Charles Elkan

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Reply to Comments on The Paradoxical Success of Fuzzy Logic

Charles Elkan, C. Elkan, 1997 .

Independence of logic database queries and update

Charles Elkan, C. Elkan, 1990, PODS '90.

A critical look at experimental evaluations of EBL

Alexander Russell, Charles Elkan, Alberto Segre, 1991, Mach. Learn..

Learning Rules to Improve a Machine Translation System

David Kauchak, Charles Elkan, C. Elkan, 2003, ECML.

Shared challenges in data mining and computational biology (abstract of invited talk)

Charles Elkan, C. Elkan, 2001, BIOKDD.

Measuring and Improving the Effectiveness of Representations

Russell Greiner, Charles Elkan, C. Elkan, 1991, IJCAI.

SysML: The New Frontier of Machine Learning Systems pdf

Gustavo Alonso, Dan Alistarh, Dimitris S. Papailiopoulos, 2019, ArXiv.

Scalability for clustering algorithms revisited

Charles Elkan, James Lewis, Fredrik Farnstrom, 2000, SKDD.

Automated Inductive Reasoning about Logic Programs

Charles Elkan, David A. McAllester, C. Elkan, 1988, ICLP/SLP.

Learning and making decisions when costs and probabilities are both unknown

Bianca Zadrozny, Charles Elkan, C. Elkan, 2001, KDD '01.

Sources of Success for Boosted Wrapper Induction

David Kauchak, Charles Elkan, Joseph Smarr, 2004, J. Mach. Learn. Res..

D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project

Russell Greiner, Charles Elkan, C. Elkan, 1993, Artif. Intell..

Conspiracy Numbers and Caching for Searching And/Or Trees and Theorem-Proving

Charles Elkan, C. Elkan, 1989, IJCAI.

Beam search algorithms for multilabel learning

Charles Elkan, Shankar Vembu, Aditya Krishna Menon, 2013, Machine Learning.

Cost-Sensitive Learning and Decision-Making When Costs Are Unknown

Charles Elkan, C. Elkan, 2000 .

Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers

Bianca Zadrozny, Charles Elkan, C. Elkan, 2001, ICML.

The Foundations of Cost-Sensitive Learning

Charles Elkan, C. Elkan, 2001, IJCAI.

Latent semantic indexing (LSI) fails for TREC collections

Charles Elkan, Avinash Atreya, C. Elkan, 2011, SKDD.

Contributions to research on machine translation

David Kauchak, Charles Elkan, 2006 .

Finding Transport Proteins in a General Protein Database

Sanmay Das, Charles Elkan, Milton H. Saier, 2007, PKDD.

A bayesian approach to motif-based protein modeling

William Noble Grundy, Charles Elkan, C. Elkan, 1998 .

A Log-Linear Model with Latent Features for Dyadic Prediction

Charles Elkan, Aditya Krishna Menon, C. Elkan, 2010, 2010 IEEE International Conference on Data Mining.

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

Zhiyong Lu, Fabio Rinaldi, Gerold Schneider, 2011, BMC Bioinformatics.

Fast recognition of musical genres using RBF networks

Charles Elkan, Douglas Turnbull, 2005, IEEE Transactions on Knowledge and Data Engineering.

Categorization-based diagnostic problem solving in the VLSI design domain

Charles Elkan, A. Hekmatpour, C. Elkan, 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

A multimedia expert system for wafer polisher maintenance

Charles Elkan, A. Hekmatpour, 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

Efficient Elastic Net Regularization for Sparse Linear Models pdf

Charles Elkan, Zachary Chase Lipton, 2015, ArXiv.

Guest Editorial for Special Issue KDD’10

Charles Elkan, Yehuda Koren, 2012, TKDD.

Playing the Imitation Game with deep learning

Charles Elkan, Zachary C. Lipton, 2016, IEEE Spectrum.

A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data

Wenkai Li, Charles Elkan, Qinghua Guo, 2011, IEEE Transactions on Geoscience and Remote Sensing.

What we need to learn if we want to do and not just talk

Charles Elkan, Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, 2018, NAACL-HLT.

Learning to Diagnose with LSTM Recurrent Neural Networks pdf

Charles Elkan, David C. Kale, Randall C. Wetzel, 2015, ICLR.

Learning the k in k-means

Greg Hamerly, Charles Elkan, C. Elkan, 2003, NIPS.

A High-Performance Explanation-Based Learning Algorithm

Charles Elkan, Alberto Maria Segre, C. Elkan, 1994, Artif. Intell..

Theory versus practice in data science

Charles Elkan, 2015, ICSC.

A Bayesian network framework for reject inference

Charles Elkan, Andrew T. Smith, C. Elkan, 2004, KDD.

Lessons learned from contests in data mining

Tie-Yan Liu, Charles Elkan, Yehuda Koren, 2011, KDD 2011.

Learning to Find Relevant Biological Articles without Negative Training Examples

Charles Elkan, Milton H. Saier, Keith Noto, 2008, Australasian Conference on Artificial Intelligence.

Nowcasting with Numerous Candidate Predictors

Charles Elkan, Brendan Duncan, C. Elkan, 2014, ECML/PKDD.

Predicting Surgery Duration with Neural Heteroscedastic Regression pdf

Charles Elkan, Julian J. McAuley, Nathan Ng, 2017, MLHC.

Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution

Charles Elkan, C. Elkan, 2006, ICML.

Dyadic Prediction Using a Latent Feature Log-Linear Model pdf

Charles Elkan, Aditya Krishna Menon, C. Elkan, 2010, ArXiv.

A Pipeline to Automate the Updating of a Specialized Protein Database

Sanmay Das, Charles Elkan, 2007 .

Not the Last Word on EBL Algorithms

Alberto M. Segre, Charles Elkan, Alberto Maria Segre, 1989 .

Proceedings of the Sixth SIAM International Conference on Data Mining: Preface

Zoran Obradovic, Philip S. Yu, Johannes Gehrke, 2006 .

Discovering motifs in dna and protein sequences: the approximate common substring problem

Charles Elkan, Timothy L. Bailey, C. Elkan, 1995 .

Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic

Charles Elkan, C. Elkan, 1996, Canadian Conference on AI.

Nonlinear Support Vector Machines Can Systematically Identify Stocks with High and Low Future Returns

Fernando J. Corbacho, Charles Elkan, Ramón Huerta, 2012, Algorithmic Finance.

Fast Algorithms for Approximating the Singular Value Decomposition

Charles Elkan, Aditya Krishna Menon, C. Elkan, 2011, TKDD.

Notes on Machine Learning Projects and Reports

Charles Elkan, 2010 .

Principled Methods for Advising Reinforcement Learning Agents

Garrison W. Cottrell, Charles Elkan, Eric Wiewiora, 2003, ICML.

Deriving TF-IDF as a Fisher Kernel

Charles Elkan, C. Elkan, 2005, SPIRE.

Alternatives to the k-means algorithm that find better clusterings

Greg Hamerly, Charles Elkan, 2002, CIKM '02.

KDD'99 Knowledge Discovery Contest

Charles Elkan, 2000, SIGKDD Explor..

Reasoning about Action in First-Order Logic

Charles Elkan, C. Elkan, 1992 .

KDD Cup and workshop 2007

Padhraic Smyth, Charles Elkan, Bing Liu, 2007, SKDD.

Achieving Fluency and Coherency in Task-oriented Dialog pdf

Charles Elkan, Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, 2018, ArXiv.

Improved disk-drive failure warnings

Joseph F. Murray, Charles Elkan, Kenneth Kreutz-Delgado, 2002, IEEE Trans. Reliab..

Bayesian approaches to failure prediction for disk drives

Greg Hamerly, Charles Elkan, C. Elkan, 2001, ICML.

Technical perspectiveCreativity helps influence prediction precision

Padhraic Smyth, Charles Elkan, 2010, CACM.

LPMEME: A Statistical Method for Inductive Logic Programming

Charles Elkan, Karan Bhatia, 1996, Canadian Conference on AI.

Optimal Thresholding of Classifiers to Maximize F1 Measure

Charles Elkan, Balakrishnan Narayanaswamy, Zachary Chase Lipton, 2014, ECML/PKDD.

Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning

Sanmay Das, Charles Elkan, Milton H. Saier, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient pdf

Li Zhou, Charles Elkan, Oleg Rokhlenko, 2017, ArXiv.

A decision procedure for conjunctive query disjointness

Charles Elkan, 1989, PODS '89.

Modeling word burstiness using the Dirichlet distribution

David Kauchak, Charles Elkan, Rasmus Elsborg Madsen, 2005, ICML.

Incremental, Approximate Planning

Charles Elkan, C. Elkan, 1990, AAAI.

Predicting accurate probabilities with a ranking loss

Xiaoqian Jiang, Lucila Ohno-Machado, Charles Elkan, 2012, ICML.

Results of the KDD'99 classifier learning

Charles Elkan, C. Elkan, 2000, SKDD.

Reinforcement Learning with a Bilinear Q Function

Charles Elkan, C. Elkan, 2011, EWRL.

Magical thinking in data mining: lessons from CoIL challenge 2000

Charles Elkan, C. Elkan, 2001, KDD '01.

Inhibition in Multiclass Classification

Charles Elkan, Shankar Vembu, José María Amigó, 2012, Neural Computation.

Visualizing the Consequences of Evidence in Bayesian Networks pdf

Charles Elkan, Clifford Champion, C. Elkan, 2017, ArXiv.

Learning and Inference in Probabilistic Classifier Chains with Beam Search

Charles Elkan, Shankar Vembu, Aditya Krishna Menon, 2012, ECML/PKDD.

Differential Privacy and Machine Learning: a Survey and Review pdf

Charles Elkan, Zhanglong Ji, Zachary Chase Lipton, 2014, ArXiv.

Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer

Charles Elkan, Timothy L. Bailey, C. Elkan, 1994, ISMB.

Electrical Engineering (Intelligent Systems, Robotics, and Control)

Terrence J. Sejnowski, Nuno Vasconcelos, Charles Elkan, 2006 .

KDD Cup and Workshop 2007

Padhraic Smyth, Charles Elkan, Bing Liu, 2007, KDD '07.

Link Prediction via Matrix Factorization

Charles Elkan, Aditya Krishna Menon, C. Elkan, 2011, ECML/PKDD.

Learning classifiers from only positive and unlabeled data

Charles Elkan, Keith Noto, C. Elkan, 2008, KDD.

On Valid and Invalid Methodologies for Experimentala Evaluations of EBL

Alberto M. Segre, Alexander Russell, Charles Elkan, 1990 .

Computer Science and Cognitive Science

Charles Elkan, Serge J. Belongie, Hector Jasso, 2007 .

A Modified Logistic Regression for Positive and Unlabeled Learning

Charles Elkan, Andreas Spanias, Kristen Jaskie, 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

Accounting for burstiness in topic models

Charles Elkan, Gabriel Doyle, C. Elkan, 2009, ICML '09.

Sources of Success for Information Extraction Methods

David Kauchak, Charles Elkan, Joseph Smarr, 2001 .

The Field Matching Problem: Algorithms and Applications

Charles Elkan, Alvaro E. Monge, C. Elkan, 1996, KDD.

Exploratory Analysis of Speedup Learning Data Using Epectation Maximization

Charles Elkan, Geoffrey J. Gordon, Alberto Maria Segre, 1996, Artif. Intell..

Expectation Maximization Algorithm

Charles Elkan, C. Elkan, 2010, Encyclopedia of Machine Learning.

Transforming classifier scores into accurate multiclass probability estimates

Bianca Zadrozny, Charles Elkan, C. Elkan, 2002, KDD.

Paradoxes of fuzzy logic, revisited

Charles Elkan, 2001, Int. J. Approx. Reason..

Web-scale information retrieval and data mining List of papers

Charles Elkan, C. Elkan, 2008 .

ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool

William Noble Grundy, Charles Elkan, Timothy L. Bailey, 1996, Comput. Appl. Biosci..

The paradoxical success of fuzzy logic

Charles Elkan, C. Elkan, 1993, IEEE Expert.

Preserving Privacy in Data Mining via Importance Weighting

Charles Elkan, C. Elkan, 2010, PSDML.

Differential privacy based on importance weighting

Charles Elkan, Zhanglong Ji, 2013, Machine Learning.

Conditional Random Fields for Word Hyphenation

Charles Elkan, Nikolaos Trogkanis, 2010, ACL.

Unsupervised learning of multiple motifs in biopolymers using expectation maximization

Charles Elkan, Timothy L. Bailey, C. Elkan, 1995, Mach. Learn..

Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic

Charles Elkan, C. Elkan, 1994, IEEE Expert.

Design and development of a semantic music discovery engine

Gert R. G. Lanckriet, Charles Elkan, Douglas Turnbull, 2008 .

Predicting labels for dyadic data

Charles Elkan, Aditya Krishna Menon, 2010, Data Mining and Knowledge Discovery.

A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems

Charles Elkan, C. Elkan, 1990, Artif. Intell..

Meta-MEME: motif-based hidden Markov models of protein families

William Noble Grundy, Charles Elkan, Timothy L. Bailey, 1997, Comput. Appl. Biosci..

Learning to Re-rank for Interactive Problem Resolution and Query Refinement

Charles Elkan, Rashmi Gangadharaiah, Balakrishnan Narayanaswamy, 2014, SIGDIAL Conference.

Quadratic Programming Feature Selection

Charles Elkan, Carlos Santa Cruz, Ramón Huerta, 2010, J. Mach. Learn. Res..

A Critical Look at Experimental Evaluations of EBL

Alexander Russell, Charles Elkan, Alberto Maria Segre, 1991, Machine Learning.

Learning gene regulatory networks from only positive and unlabeled data

Charles Elkan, Michele Ceccarelli, Luigi Cerulo, 2010, BMC Bioinformatics.

Logical Characterizations of Nonmonotonic TMSs

Charles Elkan, C. Elkan, 1989, MFCS.

Boosting and Naive Bayesian learning

Charles Elkan, C. Elkan, 1997 .

The Value of Prior Knowledge in Discovering Motifs with MEME

Charles Elkan, Timothy L. Bailey, C. Elkan, 1995, ISMB.

Policy Iteration Based on a Learned Transition Model

Charles Elkan, Vivek Ramavajjala, C. Elkan, 2012, ECML/PKDD.

MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences

William Noble Grundy, Charles Elkan, Timothy L. Bailey, 1999, Pattern Discovery in Biomolecular Data.

One-Class Remote Sensing Classification From Positive and Unlabeled Background Data

Charles Elkan, Qinghua Guo, Wenkai Li, 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Policy mining: learning decision policies from fixed sets of data

Bianca Zadrozny, Charles Elkan, 2003 .

Log-linear models and conditional random fields

Charles Elkan, 2007 .

Making generative classifiers robust to selection bias

Charles Elkan, Andrew T. Smith, C. Elkan, 2007, KDD '07.

Predictive analytics and data mining

Charles Elkan, C. Elkan, 2010 .

Using the Triangle Inequality to Accelerate k-Means

Charles Elkan, C. Elkan, 2003, ICML.

Estimating the Accuracy of Learned Concepts

Charles Elkan, Timothy L. Bailey, C. Elkan, 1993, IJCAI.

An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records

Charles Elkan, Alvaro E. Monge, C. Elkan, 1997, DMKD.

The Transporter Classification Database: recent advances

Charles Elkan, Milton H. Saier, Keith Noto, 2008, Nucleic Acids Res..

F1-Optimal Thresholding in the Multi-Label Setting

Charles Elkan, Balakrishnan Narayanaswamy, Zachary C. Lipton, 2014, ArXiv.

Evaluating Classifiers

Charles Elkan, C. Elkan, 2006 .

Learning meanings for sentences

Charles Elkan, 2012 .

Learning the � in �-means

Greg Hamerly, Charles Elkan, .

On Solving the Qualification Problem

Charles Elkan, C. Elkan, 1995 .

A Model-Independent Measure of Regression Difficulty

Umeshwar Dayal, Charles Elkan, Meichun Hsu, 2000 .