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Russell Greiner
Russ Greiner
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- affiliation: University of Alberta, Edmonton, Canada
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2020 – today
- 2024
- [j68]Philippe Weitz
, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo
The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue. Medical Image Anal. 97: 103257 (2024) - [j67]Sunil Vasu Kalmady
Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. npj Digit. Medicine 7(1) (2024) - [c138]Vikhyat Agrawal
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data. ICMHI 2024: 143-152 - [c137]Shiang Qi, Yakun Yu, Russell Greiner:
Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration. ICML 2024 - [i44]Amit K. Chakraborty, Shan Gao, Reza Miry, Pouria Ramazi, Russell Greiner, Mark A. Lewis, Hao Wang:
An early warning indicator trained on stochastic disease-spreading models with different noises. CoRR abs/2403.16233 (2024) - [i43]Adamo Young, Fei Wang, David S. Wishart, Bo Wang, Hannes L. Röst, Russ Greiner:
FraGNNet: A Deep Probabilistic Model for Mass Spectrum Prediction. CoRR abs/2404.02360 (2024) - [i42]Shan Gao, Amit K. Chakraborty, Russell Greiner, Mark A. Lewis, Hao Wang:
Early detection of disease outbreaks and non-outbreaks using incidence data. CoRR abs/2404.08893 (2024) - [i41]Vikhyat Agrawal, Sunil Vasu Kalmady, Venkataseetharam Manoj Malipeddi, Manisimha Varma Manthena, Weijie Sun, Saiful Islam, Abram Hindle, Padma Kaul, Russell Greiner:
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data. CoRR abs/2405.00725 (2024) - [i40]Shiang Qi, Yakun Yu, Russell Greiner:
Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration. CoRR abs/2405.07374 (2024) - [i39]Christian Marius Lillelund, Ali Hossein Gharari Foomani, Weijie Sun, Shiang Qi, Russell Greiner:
MENSA: A Multi-Event Network for Survival Analysis under Informative Censoring. CoRR abs/2409.06525 (2024) - [i38]Shiang Qi, Yakun Yu, Russell Greiner:
Toward Conditional Distribution Calibration in Survival Prediction. CoRR abs/2410.20579 (2024) - [i37]Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A. Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S. Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D. Mak, Soha Hassoun, Florian Huber, Justin J. J. van der Hooft, Michael A. Stravs, Sebastian Böcker, Josef Sivic, Tomás Pluskal:
MassSpecGym: A benchmark for the discovery and identification of molecules. CoRR abs/2410.23326 (2024) - 2023
- [j66]Weijie Sun, Sunil Vasu Kalmady
Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms. npj Digit. Medicine 6 (2023) - [j65]Shiang Qi
Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction. IEEE Trans. Biomed. Eng. 70(12): 3389-3400 (2023) - [c136]Zehra Shah, Shiang Qi
Exploring Language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer's Dementia. ICASSP 2023: 1-2 - [c135]Shiang Qi
An Effective Meaningful Way to Evaluate Survival Models. ICML 2023: 28244-28276 - [c134]Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulanger, Brian Buck:
Using temporal GAN to translate the current CTP scan to follow-up MRI, for predicting final acute ischemic stroke lesions. Computer-Aided Diagnosis 2023 - [c133]Yousef Nademi, Sunil Vasu Kalmady, Weijie Sun, Shiang Qi
Supervised Electrocardiogram(ECG) Features Outperform Knowledge-based And Unsupervised Features In Individualized Survival Prediction. ML4H@NeurIPS 2023: 368-384 - [c132]Yousef Nademi, Sunil Vasu Kalmady, Weijie Sun, Amir Salimi, Abram Hindle, Padma Kaul, Russell Greiner:
Generative Data by β-Variational Autoencoders Help Build Stronger Classifiers: ECG Use Case. SIPAIM 2023: 1-7 - [c131]Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan:
Copula-based deep survival models for dependent censoring. UAI 2023: 669-680 - [i36]Roberto Vega, Zehra Shah, Pouria Ramazi, Russell Greiner:
Modeling and Forecasting COVID-19 Cases using Latent Subpopulations. CoRR abs/2302.04829 (2023) - [i35]Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue. CoRR abs/2305.18033 (2023) - [i34]Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner:
An Effective Meaningful Way to Evaluate Survival Models. CoRR abs/2306.01196 (2023) - [i33]Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan:
Copula-Based Deep Survival Models for Dependent Censoring. CoRR abs/2306.11912 (2023) - 2022
- [j64]Rohan Panda, Sunil Vasu Kalmady
Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study. Frontiers Neuroinformatics 16: 805117 (2022) - [j63]Muhammad Yousefnezhad, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner:
Editorial: Multi-site neuroimage analysis: Domain adaptation and batch effects. Frontiers Neuroinformatics 16 (2022) - [j62]David S. Wishart
HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 50(D1): 622-631 (2022) - [j61]David S. Wishart
BioTransformer 3.0 - a web server for accurately predicting metabolic transformation products. Nucleic Acids Res. 50(W1): 115-123 (2022) - [j60]Fei Wang, Dana Allen, Siyang Tian, Eponine Oler, Vasuk Gautam, Russell Greiner, Thomas O. Metz, David S. Wishart
CFM-ID 4.0 - a web server for accurate MS-based metabolite identification. Nucleic Acids Res. 50(W1): 165-174 (2022) - [i32]Roberto Vega, Russell Greiner:
Domain-shift adaptation via linear transformations. CoRR abs/2201.05282 (2022) - [i31]Weijie Sun, Sunil Vasu Kalmady, Amir Salimi, Nariman Sepehrvand, Eric Ly, Abram Hindle, Russell Greiner, Padma Kaul
ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets. CoRR abs/2210.06291 (2022) - [i30]Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulanger, Brian Buck:
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP). CoRR abs/2211.09068 (2022) - [i29]Weijie Sun, Sunil Vasu Kalmady, Nariman Sepehrvand, Luan Manh Chu, Zihan Wang, Amir Salimi, Abram Hindle, Russell Greiner, Padma Kaul
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale. CoRR abs/2211.10431 (2022) - 2021
- [j59]Yaqian Long, Benoit Rivard, Arturo Sanchez-Azofeifa, Russell Greiner, Dominica Harrison, Sen Jia:
Identification of spectral features in the longwave infrared (LWIR) spectra of leaves for the discrimination of tropical dry forest tree species. Int. J. Appl. Earth Obs. Geoinformation 97: 102286 (2021) - [j58]Mohsen Soltanpour
Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Comput. Biol. Medicine 137: 104849 (2021) - [j57]Zehra Shah, Jeffrey Sawalha, Mashrura Tasnim
Learning Language and Acoustic Models for Identifying Alzheimer's Dementia From Speech. Frontiers Comput. Sci. 3: 624659 (2021) - [j56]Neil C. Borle
The challenge of predicting blood glucose concentration changes in patients with type I diabetes. Health Informatics J. 27(1): 146045822097758 (2021) - [j55]Siyang Tian
CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism. J. Chem. Inf. Model. 61(6): 3128-3140 (2021) - [j54]Michael A. Skinnider
A deep generative model enables automated structure elucidation of novel psychoactive substances. Nat. Mach. Intell. 3(11): 973-984 (2021) - [c130]Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner:
Sample efficient learning of image-based diagnostic classifiers via probabilistic labels. AISTATS 2021: 739-747 - [c129]Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Preface: AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021. SPACA 2021: 1-2 - [c128]Sunil Vasu Kalmady, Weijie Sun, Justin A. Ezekowitz, Nowell Fine, Jonathan Howlett, Anamaria Savu, Russ Greiner, Padma Kaul:
Improving the Calibration of Long Term Predictions of Heart Failure Rehospitalizations using Medical Concept Embedding. SPACA 2021: 70-82 - [c127]Li-Hao Kuan, Russell Greiner:
Finding Relevant Features for Different Times in Survival Prediction by Discrete Hazard Bayesian Network. SPACA 2021: 240-251 - [e1]Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Proceedings of AAAI Symposium on Survival Prediction - Algorithms, Challenges and Applications, SPACA 2021, Stanford University, Palo Alto, CA, USA, March 22-24, 2021. Proceedings of Machine Learning Research 146, PMLR 2021 [contents] - [i28]Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner:
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels. CoRR abs/2102.06164 (2021) - [i27]Roberto Vega, Leonardo Flores, Russell Greiner:
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting. CoRR abs/2106.01590 (2021) - [i26]Negar Hassanpour, Russell Greiner:
Variational Auto-Encoder Architectures that Excel at Causal Inference. CoRR abs/2111.06486 (2021) - 2020
- [j53]Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner:
Effective Ways to Build and Evaluate Individual Survival Distributions. J. Mach. Learn. Res. 21: 85:1-85:63 (2020) - [c126]Negar Hassanpour, Russell Greiner:
Learning Disentangled Representations for CounterFactual Regression. ICLR 2020 - [c125]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Domain Aggregation Networks for Multi-Source Domain Adaptation. ICML 2020: 10214-10224 - [c124]Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner:
Shared Space Transfer Learning for analyzing multi-site fMRI data. NeurIPS 2020 - [i25]Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner:
Shared Space Transfer Learning for analyzing multi-site fMRI data. CoRR abs/2010.15594 (2020)
2010 – 2019
- 2019
- [j52]Reyhaneh Ghoreishiamiri, Graham Little, Matthew R. G. Brown, Russell Greiner:
A simple classification framework for predicting Alzheimer's disease from region-based grey matter volume and APOE genotype status. Artif. Intell. Res. 8(2): 15- (2019) - [j51]Yannick Djoumbou Feunang
BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J. Cheminformatics 11(1): 2:1-2:25 (2019) - [j50]Jakub M. Tomczak
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines. Neural Process. Lett. 50(2): 1401-1419 (2019) - [c123]Mohsen Soltanpour, Russell Greiner, Pierre Boulanger, Brian Buck
Ischemic Stroke Lesion Prediction in CT Perfusion Scans Using Multiple Parallel U-Nets Following by a Pixel-Level Classifier. BIBE 2019: 957-963 - [c122]Negar Hassanpour, Russell Greiner:
CounterFactual Regression with Importance Sampling Weights. IJCAI 2019: 5880-5887 - [c121]Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner:
Simultaneous Prediction Intervals for Patient-Specific Survival Curves. IJCAI 2019: 5975-5981 - [c120]Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White:
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. NeurIPS 2019: 8847-8857 - [i24]Luke N. Kumar, Russell Greiner:
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach. CoRR abs/1903.10536 (2019) - [i23]Neil C. Borle, Edmond A. Ryan, Russell Greiner:
The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes. CoRR abs/1903.12347 (2019) - [i22]Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner:
Simultaneous Prediction Intervals for Patient-Specific Survival Curves. CoRR abs/1906.10780 (2019) - [i21]Seyedeh Sepideh Emam
Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning. CoRR abs/1908.09251 (2019) - [i20]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Domain Aggregation Networks for Multi-Source Domain Adaptation. CoRR abs/1909.05352 (2019) - [i19]Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russell Greiner:
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation. CoRR abs/1912.09040 (2019) - 2018
- [j49]Neil C. Borle, Meysam Feghhi, Eleni Stroulia, Russell Greiner, Abram Hindle:
Analyzing the effects of test driven development in GitHub. Empir. Softw. Eng. 23(4): 1931-1958 (2018) - [j48]Siyang Tian
CypReact: A Software Tool for in Silico Reactant Prediction for Human Cytochrome P450 Enzymes. J. Chem. Inf. Model. 58(6): 1282-1291 (2018) - [c119]Negar Hassanpour, Russell Greiner:
A Novel Evaluation Methodology for Assessing Off-Policy Learning Methods in Contextual Bandits. Canadian AI 2018: 31-44 - [c118]Neil C. Borle, Meysam Feghhi, Eleni Stroulia, Russell Greiner, Abram Hindle:
Analyzing the effects of test driven development in GitHub. ICSE 2018: 1062 - [c117]Roberto Vega
Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data. MLCN/DLF/iMIMIC@MICCAI 2018: 32-39 - [i18]Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner:
Effective Ways to Build and Evaluate Individual Survival Distributions. CoRR abs/1811.11347 (2018) - 2017
- [j47]Roberto Vega
Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artif. Intell. Res. 6(1): 37-51 (2017) - [j46]Karan Aggarwal, Finbarr Timbers, Tanner Rutgers, Abram Hindle, Eleni Stroulia, Russell Greiner:
Detecting duplicate bug reports with software engineering domain knowledge. J. Softw. Evol. Process. 29(3) (2017) - [c116]Stephen Romansky, Neil C. Borle, Shaiful Alam Chowdhury, Abram Hindle, Russell Greiner:
Deep Green: Modelling Time-Series of Software Energy Consumption. ICSME 2017: 273-283 - [c115]Mina Gheiratmand
Learning discriminative functional network features of schizophrenia. Biomedical Applications in Molecular, Structural, and Functional Imaging 2017: 101371A - [i17]Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner:
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks. CoRR abs/1712.00512 (2017) - 2016
- [j45]Yannick Djoumbou
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminformatics 8(1): 61:1-61:20 (2016) - [c114]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c113]Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner:
Boolean Matrix Factorization and Noisy Completion via Message Passing. ICML 2016: 945-954 - [i16]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. CoRR abs/1601.00034 (2016) - 2015
- [j44]Siamak Ravanbakhsh, Russell Greiner:
Perturbed message passing for constraint satisfaction problems. J. Mach. Learn. Res. 16: 1249-1274 (2015) - [c112]Shaiful Alam Chowdhury, Luke N. Kumar, Md. Toukir Imam, Mohomed Shazan Mohomed Jabbar, Varun Sapra, Karan Aggarwal, Abram Hindle, Russell Greiner:
A system-call based model of software energy consumption without hardware instrumentation. IGSC 2015: 1-6 - [c111]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Correcting Covariate Shift with the Frank-Wolfe Algorithm. IJCAI 2015: 1010-1016 - [c110]Karan Aggarwal, Tanner Rutgers, Finbarr Timbers, Abram Hindle, Russell Greiner, Eleni Stroulia:
Detecting duplicate bug reports with software engineering domain knowledge. SANER 2015: 211-220 - [i15]Siamak Ravanbakhsh, Russell Greiner:
Boolean Matrix Factorization and Completion via Message Passing. CoRR abs/1509.08535 (2015) - 2014
- [j43]Felicity Allen
CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res. 42(Webserver-Issue): 94-99 (2014) - [c109]Sheehan Khan, Russell Greiner:
The Budgeted Biomarker Discovery Problem: A Variant of Association Studies. AAAI Workshop: Modern Artificial Intelligence for Health Analytics 2014 - [c108]Sheehan Khan, Russell Greiner:
Budgeted transcript discovery: A framework for joint exploration and validation studies. BIBM 2014: 188-191 - [c107]Baidya Nath Saha, Amritpal Saini, Nilanjan Ray, Russell Greiner, Judith Hugh, Mauro Tambasco
A robust convergence index filter for breast cancer cell segmentation. ICIP 2014: 922-926 - [c106]Junfeng Wen, Chun-Nam Yu, Russell Greiner:
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification. ICML 2014: 631-639 - [c105]Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner:
Min-Max Problems on Factor Graphs. ICML 2014: 1035-1043 - [c104]Kun Deng, Russ Greiner, Susan A. Murphy:
Budgeted Learning for Developing Personalized Treatment. ICMLA 2014: 7-14 - [c103]Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner:
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. NIPS 2014: 289-297 - [i14]Siamak Ravanbakhsh, Russell Greiner:
Perturbed Message Passing for Constraint Satisfaction Problems. CoRR abs/1401.6686 (2014) - [i13]Siamak Ravanbakhsh, Russell Greiner, Brendan J. Frey:
Training Restricted Boltzmann Machine by Perturbation. CoRR abs/1405.1436 (2014) - [i12]Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner:
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. CoRR abs/1406.0941 (2014) - [i11]Siamak Ravanbakhsh, Philip Liu, Trent C. Bjorndahl, Rupasri Mandal, Jason R. Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu, Claudio Luchinat, Russell Greiner, David S. Wishart:
Accurate, fully-automated NMR spectral profiling for metabolomics. CoRR abs/1409.1456 (2014) - [i10]Siamak Ravanbakhsh, Russell Greiner:
Algebra of inference in graphical models revisited. CoRR abs/1409.7410 (2014) - 2013
- [j42]Mohsen Hajiloo, Yadav Sapkota
ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction. BMC Bioinform. 14: 61 (2013) - [j41]Mohsen Hajiloo, Babak Damavandi, Metanat HooshSadat, Farzad Sangi, John R. Mackey, Carol E. Cass, Russell Greiner, Sambasivarao Damaraju:
Breast cancer prediction using genome wide single nucleotide polymorphism data. BMC Bioinform. 14(S-13): S3 (2013) - [j40]Shaojun Wang, Shaomin Wang, Li Cheng
Exploiting Syntactic, Semantic, and Lexical Regularities in Language Modeling via Directed Markov Random Fields. Comput. Intell. 29(4): 649-679 (2013) - [j39]Shaojun Wang, Russell Greiner
Consistency and Generalization Bounds for Maximum Entropy Density Estimation. Entropy 15(12): 5439-5463 (2013) - [j38]David S. Wishart
HMDB 3.0 - The Human Metabolome Database in 2013. Nucleic Acids Res. 41(Database-Issue): 801-807 (2013) - [c102]Marius Stanescu, Sergio Poo Hernandez, Graham Erickson, Russell Greiner, Michael Buro:
Predicting Army Combat Outcomes in StarCraft. AIIDE 2013 - [c101]Idanis Diaz, Pierre Boulanger, Russell Greiner, Bret Hoehn, Lindsay Rowe, Albert Murtha:
An automatic brain tumor segmentation tool. EMBC 2013: 3339-3342 - [c100]Sheehan Khan, Russell Greiner:
Finding Discriminatory Genes: A Methodology for Validating Microarray Studies. ICDM Workshops 2013: 64-71 - [c99]Mohamed Ben Salah, Idanis Diaz, Russell Greiner, Pierre Boulanger, Bret Hoehn, Albert Murtha:
Fully Automated Brain Tumor Segmentation Using Two MRI Modalities. ISVC (1) 2013: 30-39 - [c98]Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári:
Online Learning with Costly Features and Labels. NIPS 2013: 1241-1249 - [i9]Tim Van Allen, Russell Greiner, Peter Hooper:
Bayesian Error-Bars for Belief Net Inference. CoRR abs/1301.2313 (2013) - [i8]Jie Cheng, Russell Greiner:
Comparing Bayesian Network Classifiers. CoRR abs/1301.6684 (2013) - [i7]Russell Greiner, Adam J. Grove, Dale Schuurmans:
Learning Bayesian Nets that Perform Well. CoRR abs/1302.1542 (2013) - [i6]Felicity Allen, Russell Greiner, David S. Wishart:
Competitive Fragmentation Modeling of ESI-MS/MS spectra for metabolite identification. CoRR abs/1312.0264 (2013) - 2012
- [j37]Baidya Nath Saha, Nilanjan Ray, Russell Greiner, Albert Murtha, Hong Zhang:
Quick detection of brain tumors and edemas: A bounding box method using symmetry. Comput. Medical Imaging Graph. 36(2): 95-107 (2012) - [j36]Ashkan Zarnani, Petr Musílek
Learning to predict ice accretion on electric power lines. Eng. Appl. Artif. Intell. 25(3): 609-617 (2012) - [j35]Davoud Moulavi, Mohsen Hajiloo, Jörg Sander
Combining gene expression and interaction network data to improve kidney lesion score prediction. Int. J. Bioinform. Res. Appl. 8(1/2): 54-66 (2012) - [j34]Daniel J. Lizotte
An experimental methodology for response surface optimization methods. J. Glob. Optim. 53(4): 699-736 (2012) - [c97]Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner:
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. ICML 2012 - [i5]Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn:
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling. CoRR abs/1205.2642 (2012) - [i4]Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner:
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions. CoRR abs/1206.3233 (2012) - [i3]Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner:
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. CoRR abs/1206.4654 (2012) - [i2]Omid Madani, Daniel J. Lizotte, Russell Greiner:
Active Model Selection. CoRR abs/1207.4138 (2012) - [i1]Daniel J. Lizotte, Omid Madani, Russell Greiner:
Budgeted Learning of Naive-Bayes Classifiers. CoRR abs/1212.2472 (2012) - 2011
- [c96]Idanis Diaz, Pierre Boulanger, Russell Greiner, Albert Murtha:
A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation. EMBC 2011: 3934-3937 - [c95]Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos:
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors. NIPS 2011: 1845-1853 - [c94]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) 2011: 124-135 - 2010
- [j33]Oliver Schulte, Wei Luo
Mind change optimal learning of Bayes net structure from dependency and independency data. Inf. Comput. 208(1): 63-82 (2010) - [c93]Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner:
A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra. AAAI 2010: 1280-1286 - [c92]Oliver Schulte, Gustavo Frigo, Russell Greiner, Hassan Khosravi
The IMAP Hybrid Method for Learning Gaussian Bayes Nets. Canadian AI 2010: 123-134 - [c91]Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner:
Budgeted Distribution Learning of Belief Net Parameters. ICML 2010: 879-886
2000 – 2009
- 2009
- [j32]Babak Bostan, Russell Greiner, Duane Szafron, Paul Lu:
Predicting homologous signaling pathways using machine learning. Bioinform. 25(22): 2913-2920 (2009) - [j31]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Making an accurate classifier ensemble by voting on classifications from imputed learning sets. Int. J. Inf. Decis. Sci. 1(3): 301-322 (2009) - [j30]David S. Wishart
HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37(Database-Issue): 603-610 (2009) - [c90]Tingshao Zhu, Russell Greiner, Bin Hu:
LILAC - Learn from Internet: Log, Annotation, and Content. AAAI Spring Symposium: Experimental Design for Real-World Systems 2009: 57- - [c89]Aliaksei Kerhet, Cormac Small
Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach. AIME 2009: 146-155 - [c88]Oliver Schulte, Gustavo Frigo, Russell Greiner, Wei Luo
A new hybrid method for Bayesian network learning With dependency constraints. CIDM 2009: 53-60 - [c87]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
VipBoost: A More Accurate Boosting Algorithm. FLAIRS 2009 - [c86]Alireza Farhangfar, Russell Greiner, Csaba Szepesvári:
Learning to segment from a few well-selected training images. ICML 2009: 305-312 - [c85]Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant:
Learning when to stop thinking and do something! ICML 2009: 825-832 - [c84]Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn:
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling. UAI 2009: 232-239 - 2008
- [j29]Tim Van Allen, Ajit Singh, Russell Greiner, Peter Hooper:
Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artif. Intell. 172(4-5): 483-513 (2008) - [j28]Alona Fyshe
Improving subcellular localization prediction using text classification and the gene ontology. Bioinform. 24(21): 2512-2517 (2008) - [j27]Ilya Levner, Hong Zhang, Russell Greiner:
Heterogeneous Stacking for Classification-Driven Watershed Segmentation. EURASIP J. Adv. Signal Process. 2008 (2008) - [j26]Chi-Hoon Lee, Osmar R. Zaïane, Ho-Hyun Park, Jiayuan Huang, Russell Greiner:
Clustering high dimensional data: A graph-based relaxed optimization approach. Inf. Sci. 178(23): 4501-4511 (2008) - [c83]Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner, Shaojun Wang, Albert Murtha:
Constrained Classification on Structured Data. AAAI 2008: 1812-1813 - [c82]Alejandro Isaza, Jieshan Lu, Vadim Bulitko, Russell Greiner:
A Cover-Based Approach to Multi-Agent Moving Target Pursuit. AIIDE 2008 - [c81]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
A Mixture Imputation-Boosted Collaborative Filter. FLAIRS 2008: 312-316 - [c80]Ilya Levner, Russell Greiner, Hong Zhang:
Supervised image segmentation via ground truth decomposition. ICIP 2008: 737-740 - [c79]John D. Lees-Miller
Does Wikipedia Information Help Netflix Predictions? ICMLA 2008: 337-343 - [c78]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Using Imputation Techniques to Help Learn Accurate Classifiers. ICTAI (1) 2008: 437-444 - [c77]Alireza Farhangfar, Russell Greiner, Martin Zinkevich:
A Fast Way to Produce Optimal Fixed-Depth Decision Trees. ISAIM 2008 - [c76]Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R. G. Brown, Russell Greiner:
Segmenting Brain Tumors Using Pseudo-Conditional Random Fields. MICCAI (1) 2008: 359-366 - [c75]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
Imputation-boosted collaborative filtering using machine learning classifiers. SAC 2008: 949-950 - [c74]Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner:
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction. UAI 2008: 306-314 - [c73]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Imputed Neighborhood Based Collaborative Filtering. Web Intelligence 2008: 633-639 - 2007
- [j25]Lihong Li, Vadim Bulitko, Russell Greiner:
Focus of Attention in Reinforcement Learning. J. Univers. Comput. Sci. 13(9): 1246-1269 (2007) - [j24]David S. Wishart
HMDB: the Human Metabolome Database. Nucleic Acids Res. 35(Database-Issue): 521-526 (2007) - [c72]Oliver Schulte, Wei Luo
Mind Change Optimal Learning of Bayes Net Structure. COLT 2007: 187-202 - [c71]Yuhong Guo, Russell Greiner:
Optimistic Active-Learning Using Mutual Information. IJCAI 2007: 823-829 - [c70]David S. Wishart, Russell Greiner:
Session Introduction. Pacific Symposium on Biocomputing 2007: 112-114 - [c69]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu:
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts. Web Intelligence 2007: 645-649 - 2006
- [j23]Russell Greiner, Ryan Hayward, Magdalena Jankowska, Michael Molloy:
Finding optimal satisficing strategies for and-or trees. Artif. Intell. 170(1): 19-58 (2006) - [j22]Marianne Morris, Russell Greiner, Jörg Sander
Learning a Classification-based Glioma Growth Model Using MRI Data. J. Comput. 1(7): 21-31 (2006) - [j21]Luca Pireddu
The Path-A metabolic pathway prediction web server. Nucleic Acids Res. 34(Web-Server-Issue): 714-719 (2006) - [c68]Brett Poulin, Roman Eisner, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Alona Fyshe, Brandon Pearcy, Cam Macdonell, John Anvik:
Visual Explanation of Evidence with Additive Classifiers. AAAI 2006: 1822-1829 - [c67]Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans:
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. ACL 2006 - [c66]Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt:
A Classification-Based Glioma Diffusion Model Using MRI Data. Canadian AI 2006: 98-109 - [c65]Ramana Isukapalli, Ahmed M. Elgammal
Learning to Detect Objects of Many Classes Using Binary Classifiers. ECCV (1) 2006: 352-364 - [c64]Ramana Isukapalli, Ahmed M. Elgammal
Learning to Identify Facial Expression During Detection Using Markov Decision Process. FGR 2006: 305-310 - [c63]Shaojun Wang, Shaomin Wang, Li Cheng
Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. ICGI 2006: 97-111 - [c62]Chi-Hoon Lee, Russell Greiner, Shaojun Wang:
Using query-specific variance estimates to combine Bayesian classifiers. ICML 2006: 529-536 - [c61]Ramana Isukapalli, Ahmed M. Elgammal
Learning Policies for Efficiently Identifying Objects of Many Classes. ICPR (3) 2006: 356-361 - [c60]Robert Price, Russell Greiner, Gerald Häubl, Alden Flatt:
Automatic construction of personalized customer interfaces. IUI 2006: 250-257 - [c59]Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner:
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. NIPS 2006: 793-800 - [c58]Jiayuan Huang, Tingshao Zhu
Information Marginalization on Subgraphs. PKDD 2006: 199-210 - [c57]Chi-Hoon Lee, Russell Greiner, Osmar R. Zaïane:
Efficient Spatial Classification Using Decoupled Conditional Random Fields. PKDD 2006: 272-283 - 2005
- [j20]Russell Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou:
Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. Mach. Learn. 59(3): 297-322 (2005) - [j19]Paul Lu, Duane Szafron, Russell Greiner, David S. Wishart
PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization. Nucleic Acids Res. 33(Database-Issue): 147-153 (2005) - [j18]Gary H. Van Domselaar, Paul Stothard, Savita Shrivastava, Joseph A. Cruz, Anchi Guo, Xiaoli Dong, Paul Lu, Duane Szafron, Russell Greiner, David S. Wishart
BASys: a web server for automated bacterial genome annotation. Nucleic Acids Res. 33(Web-Server-Issue): 455-459 (2005) - [c56]Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price:
Goal-Directed Site-Independent Recommendations from Passive Observations. AAAI 2005: 549-557 - [c55]Yuhong Guo, Russell Greiner:
Discriminative Model Selection for Belief Net Structures. AAAI 2005: 770-776 - [c54]Brett Poulin, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Roman Eisner, Alona Fyshe, Brandon Pearcy, Luca Pireddu:
The Proteome Analyst Suite of Automated Function Prediction Tools. AAAI 2005: 1698-1699 - [c53]Ramana Isukapalli, Ahmed M. Elgammal
Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression. AMFG 2005: 70-84 - [c52]Roman Eisner, Brett Poulin, Duane Szafron, Paul Lu, Russell Greiner:
Improving Protein Function Prediction Using the Hierarchical Structure of the Gene Ontology. CIBCB 2005: 354-363 - [c51]Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner:
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines. CVBIA 2005: 469-478 - [c50]Aloak Kapoor, Russell Greiner:
Learning and Classifying Under Hard Budgets. ECML 2005: 170-181 - [c49]Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ICML 2005: 948-955 - [c48]Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz:
Segmenting brain tumors using alignment-based features. ICMLA 2005 - [c47]Yuhong Guo, Russell Greiner, Dale Schuurmans:
Learning Coordination Classifiers. IJCAI 2005: 714-721 - [c46]Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price:
Using Learned Browsing Behavior Models to Recommend Relevant Web Pages. IJCAI 2005: 1589-1590 - [c45]Chi-Hoon Lee, Russell Greiner, Mark Schmidt:
Support Vector Random Fields for Spatial Classification. PKDD 2005: 121-132 - [c44]Tingshao Zhu
Off-line Evaluation of Recommendation Functions. User Modeling 2005: 337-341 - 2004
- [j17]Zhiyong Lu, Duane Szafron, Russell Greiner, Paul Lu, David S. Wishart
Predicting subcellular localization of proteins using machine-learned classifiers. Bioinform. 20(4): 547-556 (2004) - [j16]Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart
Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Res. 32(Web-Server-Issue): 365-371 (2004) - [c43]Omid Madani, Daniel J. Lizotte, Russell Greiner:
The Budgeted Multi-armed Bandit Problem. COLT 2004: 643-645 - [c42]Lihong Li, Vadim Bulitko, Russell Greiner:
Batch Reinforcement Learning with State Importance. ECML 2004: 566-568 - [c41]Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ISCSLP 2004: 305-308 - [c40]Omid Madani, Daniel J. Lizotte, Russell Greiner:
Active Model Selection. UAI 2004: 357-365 - 2003
- [c39]Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee, Russell Greiner:
Towards Automated Creation of Image Interpretation Systems. Australian Conference on Artificial Intelligence 2003: 653-665 - [c38]Bin Shen, Xiaoyuan Su, Russell Greiner, Petr Musílek, Corrine Cheng:
Discriminative Parameter Learning of General Bayesian Network Classifiers. ICTAI 2003: 296-305 - [c37]Tingshao Zhu
Predicting Web Information Content. ITWP 2003: 241-254 - [c36]Ramana Isukapalli, Russell Greiner:
Use of Off-line Dynamic Programming for Efficient Image Interpretation. IJCAI 2003: 1319-1325 - [c35]Vadim Bulitko, Lihong Li, Russell Greiner, Ilya Levner:
Lookahead Pathologies for Single Agent Search. IJCAI 2003: 1531-1533 - [c34]Daniel J. Lizotte, Omid Madani, Russell Greiner:
Budgeted Learning of Naive-Bayes Classifiers. UAI 2003: 378-385 - [c33]Tingshao Zhu
Learning a Model of a Web User's Interests. User Modeling 2003: 65-75 - [c32]Tingshao Zhu, Russell Greiner, Gerald Häubl:
An Effective Complete-Web Recommender System. WWW (Alternate Paper Tracks) 2003 - 2002
- [j15]Jie Cheng, Russell Greiner, Jonathan Kelly, David A. Bell, Weiru Liu:
Learning Bayesian networks from data: An information-theory based approach. Artif. Intell. 137(1-2): 43-90 (2002) - [j14]Russell Greiner, Adam J. Grove, Dan Roth:
Learning cost-sensitive active classifiers. Artif. Intell. 139(2): 137-174 (2002) - [c31]Russell Greiner, Wei Zhou:
Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. AAAI/IAAI 2002: 167-173 - [c30]Russell Greiner, Ryan Hayward, Michael Molloy:
Optimal Depth-First Strategies for And-Or Trees. AAAI/IAAI 2002: 725-730 - [c29]Ilya Levner, Vadim Bulitko, Omid Madani, Russell Greiner:
Performance of Lookahead Control Policies in the Face of Abstractions and Approximations. SARA 2002: 299-307 - 2001
- [j13]Russell Greiner, Christian Darken, N. Iwan Santoso:
Efficient reasoning. ACM Comput. Surv. 33(1): 1-30 (2001) - [c28]Jie Cheng, Russell Greiner:
Learning Bayesian Belief Network Classifiers: Algorithms and System. AI 2001: 141-151 - [c27]Ramana Isukapalli, Russell Greiner:
Efficient Car Recognition Policies. ICRA 2001: 2134-2139 - [c26]Ramana Isukapalli, Russell Greiner:
Efficient Interpretation Policies. IJCAI 2001: 1381-1390 - [c25]Tim Van Allen, Russell Greiner, Peter Hooper:
Bayesian Error-Bars for Belief Net Inference. UAI 2001: 522-529 - 2000
- [c24]Benjamin Korvemaker, Russell Greiner:
Predicting UNIX Command Lines: Adjusting to User Patterns. AAAI/IAAI 2000: 230-235 - [c23]Tim Van Allen, Russell Greiner:
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison. ICML 2000: 1047-1054
1990 – 1999
- 1999
- [j12]Russell Greiner:
The Complexity of Revising Logic Programs. J. Log. Program. 40(2-3): 273-298 (1999) - [c22]Jie Cheng, Russell Greiner:
Comparing Bayesian Network Classifiers. UAI 1999: 101-108 - 1997
- [j11]Devika Subramanian
The Relevance of Relevance (Editorial). Artif. Intell. 97(1-2): 1-5 (1997) - [j10]Russell Greiner, Adam J. Grove, Alexander Kogan:
Knowing what doesn't Matter: Exploiting the Omission of Irrelevant Data. Artif. Intell. 97(1-2): 345-380 (1997) - [c21]Tobias Scheffer, Russell Greiner, Christian Darken:
Why Experimentation can be better than "Perfect Guidance". ICML 1997: 331-339 - [c20]Russell Greiner, Adam J. Grove, Dale Schuurmans:
Learning Bayesian Nets that Perform Well. UAI 1997: 198-207 - 1996
- [j9]Russell Greiner, Pekka Orponen
Probably Approximately Optimal Satisficing Strategies. Artif. Intell. 82(1-2): 21-44 (1996) - [j8]Russell Greiner:
PALO: A Probabilistic Hill-Climbing Algorithm. Artif. Intell. 84(1-2): 177-208 (1996) - [j7]Russell Greiner, Ramana Isukapalli:
Learning to select useful landmarks. IEEE Trans. Syst. Man Cybern. Part B 26(3): 437-449 (1996) - [c19]Russell Greiner, Adam J. Grove, Dan Roth:
Learning Active Classifiers. ICML 1996: 207-215 - [c18]Russell Greiner, Adam J. Grove, Alexander Kogan:
Exploiting the Omission of Irrelevant Data. ICML 1996: 216-224 - 1995
- [c17]Dale Schuurmans, Russell Greiner:
Sequential PAC Learning. COLT 1995: 377-384 - [c16]Russell Greiner:
The Challenge of Revising an Impure Theory. ICML 1995: 269-277 - [c15]Russell Greiner:
The Complexity of Theory Revision. IJCAI 1995: 1162-1168 - [c14]Dale Schuurmans, Russell Greiner:
Practical PAC Learning. IJCAI 1995: 1169-1177 - 1994
- [c13]Russell Greiner, Ramana Isukapalli:
Learning to Select Useful Landmarks. AAAI 1994: 1251-1256 - 1993
- [j6]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) - 1992
- [c12]Russell Greiner, Igor Jurisica:
A Statistical Approach to Solving the EBL Utility Problem. AAAI 1992: 241-248 - [c11]Russell Greiner, Dale Schuurmans:
Learning an Optimally Accurate Representation System. ECAI Workshop on Knowledge Representation and Reasoning 1992: 145-159 - [c10]Russell Greiner, Dale Schuurmans:
Learning Useful Horn Approximations. KR 1992: 383-392 - [c9]Russell Greiner:
Learning Efficient Query Processing Strategies. PODS 1992: 33-46 - 1991
- [j5]Russell Greiner:
Finding Optimal Derivation Strategies in Redundant Knowledge Bases. Artif. Intell. 50(1): 95-115 (1991) - [c8]Russell Greiner, Charles Elkan:
Measuring and Improving the Effectiveness of Representations. IJCAI 1991: 518-524 - [c7]Russell Greiner, Pekka Orponen:
Probably Approximately Optimal Derivation Strategies. KR 1991: 277-288 - 1990
- [c6]Pekka Orponen, Russell Greiner:
On the Sample Complexity of Finding Good Search Strategies. COLT 1990: 352-358
1980 – 1989
- 1989
- [j4]Russell Greiner, Barbara A. Smith, Ralph W. Wilkerson:
A Correction to the Algorithm in Reiter's Theory of Diagnosis. Artif. Intell. 41(1): 79-88 (1989) - [c5]Russell Greiner:
Towards a Formal Analysis of EBL. ML 1989: 450-453 - [c4]Russell Greiner, J. Likuski:
Incorporating Redundant Learned Rules: A Preliminary Formal Analysis of EBL. IJCAI 1989: 744-749 - 1988
- [j3]Russell Greiner:
Learning by Understanding Analogies. Artif. Intell. 35(1): 81-125 (1988) - [j2]Russell Greiner:
Against the unjustified use of probabilities. Comput. Intell. 4: 79-83 (1988) - [j1]Russell Greiner:
A Review of Machine Learning at AAAI-87. Mach. Learn. 3: 79-92 (1988) - [c3]S. Lee, Evangelos E. Milios, Russell Greiner, James R. Rossiter:
Signal abstractions in the machine analysis of radar signals for ice profiling. ICASSP 1988: 1224-1227 - 1985
- [b1]Russell Greiner:
Learning by understanding analogies. Stanford University, USA, 1985 - 1983
- [c2]Russell Greiner, Michael R. Genesereth:
What's New? A Semantic Definition of Novelty. IJCAI 1983: 450-454 - 1980
- [c1]Russell Greiner, Douglas B. Lenat:
A Representation Language Language. AAAI 1980: 165-169
Coauthor Index
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