Automated Inductive Reasoning about Logic Programs. - Charles Elkan; David A. McAllester (ICLP/SLP, 1988)

Conspiracy Numbers and Caching for Searching And/Or Trees and Theorem-Proving. - Charles Elkan (IJCAI, 1989)

Logical Characterizations of Nonmonotonic TMSs. - Charles Elkan (MFCS, 1989)

A Decision Procedure for Conjunctive Query Disjointness. - Charles Elkan (PODS, 1989)

Incremental, Approximate Planning. - Charles Elkan (AAAI, 1990)

Independence of Logic Database Queries and Updates. - Charles Elkan (PODS, 1990)

A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems. - Charles Elkan (Artif. Intell., 1990)

Automated Inductive Reasoning about Logic Programs. - Charles Elkan (1990)

Measuring and Improving the Effectiveness of Representations. - Russell Greiner; Charles Elkan (IJCAI, 1991)

A Critical Look at Experimental Evaluations of EBL. - Alberto M. Segre; Charles Elkan; Alexander Russell (Mach. Learn., 1991)

Estimating the Accuracy of Learned Concepts. - Timothy L. Bailey; Charles Elkan (IJCAI, 1993)

D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. - Charles Elkan; Russell Greiner (Artif. Intell., 1993)

Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer. - Timothy L. Bailey; Charles Elkan (ISMB, 1994)

A High-Performance Explanation-Based Learning Algorithm. - Alberto M. Segre; Charles Elkan (Artif. Intell., 1994)

The Paradoxical Success of Fuzzy Logic. - Charles Elkan (IEEE Expert, 1994)

Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic. - Charles Elkan (IEEE Expert, 1994)

The Value of Prior Knowledge in Discovering Motifs with MEME. - Timothy L. Bailey; Charles Elkan (ISMB, 1995)

Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. - Timothy L. Bailey; Charles Elkan (Mach. Learn., 1995)

LPMEME: A Statistical Method for Inductive Logic Programming. - Karan Bhatia; Charles Elkan (AI, 1996)

Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic. - Charles Elkan (AI, 1996)

The Field Matching Problem: Algorithms and Applications. - Alvaro E. Monge; Charles Elkan (KDD, 1996)

Exploratory Analysis of Speedup Learning Data Using Epectation Maximization. - Alberto Maria Segre; Geoffrey J. Gordon; Charles Elkan (Artif. Intell., 1996)

ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool. - William Noble Grundy; Timothy L. Bailey; Charles Elkan (Comput. Appl. Biosci., 1996)

An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. - Alvaro E. Monge; Charles Elkan (DMKD, 1997)

Meta-MEME: motif-based hidden Markov models of protein families. - William Noble Grundy; Timothy L. Bailey; Charles Elkan; Michael E. Baker (Comput. Appl. Biosci., 1997)

MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences. - Timothy L. Bailey; Michael E. Baker; Charles Elkan; William Noble Grundy (Pattern Discovery in Biomolecular Data, 1999)

Results of the KDD'99 Classifier Learning. - Charles Elkan (SIGKDD Explor., 2000)

KDD'99 Knowledge Discovery Contest. - Charles Elkan (SIGKDD Explor., 2000)

Scalability for Clustering Algorithms Revisited. - Fredrik Farnstrom; James Lewis; Charles Elkan (SIGKDD Explor., 2000)

Bayesian approaches to failure prediction for disk drives. - Greg Hamerly; Charles Elkan (ICML, 2001)

Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. - Bianca Zadrozny; Charles Elkan (ICML, 2001)

The Foundations of Cost-Sensitive Learning. - Charles Elkan (IJCAI, 2001)

Magical thinking in data mining: lessons from CoIL challenge 2000. - Charles Elkan (KDD, 2001)

Shared challenges in data mining and computational biology (abstract of invited talk). - Charles Elkan (BIOKDD, 2001)

Learning and making decisions when costs and probabilities are both unknown. - Bianca Zadrozny; Charles Elkan (KDD, 2001)

Paradoxes of fuzzy logic, revisited. - Charles Elkan (Int. J. Approx. Reason., 2001)

Alternatives to the k-means algorithm that find better clusterings. - Greg Hamerly; Charles Elkan (CIKM, 2002)

Transforming classifier scores into accurate multiclass probability estimates. - Bianca Zadrozny; Charles Elkan (KDD, 2002)

Improved disk-drive failure warnings. - Gordon F. Hughes; Joseph F. Murray; Kenneth Kreutz-Delgado; Charles Elkan (IEEE Trans. Reliab., 2002)

Learning Rules to Improve a Machine Translation System. - David Kauchak; Charles Elkan (ECML, 2003)

Using the Triangle Inequality to Accelerate k-Means. - Charles Elkan (ICML, 2003)

Principled Methods for Advising Reinforcement Learning Agents. - Eric Wiewiora; Garrison W. Cottrell; Charles Elkan (ICML, 2003)

Learning the k in k-means. - Greg Hamerly; Charles Elkan (NIPS, 2003)

A Bayesian network framework for reject inference. - Andrew T. Smith 001; Charles Elkan (KDD, 2004)

Sources of Success for Boosted Wrapper Induction. - David Kauchak; Joseph Smarr; Charles Elkan (J. Mach. Learn. Res., 2004)

Modeling word burstiness using the Dirichlet distribution. - Rasmus Elsborg Madsen; David Kauchak; Charles Elkan (ICML, 2005)

Deriving TF-IDF as a Fisher Kernel. - Charles Elkan (SPIRE, 2005)

Fast Recognition of Musical Genres Using RBF Networks. - Douglas Turnbull; Charles Elkan (IEEE Trans. Knowl. Data Eng., 2005)

Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. - Charles Elkan (ICML, 2006)

Making generative classifiers robust to selection bias. - Andrew T. Smith 001; Charles Elkan (KDD, 2007)

Finding Transport Proteins in a General Protein Database. - Sanmay Das; Milton H. Saier Jr.; Charles Elkan (PKDD, 2007)

KDD Cup and workshop 2007. - James Bennett; Charles Elkan; Bing Liu 001; Padhraic Smyth; Domonkos Tikk (SIGKDD Explor., 2007)

Learning to Find Relevant Biological Articles without Negative Training Examples. - Keith Noto; Milton H. Saier Jr.; Charles Elkan (Australasian Conference on Artificial Intelligence, 2008)

Learning a two-stage SVM/CRF sequence classifier. - Guilherme Hoefel; Charles Elkan (CIKM, 2008)

Learning classifiers from only positive and unlabeled data. - Charles Elkan; Keith Noto (KDD, 2008)

Accounting for burstiness in topic models. - Gabriel Doyle; Charles Elkan (ICML, 2009)

The Transporter Classification Database: recent advances. - Milton H. Saier Jr.; Ming Ren Yen; Keith Noto; Dorjee G. Tamang; Charles Elkan (Nucleic Acids Res., 2009)

Conditional Random Fields for Word Hyphenation. - Nikolaos Trogkanis; Charles Elkan (ACL, 2010)

A Log-Linear Model with Latent Features for Dyadic Prediction. - Aditya Krishna Menon; Charles Elkan (ICDM, 2010)

Preserving Privacy in Data Mining via Importance Weighting. - Charles Elkan (PSDML, 2010)

Learning gene regulatory networks from only positive and unlabeled data. - Luigi Cerulo; Charles Elkan; Michele Ceccarelli (BMC Bioinform., 2010)

Technical perspective - Creativity helps influence prediction precision. - Padhraic Smyth; Charles Elkan (Commun. ACM, 2010)

Dyadic Prediction Using a Latent Feature Log-Linear Model - Aditya Krishna Menon; Charles Elkan (CoRR, 2010)

Predicting labels for dyadic data. - Aditya Krishna Menon; Charles Elkan (Data Min. Knowl. Discov., 2010)

Quadratic Programming Feature Selection. - Irene Rodríguez-Luján; Ramón Huerta; Charles Elkan; Carlos Santa Cruz (J. Mach. Learn. Res., 2010)

Latent semantic indexing (LSI) fails for TREC collections. - Avinash Atreya; Charles Elkan (SIGKDD Explor., 2010)

Reinforcement Learning with a Bilinear Q Function. - Charles Elkan (EWRL, 2011)

Link Prediction via Matrix Factorization. - Aditya Krishna Menon; Charles Elkan (ECML/PKDD (2), 2011)

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text. - 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; et al. (BMC Bioinform., 2011)

Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning. - Aditya Kumar Sehgal; Sanmay Das; Keith Noto; Milton H. Saier Jr.; Charles Elkan (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data. - Wenkai Li; Qinghua Guo 002; Charles Elkan (IEEE Trans. Geosci. Remote. Sens., 2011)

Fast Algorithms for Approximating the Singular Value Decomposition. - Aditya Krishna Menon; Charles Elkan (ACM Trans. Knowl. Discov. Data, 2011)

Predicting accurate probabilities with a ranking loss. - Aditya Krishna Menon; Xiaoqian Jiang; Shankar Vembu; Charles Elkan; Lucila Ohno-Machado (ICML, 2012)

Learning and Inference in Probabilistic Classifier Chains with Beam Search. - Abhishek Kumar 004; Shankar Vembu; Aditya Krishna Menon; Charles Elkan (ECML/PKDD (1), 2012)

Policy Iteration Based on a Learned Transition Model. - Vivek Ramavajjala; Charles Elkan (ECML/PKDD (2), 2012)

Inhibition in Multiclass Classification. - Ramón Huerta; Shankar Vembu; José M. Amigó; Thomas Nowotny; Charles Elkan (Neural Comput., 2012)

Guest Editorial for Special Issue KDD'10. - Charles Elkan; Yehuda Koren (ACM Trans. Knowl. Discov. Data, 2012)

Nonlinear support vector machines can systematically identify stocks with high and low future returns. - Ramón Huerta; Fernando J. Corbacho; Charles Elkan (Algorithmic Finance, 2013)

Differential privacy based on importance weighting. - Zhanglong Ji; Charles Elkan (Mach. Learn., 2013)

Beam search algorithms for multilabel learning. - Abhishek Kumar 004; Shankar Vembu; Aditya Krishna Menon; Charles Elkan (Mach. Learn., 2013)

Nowcasting with Numerous Candidate Predictors. - Brendan Andrew Duncan; Charles Elkan (ECML/PKDD (1), 2014)

Optimal Thresholding of Classifiers to Maximize F1 Measure. - Zachary Chase Lipton; Charles Elkan; Balakrishnan Narayanaswamy (ECML/PKDD (2), 2014)

Learning to Re-rank for Interactive Problem Resolution and Query Refinement. - Rashmi Gangadharaiah; Balakrishnan Narayanaswamy; Charles Elkan (SIGDIAL Conference, 2014)

Differential Privacy and Machine Learning: a Survey and Review. - Zhanglong Ji; Zachary Chase Lipton; Charles Elkan (CoRR, 2014)

F1-Optimal Thresholding in the Multi-Label Setting. - Zachary Chase Lipton; Charles Elkan; Balakrishnan Narayanaswamy (CoRR, 2014)

Probabilistic Modeling of a Sales Funnel to Prioritize Leads. - Brendan Andrew Duncan; Charles Peter Elkan (KDD, 2015)

Theory versus practice in data science. - Charles Elkan (ICSC, 2015)

Efficient Elastic Net Regularization for Sparse Linear Models. - Zachary Chase Lipton; Charles Elkan (CoRR, 2015)

Learning to Diagnose with LSTM Recurrent Neural Networks. - Zachary Chase Lipton; David C. Kale; Charles Elkan; Randall C. Wetzel (ICLR (Poster), 2016)

Predicting Surgery Duration with Neural Heteroscedastic Regression. - Nathan H. Ng; Rodney A. Gabriel; Julian J. McAuley; Charles Elkan; Zachary C. Lipton (MLHC, 2017)

Visualizing the Consequences of Evidence in Bayesian Networks. - Clifford Champion; Charles Elkan (CoRR, 2017)

End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient. - Li Zhou 006; Kevin Small; Oleg Rokhlenko; Charles Elkan (CoRR, 2017)

What we need to learn if we want to do and not just talk. - Rashmi Gangadharaiah; Balakrishnan Narayanaswamy; Charles Elkan (NAACL-HLT (3), 2018)

Achieving Fluency and Coherency in Task-oriented Dialog. - Rashmi Gangadharaiah; Balakrishnan Narayanaswamy; Charles Elkan (CoRR, 2018)

A Modified Logistic Regression for Positive and Unlabeled Learning. - Kristen Jaskie; Charles Elkan; Andreas Spanias (ACSSC, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

One-Class Remote Sensing Classification From Positive and Unlabeled Background Data. - Wenkai Li; Qinghua Guo 002; Charles Elkan (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021)

Gamified crowd-sourcing of high-quality data for visual fine-tuning. - Shashank Yadav; Rohan Tomar; Garvit Jain; Chirag Ahooja; Shubham Chaudhary 008; Charles Elkan (CoRR, 2024)