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ORCIDCharles Elkan
Charles Peter Elkan
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- affiliation: University of California, San Diego, USA
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2020 – today
- 2021
[j34]Wenkai Li
, Qinghua Guo, Charles Elkan:
One-Class Remote Sensing Classification From Positive and Unlabeled Background Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 730-746 (2021)
2010 – 2019
- 2019
- [c54]Kristen Jaskie, Charles Elkan, Andreas Spanias:
A Modified Logistic Regression for Positive and Unlabeled Learning. ACSSC 2019: 2007-2011 - [i10]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - 2018
- [c53]Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, Charles Elkan:
What we need to learn if we want to do and not just talk. NAACL-HLT (3) 2018: 25-32 - [i9]Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, Charles Elkan:
Achieving Fluency and Coherency in Task-oriented Dialog. CoRR abs/1804.03799 (2018) - 2017
- [c52]Nathan H. Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. MLHC 2017: 100-111 - [i8]Nathan Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. CoRR abs/1702.05386 (2017) - [i7]Clifford Champion, Charles Elkan:
Visualizing the Consequences of Evidence in Bayesian Networks. CoRR abs/1707.00791 (2017) - [i6]Li Zhou, Kevin Small, Oleg Rokhlenko, Charles Elkan:
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient. CoRR abs/1712.02838 (2017) - 2016
- [c51]Zachary Chase Lipton, David C. Kale, Charles Elkan, Randall C. Wetzel:
Learning to Diagnose with LSTM Recurrent Neural Networks. ICLR (Poster) 2016 - 2015
- [c50]Brendan Andrew Duncan, Charles Peter Elkan:
Probabilistic Modeling of a Sales Funnel to Prioritize Leads. KDD 2015: 1751-1758 - [c49]Charles Elkan:
Theory versus practice in data science. ICSC 2015: xix - [i5]Zachary Chase Lipton, Charles Elkan:
Efficient Elastic Net Regularization for Sparse Linear Models. CoRR abs/1505.06449 (2015) - 2014
- [c48]Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy:
Optimal Thresholding of Classifiers to Maximize F1 Measure. ECML/PKDD (2) 2014: 225-239 - [c47]Brendan Andrew Duncan, Charles Elkan:
Nowcasting with Numerous Candidate Predictors. ECML/PKDD (1) 2014: 370-385 - [c46]Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, Charles Elkan:
Learning to Re-rank for Interactive Problem Resolution and Query Refinement. SIGDIAL Conference 2014: 218-227 - [i4]Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy:
F1-Optimal Thresholding in the Multi-Label Setting. CoRR abs/1402.1892 (2014) - [i3]Zhanglong Ji, Zachary Chase Lipton, Charles Elkan:
Differential Privacy and Machine Learning: a Survey and Review. CoRR abs/1412.7584 (2014) - 2013
- [j33]Ramón Huerta, Fernando J. Corbacho, Charles Elkan:
Nonlinear support vector machines can systematically identify stocks with high and low future returns. Algorithmic Finance 2(1): 45-58 (2013) - [j32]Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Beam search algorithms for multilabel learning. Mach. Learn. 92(1): 65-89 (2013) - [j31]Zhanglong Ji, Charles Elkan:
Differential privacy based on importance weighting. Mach. Learn. 93(1): 163-183 (2013) - 2012
- [j30]Ramón Huerta, Shankar Vembu, José M. Amigó, Thomas Nowotny, Charles Elkan:
Inhibition in Multiclass Classification. Neural Comput. 24(9): 2473-2507 (2012) - [j29]Charles Elkan, Yehuda Koren:
Guest Editorial for Special Issue KDD'10. ACM Trans. Knowl. Discov. Data 5(4): 18:1-18:2 (2012) - [c45]Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado:
Predicting accurate probabilities with a ranking loss. ICML 2012 - [c44]Vivek Ramavajjala, Charles Elkan:
Policy Iteration Based on a Learned Transition Model. ECML/PKDD (2) 2012: 211-226 - [c43]Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Learning and Inference in Probabilistic Classifier Chains with Beam Search. ECML/PKDD (1) 2012: 665-680 - [i2]Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado:
Predicting accurate probabilities with a ranking loss. CoRR abs/1206.4661 (2012) - 2011
- [j28]Martin Krallinger, Miguel Vazquez, Florian Leitner, David Salgado, Andrew Chatr-aryamontri, Andrew G. Winter, Livia Perfetto, Leonardo Briganti, Luana Licata, Marta Iannuccelli, Luisa Castagnoli, Gianni Cesareni, Mike Tyers, Gerold Schneider, Fabio Rinaldi, Robert Leaman, Graciela Gonzalez, Sérgio Matos, Sun Kim, W. John Wilbur, Luis M. Rocha, Hagit Shatkay, Ashish V. Tendulkar, Shashank Agarwal, Feifan Liu, Xinglong Wang, Rafal Rak, Keith Noto, Charles Elkan, Zhiyong Lu:
The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text. BMC Bioinform. 12(S-8): S3 (2011) - [j27]Aditya Kumar Sehgal, Sanmay Das, Keith Noto, Milton H. Saier Jr., Charles Elkan:
Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 851-857 (2011) - [j26]Wenkai Li, Qinghua Guo, Charles Elkan:
A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data. IEEE Trans. Geosci. Remote. Sens. 49(2): 717-725 (2011) - [j25]Aditya Krishna Menon, Charles Elkan:
Fast Algorithms for Approximating the Singular Value Decomposition. ACM Trans. Knowl. Discov. Data 5(2): 13:1-13:36 (2011) - [c42]Charles Elkan:
Reinforcement Learning with a Bilinear Q Function. EWRL 2011: 78-88 - [c41]Aditya Krishna Menon, Charles Elkan:
Link Prediction via Matrix Factorization. ECML/PKDD (2) 2011: 437-452 - 2010
- [j24]Luigi Cerulo, Charles Elkan, Michele Ceccarelli:
Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinform. 11: 228 (2010) - [j23]Padhraic Smyth, Charles Elkan:
Technical perspective - Creativity helps influence prediction precision. Commun. ACM 53(4): 88 (2010) - [j22]Aditya Krishna Menon, Charles Elkan:
Predicting labels for dyadic data. Data Min. Knowl. Discov. 21(2): 327-343 (2010) - [j21]Irene Rodríguez-Luján, Ramón Huerta, Charles Elkan, Carlos Santa Cruz:
Quadratic Programming Feature Selection. J. Mach. Learn. Res. 11: 1491-1516 (2010) - [j20]Avinash Atreya, Charles Elkan:
Latent semantic indexing (LSI) fails for TREC collections. SIGKDD Explor. 12(2): 5-10 (2010) - [c40]Nikolaos Trogkanis, Charles Elkan:
Conditional Random Fields for Word Hyphenation. ACL 2010: 366-374 - [c39]Aditya Krishna Menon, Charles Elkan:
A Log-Linear Model with Latent Features for Dyadic Prediction. ICDM 2010: 364-373 - [c38]Charles Elkan:
Preserving Privacy in Data Mining via Importance Weighting. PSDML 2010: 15-21 - [i1]Aditya Krishna Menon, Charles Elkan:
Dyadic Prediction Using a Latent Feature Log-Linear Model. CoRR abs/1006.2156 (2010)
2000 – 2009
- 2009
- [j19]Milton H. Saier Jr., Ming Ren Yen, Keith Noto, Dorjee G. Tamang, Charles Elkan:
The Transporter Classification Database: recent advances. Nucleic Acids Res. 37(Database-Issue): 274-278 (2009) - [c37]Gabriel Doyle, Charles Elkan:
Accounting for burstiness in topic models. ICML 2009: 281-288 - 2008
- [c36]Keith Noto, Milton H. Saier Jr., Charles Elkan:
Learning to Find Relevant Biological Articles without Negative Training Examples. Australasian Conference on Artificial Intelligence 2008: 202-213 - [c35]Guilherme Hoefel, Charles Elkan:
Learning a two-stage SVM/CRF sequence classifier. CIKM 2008: 271-278 - [c34]Charles Elkan, Keith Noto:
Learning classifiers from only positive and unlabeled data. KDD 2008: 213-220 - 2007
- [j18]James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk:
KDD Cup and workshop 2007. SIGKDD Explor. 9(2): 51-52 (2007) - [c33]Andrew T. Smith, Charles Elkan:
Making generative classifiers robust to selection bias. KDD 2007: 657-666 - [c32]Sanmay Das, Milton H. Saier Jr., Charles Elkan:
Finding Transport Proteins in a General Protein Database. PKDD 2007: 54-66 - 2006
- [c31]Charles Elkan:
Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. ICML 2006: 289-296 - 2005
- [j17]Douglas Turnbull, Charles Elkan:
Fast Recognition of Musical Genres Using RBF Networks. IEEE Trans. Knowl. Data Eng. 17(4): 580-584 (2005) - [c30]Rasmus Elsborg Madsen, David Kauchak, Charles Elkan:
Modeling word burstiness using the Dirichlet distribution. ICML 2005: 545-552 - [c29]Charles Elkan:
Deriving TF-IDF as a Fisher Kernel. SPIRE 2005: 295-300 - 2004
- [j16]David Kauchak, Joseph Smarr, Charles Elkan:
Sources of Success for Boosted Wrapper Induction. J. Mach. Learn. Res. 5: 499-527 (2004) - [c28]Andrew T. Smith, Charles Elkan:
A Bayesian network framework for reject inference. KDD 2004: 286-295 - 2003
- [c27]David Kauchak, Charles Elkan:
Learning Rules to Improve a Machine Translation System. ECML 2003: 205-216 - [c26]Charles Elkan:
Using the Triangle Inequality to Accelerate k-Means. ICML 2003: 147-153 - [c25]Eric Wiewiora, Garrison W. Cottrell, Charles Elkan:
Principled Methods for Advising Reinforcement Learning Agents. ICML 2003: 792-799 - [c24]Greg Hamerly, Charles Elkan:
Learning the k in k-means. NIPS 2003: 281-288 - 2002
- [j15]Gordon F. Hughes, Joseph F. Murray, Kenneth Kreutz-Delgado, Charles Elkan:
Improved disk-drive failure warnings. IEEE Trans. Reliab. 51(3): 350-357 (2002) - [c23]Greg Hamerly, Charles Elkan:
Alternatives to the k-means algorithm that find better clusterings. CIKM 2002: 600-607 - [c22]Bianca Zadrozny, Charles Elkan:
Transforming classifier scores into accurate multiclass probability estimates. KDD 2002: 694-699 - 2001
- [j14]Charles Elkan:
Paradoxes of fuzzy logic, revisited. Int. J. Approx. Reason. 26(2): 153-155 (2001) - [c21]Greg Hamerly, Charles Elkan:
Bayesian approaches to failure prediction for disk drives. ICML 2001: 202-209 - [c20]Bianca Zadrozny, Charles Elkan:
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. ICML 2001: 609-616 - [c19]Charles Elkan:
The Foundations of Cost-Sensitive Learning. IJCAI 2001: 973-978 - [c18]Charles Elkan:
Shared challenges in data mining and computational biology (abstract of invited talk). BIOKDD 2001: 44 - [c17]Bianca Zadrozny, Charles Elkan:
Learning and making decisions when costs and probabilities are both unknown. KDD 2001: 204-213 - [c16]Charles Elkan:
Magical thinking in data mining: lessons from CoIL challenge 2000. KDD 2001: 426-431 - 2000
- [j13]Charles Elkan:
Results of the KDD'99 Classifier Learning. SIGKDD Explor. 1(2): 63-64 (2000) - [j12]Charles Elkan:
KDD'99 Knowledge Discovery Contest. SIGKDD Explor. 1(2): 78 (2000) - [j11]Fredrik Farnstrom, James Lewis, Charles Elkan:
Scalability for Clustering Algorithms Revisited. SIGKDD Explor. 2(1): 51-57 (2000)
1990 – 1999
- 1999
- [p1]Timothy L. Bailey, Michael E. Baker, Charles Elkan, William Noble Grundy:
MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences. Pattern Discovery in Biomolecular Data 1999: 30-54 - 1997
- [j10]William Noble Grundy, Timothy L. Bailey, Charles Elkan, Michael E. Baker:
Meta-MEME: motif-based hidden Markov models of protein families. Comput. Appl. Biosci. 13(4): 397-406 (1997) - [c15]Alvaro E. Monge, Charles Elkan:
An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. DMKD 1997 - 1996
- [j9]Alberto Maria Segre, Geoffrey J. Gordon, Charles Elkan:
Exploratory Analysis of Speedup Learning Data Using Epectation Maximization. Artif. Intell. 85(1-2): 301-319 (1996) - [j8]William Noble Grundy, Timothy L. Bailey, Charles Elkan:
ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool. Comput. Appl. Biosci. 12(4): 303-310 (1996) - [c14]Charles Elkan:
Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic. AI 1996: 54-68 - [c13]Karan Bhatia, Charles Elkan:
LPMEME: A Statistical Method for Inductive Logic Programming. AI 1996: 227-239 - [c12]Alvaro E. Monge, Charles Elkan:
The Field Matching Problem: Algorithms and Applications. KDD 1996: 267-270 - 1995
- [j7]Timothy L. Bailey, Charles Elkan:
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Mach. Learn. 21(1-2): 51-80 (1995) - [c11]Timothy L. Bailey, Charles Elkan:
The Value of Prior Knowledge in Discovering Motifs with MEME. ISMB 1995: 21-29 - 1994
- [j6]Alberto M. Segre, Charles Elkan:
A High-Performance Explanation-Based Learning Algorithm. Artif. Intell. 69(1-2): 1-50 (1994) - [j5]Charles Elkan:
The Paradoxical Success of Fuzzy Logic. IEEE Expert 9(4): 3-8 (1994) - [j4]Charles Elkan:
Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic. IEEE Expert 9(4): 47-49 (1994) - [c10]Timothy L. Bailey, Charles Elkan:
Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer. ISMB 1994: 28-36 - 1993
- [j3]Charles Elkan, Russell Greiner:
D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Artif. Intell. 61(1): 41-52 (1993) - [c9]Charles Elkan:
The Paradoxical Success of Fuzzy Logic. AAAI 1993: 698-703 - [c8]Timothy L. Bailey, Charles Elkan:
Estimating the Accuracy of Learned Concepts. IJCAI 1993: 895-901 - 1991
- [j2]Alberto M. Segre, Charles Elkan, Alexander Russell:
A Critical Look at Experimental Evaluations of EBL. Mach. Learn. 6: 183-195 (1991) - [c7]Russell Greiner, Charles Elkan:
Measuring and Improving the Effectiveness of Representations. IJCAI 1991: 518-524 - 1990
- [b1]Charles Elkan:
Automated Inductive Reasoning about Logic Programs. Cornell University, USA, 1990 - [j1]Charles Elkan:
A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems. Artif. Intell. 43(2): 219-234 (1990) - [c6]Charles Elkan:
Incremental, Approximate Planning. AAAI 1990: 145-150 - [c5]Charles Elkan:
Independence of Logic Database Queries and Updates. PODS 1990: 154-160
1980 – 1989
- 1989
- [c4]Charles Elkan:
Conspiracy Numbers and Caching for Searching And/Or Trees and Theorem-Proving. IJCAI 1989: 341-348 - [c3]Charles Elkan:
Logical Characterizations of Nonmonotonic TMSs. MFCS 1989: 218-224 - [c2]Charles Elkan:
A Decision Procedure for Conjunctive Query Disjointness. PODS 1989: 134-139 - 1988
- [c1]Charles Elkan, David A. McAllester:
Automated Inductive Reasoning about Logic Programs. ICLP/SLP 1988: 876-892
Coauthor Index
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