteration Models. HLT-NAACL (Student Research Workshop and Doctoral Consortium) 2009: 61-65 - [c35]Koby Crammer, Alex Kulesza, Mark Dredze:
Adaptive Regularization of Weight Vectors. NIPS 2009: 414-422 - [c34]Partha Pratim Talukdar, Koby Crammer:
New Regularized Algorithms for Transductive Learning. ECML/PKDD (2) 2009: 442-457 - [c33]Koby Crammer, Mehryar Mohri, Fernando Pereira:
Gaussian Margin Machines. AISTATS 2009: 105-112 - 2008
- [j10]Qian Liu, Koby Crammer, Fernando C. N. Pereira, David S. Roos:
Reranking candidate gene models with cross-species comparison for improved gene prediction. BMC Bioinform. 9 (2008) - [j9]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Multiple Sources. J. Mach. Learn. Res. 9: 1757-1774 (2008) - [j8]Partha Pratim Talukdar, Marie Jacob, Muhammad Salman Mehmood, Koby Crammer, Zachary G. Ives, Fernando C. N. Pereira, Sudipto Guha:
Learning to create data-integrating queries. Proc. VLDB Endow. 1(1): 785-796 (2008) - [c32]Koby Crammer:
Advanced Online Learning for Natural Language Processing. ACL (Tutorial Abstracts) 2008: 4 - [c31]Mark Dredze, Koby Crammer:
Active Learning with Confidence. ACL (2) 2008: 233-236 - [c30]Ron Bekkerman, Koby Crammer:
One-Class Clustering in the Text Domain. EMNLP 2008: 41-50 - [c29]Mark Dredze, Koby Crammer:
Online Methods for Multi-Domain Learning and Adaptation. EMNLP 2008: 689-697 - [c28]Koby Crammer, Partha Pratim Talukdar, Fernando C. N. Pereira:
A rate-distortion one-class model and its applications to clustering. ICML 2008: 184-191 - [c27]Mark Dredze, Koby Crammer, Fernando Pereira:
Confidence-weighted linear classification. ICML 2008: 264-271 - [c26]Koby Crammer, Mark Dredze, Fernando Pereira:
Exact Convex Confidence-Weighted Learning. NIPS 2008: 345-352 - 2007
- [j7]Axel Bernal, Koby Crammer, Artemis G. Hatzigeorgiou, Fernando Pereira:
Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction. PLoS Comput. Biol. 3(3) (2007) - [c25]Koby Crammer, Mark Dredze, Kuzman Ganchev, Partha Pratim Talukdar, Steven Carroll:
Automatic Code Assignment to Medical Text. BioNLP@ACL 2007: 129-136 - [c24]Koby Crammer:
A conservative aggressive subspace tracker. INTERSPEECH 2007: 498-501 - [c23]John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman:
Learning Bounds for Domain Adaptation. NIPS 2007: 129-136 - 2006
- [j6]Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer:
Online Passive-Aggressive Algorithms. J. Mach. Learn. Res. 7: 551-585 (2006) - [c22]Linli Xu, Koby Crammer, Dale Schuurmans:
Robust Support Vector Machine Training via Convex Outlier Ablation. AAAI 2006: 536-542 - [c21]Koby Crammer:
Online Tracking of Linear Subspaces. COLT 2006: 438-452 - [c20]Koby Crammer, Daniel D. Lee:
Room Impulse Response Estimation using Sparse Online Prediction and Absolute Loss. ICASSP (3) 2006: 748-751 - [c19]Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira:
Analysis of Representations for Domain Adaptation. NIPS 2006: 137-144 - [c18]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Multiple Sources. NIPS 2006: 321-328 - [c17]Koby Crammer, Amir Globerson:
Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006 - 2005
- [j5]Koby Crammer, Yoram Singer:
Online Ranking by Projecting. Neural Comput. 17(1): 145-175 (2005) - [c16]Ryan T. McDonald, Koby Crammer, Fernando C. N. Pereira:
Online Large-Margin Training of Dependency Parsers. ACL 2005: 91-98 - [c15]Koby Crammer, Yoram Singer:
Loss Bounds for Online Category Ranking. COLT 2005: 48-62 - [c14]Ryan T. McDonald, Koby Crammer, Fernando Pereira:
Flexible Text Segmentation with Structured Multilabel Classification. HLT/EMNLP 2005: 987-994 - [c13]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Data of Variable Quality. NIPS 2005: 219-226 - 2004
- [b1]Koby Crammer:
Online learning of complex categorical problems (למידה מקוונת של בעיות דירוג מורכבות.). Hebrew University of Jerusalem, Israel, 2004 - [c12]Koby Crammer, Gal Chechik:
A needle in a haystack: local one-class optimization. ICML 2004 - [c11]Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer:
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities. NIPS 2004: 1273-1280 - 2003
- [j4]Koby Crammer, Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems. J. Mach. Learn. Res. 3: 951-991 (2003) - [j3]Koby Crammer, Yoram Singer:
A Family of Additive Online Algorithms for Category Ranking. J. Mach. Learn. Res. 3: 1025-1058 (2003) - [c10]Koby Crammer, Yoram Singer:
Learning Algorithm for Enclosing Points in Bregmanian Spheres. COLT 2003: 388-402 - [c9]Koby Crammer, Jaz S. Kandola, Yoram Singer:
Online Classification on a Budget. NIPS 2003: 225-232 - [c8]Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer:
Online Passive-Aggressive Algorithms. NIPS 2003: 1229-1236 - 2002
- [j2]Koby Crammer, Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems. Mach. Learn. 47(2-3): 201-233 (2002) - [c7]Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Margin Analysis of the LVQ Algorithm. NIPS 2002: 462-469 - [c6]Koby Crammer, Joseph Keshet, Yoram Singer:
Kernel Design Using Boosting. NIPS 2002: 537-544 - [c5]Koby Crammer, Yoram Singer:
A new family of online algorithms for category ranking. SIGIR 2002: 151-158 - 2001
- [j1]Koby Crammer, Yoram Singer:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. J. Mach. Learn. Res. 2: 265-292 (2001) - [c4]Koby Crammer, Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems. COLT/EuroCOLT 2001: 99-115 - [c3]Koby Crammer, Yoram Singer:
Pranking with Ranking. NIPS 2001: 641-647 - 2000
- [c2]Koby Crammer, Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46 - [c1]Koby Crammer, Yoram Singer:
Improved Output Coding for Classification Using Continuous Relaxation. NIPS 2000: 437-443
Coauthor Index
Fernando Pereira 0003
aka: Fernando C. N. Pereira
aka: Fernando C. N. Pereira
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:18 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
