Greiner, Russell

77 publications

ICLR 2025 Extendable and Iterative Structure Learning Strategy for Bayesian Networks Hamid Kalantari, Russell Greiner, Pouria Ramazi

TMLR 2025 FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction Adamo Young, Fei Wang, David Wishart, Bo Wang, Russell Greiner, Hannes Rost

ICML 2024 Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration Shi-Ang Qi, Yakun Yu, Russell Greiner

NeurIPS 2024 MassSpecGym: A Benchmark for the Discovery and Identification of Molecules 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, Tomáš Pluskal

NeurIPS 2024 Toward Conditional Distribution Calibration in Survival Prediction Shi-ang Qi, Yakun Yu, Russell Greiner

ICML 2023 An Effective Meaningful Way to Evaluate Survival Models Shi-Ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner

UAI 2023 Copula-Based Deep Survival Models for Dependent Censoring Ali Hossein Foomani Gharari, Michael Cooper, Russell Greiner, Rahul G Krishnan

NeurIPSW 2022 Improving ECG-Based COVID-19 Diagnosis and Mortality Predictions Using Pre-Pandemic Medical Records at Population-Scale Weijie Sun, Sunil Vasu Kalmady, Nariman Sepehrvand, Luan Manh Chu, Zihan Wang, Amir Salimi, Abram Hindle, Russell Greiner, Padma Kaul

AISTATS 2021 Sample Efficient Learning of Image-Based Diagnostic Classifiers via Probabilistic Labels Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth, Jeevesh Kapur, Jacob Jaremko, Russell Greiner

ICML 2020 Domain Aggregation Networks for Multi-Source Domain Adaptation Junfeng Wen, Russell Greiner, Dale Schuurmans

JMLR 2020 Effective Ways to Build and Evaluate Individual Survival Distributions Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner

ICLR 2020 Learning Disentangled Representations for CounterFactual Regression Negar Hassanpour, Russell Greiner

NeurIPS 2020 Shared Space Transfer Learning for Analyzing Multi-Site fMRI Data Tony Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew Greenshaw, Russell Greiner

IJCAI 2019 CounterFactual Regression with Importance Sampling Weights Negar Hassanpour, Russell Greiner

NeurIPS 2019 Learning Macroscopic Brain Connectomes via Group-Sparse Factorization Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White

IJCAI 2019 Simultaneous Prediction Intervals for Patient-Specific Survival Curves Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner

ICML 2016 Boolean Matrix Factorization and Noisy Completion via Message Passing Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner

AISTATS 2016 Stochastic Neural Networks with Monotonic Activation Functions Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner

IJCAI 2015 Correcting Covariate Shift with the Frank-Wolfe Algorithm Junfeng Wen, Russell Greiner, Dale Schuurmans

JMLR 2015 Perturbed Message Passing for Constraint Satisfaction Problems Siamak Ravanbakhsh, Russell Greiner

NeurIPS 2014 Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner

ICML 2014 Min-Max Problems on Factor Graphs Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner

ICML 2014 Robust Learning Under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification Junfeng Wen, Chun-Nam Yu, Russell Greiner

NeurIPS 2013 Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari

ICML 2012 A Generalized Loop Correction Method for Approximate Inference in Graphical Models Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner

NeurIPS 2011 Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos

AAAI 2010 A Cross-Entropy Method That Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner

ICML 2010 Budgeted Distribution Learning of Belief Net Parameters Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner

UAI 2009 Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn

ICML 2009 Learning When to Stop Thinking and Do Something! Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant

ICML 2009 Learning to Segment from a Few Well-Selected Training Images Alireza Farhangfar, Russell Greiner, Csaba Szepesvári

AAAI 2008 Constrained Classification on Structured Data Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner, Shaojun Wang, Albert Murtha

UAI 2008 Speeding up Planning in Markov Decision Processes via Automatically Constructed Abstraction Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner

COLT 2007 Mind Change Optimal Learning of Bayes Net Structure Oliver Schulte, Wei Luo, Russell Greiner

IJCAI 2007 Optimistic Active-Learning Using Mutual Information Yuhong Guo, Russell Greiner

ECCV 2006 Learning to Detect Objects of Many Classes Using Binary Classifiers Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner

NeurIPS 2006 Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields Chi-hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner

ICML 2006 Using Query-Specific Variance Estimates to Combine Bayesian Classifiers Chi-Hoon Lee, Russell Greiner, Shaojun Wang

AAAI 2006 Visual Explanation of Evidence with Additive Classifiers Brett Poulin, Roman Eisner, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Alona Fyshe, Brandon Pearcy, Cam Macdonell, John Anvik

AAAI 2005 Discriminative Model Selection for Belief Net Structures Yuhong Guo, Russell Greiner

ICML 2005 Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng

AAAI 2005 Goal-Directed Site-Independent Recommendations from Passive Observations Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price

IJCAI 2005 Learning Coordination Classifiers Yuhong Guo, Russell Greiner, Dale Schuurmans

ECML-PKDD 2005 Learning and Classifying Under Hard Budgets Aloak Kapoor, Russell Greiner

MLJ 2005 Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers Russell Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou

AAAI 2005 The Proteome Analyst Suite of Automated Function Prediction Tools Brett Poulin, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Roman Eisner, Alona Fyshe, Brandon Pearcy, Luca Pireddu

IJCAI 2005 Using Learned Browsing Behavior Models to Recommend Relevant Web Pages Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price

UAI 2004 Active Model Selection Omid Madani, Daniel J. Lizotte, Russell Greiner

ECML-PKDD 2004 Batch Reinforcement Learning with State Importance Lihong Li, Vadim Bulitko, Russell Greiner

COLT 2004 The Budgeted Multi-Armed Bandit Problem Omid Madani, Daniel J. Lizotte, Russell Greiner

UAI 2003 Budgeted Learning of Naive-Bayes Classifiers Daniel J. Lizotte, Omid Madani, Russell Greiner

IJCAI 2003 Lookahead Pathologies for Single Agent Search Vadim Bulitko, Lihong Li, Russell Greiner, Ilya Levner

IJCAI 2003 Use of Off-Line Dynamic Programming for Efficient Image Interpretation Ramana Isukapalli, Russell Greiner

AAAI 2002 Optimal Depth-First Strategies for And-or Trees Russell Greiner, Ryan Hayward, Michael Molloy

AAAI 2002 Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers Russell Greiner, Wei Zhou

UAI 2001 Bayesian Error-Bars for Belief Net Inference Tim Van Allen, Russell Greiner, Peter Hooper

IJCAI 2001 Efficient Interpretation Policies Ramana Isukapalli, Russell Greiner

ICML 2000 Model Selection Criteria for Learning Belief Nets: An Empirical Comparison Tim Van Allen, Russell Greiner

AAAI 2000 Predicting UNIX Command Lines: Adjusting to User Patterns Benjamin Korvemaker, Russell Greiner

UAI 1999 Comparing Bayesian Network Classifiers Jie Cheng, Russell Greiner

UAI 1997 Learning Bayesian Nets That Perform Well Russell Greiner, Adam J. Grove, Dale Schuurmans

ICML 1997 Why Experimentation Can Be Better than "Perfect Guidance" Tobias Scheffer, Russell Greiner, Christian Darken

ICML 1996 Exploiting the Omission of Irrelevant Data Russell Greiner, Adam J. Grove, Alexander Kogan

ICML 1996 Learning Active Classifiers Russell Greiner, Adam J. Grove, Dan Roth

IJCAI 1995 Practical PAC Learning Dale Schuurmans, Russell Greiner

COLT 1995 Sequential PAC Learning Dale Schuurmans, Russell Greiner

ICML 1995 The Challenge of Revising an Impure Theory Russell Greiner

IJCAI 1995 The Complexity of Theory Revision Russell Greiner

AAAI 1994 Learning to Select Useful Landmarks Russell Greiner, Ramana Isukapalli

AAAI 1992 A Statistical Approach to Solving the EBL Utility Problem Russell Greiner, Igor Jurisica

IJCAI 1991 Measuring and Improving the Effectiveness of Representations Russell Greiner, Charles Elkan

COLT 1990 On the Sample Complexity of Finding Good Search Strategies Pekka Orponen, Russell Greiner

IJCAI 1989 Incorporating Redundant Learned Rules: A Preliminary Formal Analysis of EBL Russell Greiner, J. Likuski

ICML 1989 Towards a Formal Analysis of EBL Russell Greiner

MLJ 1988 A Review of Machine Learning at AAAI-87 Russell Greiner

IJCAI 1983 What's New? a Semantic Definition of Novelty Russell Greiner, Michael R. Genesereth

AAAI 1980 A Representation Language Language Russell Greiner, Douglas B. Lenat