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ORCIDThomas G. Dietterich
Thomas Glenn Dietterich – Tom Dietterich
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- affiliation: Oregon State University, School of Electrical Engineering and Computer Science
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Journal Articles
- 2023
[j64]Kiri L. Wagstaff, Thomas G. Dietterich:
Hidden Heterogeneity: When to Choose Similarity-Based Calibration. Trans. Mach. Learn. Res. 2023 (2023)- 2022
- [j63]Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich:
PAC Guarantees and Effective Algorithms for Detecting Novel Categories. J. Mach. Learn. Res. 23: 44:1-44:47 (2022) - [j62]Thomas G. Dietterich
, Alexander Guyer:
The familiarity hypothesis: Explaining the behavior of deep open set methods. Pattern Recognit. 132: 108931 (2022) - 2021
- [j61]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. Proc. IEEE 109(5): 756-795 (2021) - 2020
- [j60]Joshua Alspector, Thomas G. Dietterich:
DARPA's Role in Machine Learning. AI Mag. 41(2): 36-48 (2020) - [j59]Shubhomoy Das, Weng-Keen Wong, Thomas G. Dietterich, Alan Fern, Andrew Emmott:
Discovering Anomalies by Incorporating Feedback from an Expert. ACM Trans. Knowl. Discov. Data 14(4): 49:1-49:32 (2020) - 2019
- [j58]Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman:
Computational sustainability: computing for a better world and a sustainable future. Commun. ACM 62(9): 56-65 (2019) - [j57]Thomas G. Dietterich:
Robust artificial intelligence and robust human organizations. Frontiers Comput. Sci. 13(1): 1-3 (2019) - [j56]Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Weng-Keen Wong:
Sequential Feature Explanations for Anomaly Detection. ACM Trans. Knowl. Discov. Data 13(1): 1:1-1:22 (2019) - 2017
- [j55]Thomas G. Dietterich:
Steps Toward Robust Artificial Intelligence. AI Mag. 38(3): 3-24 (2017) - [j54]Jesse Hostetler, Alan Fern, Thomas G. Dietterich:
Sample-Based Tree Search with Fixed and Adaptive State Abstractions. J. Artif. Intell. Res. 60: 717-777 (2017) - [j53]Sean McGregor, Hailey Buckingham, Thomas G. Dietterich, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer:
Interactive visualization for testing Markov Decision Processes: MDPVIS. J. Vis. Lang. Comput. 39: 93-106 (2017) - 2015
- [j52]Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, D. Scott Phoenix:
Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. AI Mag. 36(4): 3-4 (2015) - [j51]Eric Eaton, Tom Dietterich, Maria L. Gini, Barbara J. Grosz, Charles L. Isbell Jr., Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael J. Wooldridge:
Who speaks for AI? AI Matters 2(2): 4-14 (2015) - [j50]Thomas G. Dietterich, Eric Horvitz:
Rise of concerns about AI: reflections and directions. Commun. ACM 58(10): 38-40 (2015) - [j49]Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, H. Jo Albers:
PAC optimal MDP planning with application to invasive species management. J. Mach. Learn. Res. 16: 3877-3903 (2015) - 2014
- [j48]Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling:
Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability. AI Mag. 35(2): 31-48 (2014) - [j47]Kshitij Judah, Alan Paul Fern, Thomas G. Dietterich, Prasad Tadepalli:
Active lmitation learning: formal and practical reductions to I.I.D. learning. J. Mach. Learn. Res. 15(1): 3925-3963 (2014) - 2012
- [j46]Xiaoqin Zhang, Bhavesh Shrestha, Sung Wook Yoon, Subbarao Kambhampati, Phillip DiBona, Jinhong K. Guo, Daniel McFarlane, Martin O. Hofmann, Kenneth R. Whitebread, Darren Scott Appling, Elizabeth T. Whitaker, Ethan Trewhitt, Li Ding, James Michaelis, Deborah L. McGuinness, James A. Hendler, Janardhan Rao Doppa, Charles Parker, Thomas G. Dietterich, Prasad Tadepalli, Weng-Keen Wong, Derek T. Green, Antons Rebguns, Diana F. Spears, Ugur Kuter, Geoffrey Levine, Gerald DeJong, Reid MacTavish, Santiago Ontañón, Jainarayan Radhakrishnan, Ashwin Ram, Hala Mostafa, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Victor R. Lesser, Zhexuan Song:
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration. ACM Trans. Intell. Syst. Technol. 3(4): 75:1-75:38 (2012) - 2011
- [j45]Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich:
Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning. AI Mag. 32(1): 35-50 (2011) - [j44]Xinlong Bao, Thomas G. Dietterich:
FolderPredictor: Reducing the cost of reaching the right folder. ACM Trans. Intell. Syst. Technol. 2(1): 8:1-8:23 (2011) - [j43]Ethan W. Dereszynski, Thomas G. Dietterich:
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns. ACM Trans. Sens. Networks 8(1): 3:1-3:36 (2011) - 2009
- [j42]Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Jonathan L. Herlocker:
Interacting meaningfully with machine learning systems: Three experiments. Int. J. Hum. Comput. Stud. 67(8): 639-662 (2009) - [j41]Jianqiang Shen, Thomas G. Dietterich:
A family of large margin linear classifiers and its application in dynamic environments. Stat. Anal. Data Min. 2(5-6): 328-345 (2009) - 2008
- [j40]Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern:
Learning first-order probabilistic models with combining rules. Ann. Math. Artif. Intell. 54(1-3): 223-256 (2008) - [j39]Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli:
Structured machine learning: the next ten years. Mach. Learn. 73(1): 3-23 (2008) - [j38]Natalia Larios, Hongli Deng, Wei Zhang, Matt Sarpola, Jenny Yuen, Robert Paasch, Andrew Moldenke, David A. Lytle, Salvador Ruiz-Correa, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich:
Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects. Mach. Vis. Appl. 19(2): 105-123 (2008) - 2007
- [j37]Sarabjot Singh Anand, Daniel Bahls, Catherina Burghart, Mark H. Burstein, Huajun Chen, John Collins, Thomas G. Dietterich, Jon Doyle, Chris Drummond, William Elazmeh, Christopher W. Geib, Judy Goldsmith, Hans W. Guesgen, Jim Hendler, Dietmar Jannach, Nathalie Japkowicz, Ulrich Junker, Gal A. Kaminka, Alfred Kobsa, Jérôme Lang, David B. Leake, Lundy Lewis, Gerard Ligozat, Sofus A. Macskassy, Drew V. McDermott, Ted Metzler, Bamshad Mobasher, Ullas Nambiar, Zaiqing Nie, Klas Orsvärn, Barry O'Sullivan, David V. Pynadath, Jochen Renz, Rita V. Rodríguez, Thomas Roth-Berghofer, Stefan Schulz, Rudi Studer, Yimin Wang, Michael P. Wellman:
AAAI-07 Workshop Reports. AI Mag. 28(4): 119-128 (2007) - 2005
- [j36]Valentina Bayer Zubek, Thomas G. Dietterich:
Integrating Learning from Examples into the Search for Diagnostic Policies. J. Artif. Intell. Res. 24: 263-303 (2005) - 2004
- [j35]Giorgio Valentini, Thomas G. Dietterich:
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods. J. Mach. Learn. Res. 5: 725-775 (2004) - 2000
- [j34]Thomas G. Dietterich:
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. J. Artif. Intell. Res. 13: 227-303 (2000) - [j33]Thomas G. Dietterich:
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Mach. Learn. 40(2): 139-157 (2000) - 1998
- [j32]Thomas G. Dietterich:
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Comput. 10(7): 1895-1923 (1998) - 1997
- [j31]Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez:
Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Artif. Intell. 89(1-2): 31-71 (1997) - [j30]Thomas G. Dietterich:
Machine-Learning Research. AI Mag. 18(4): 97-136 (1997) - [j29]Thomas G. Dietterich, Nicholas S. Flann:
Explanation-Based Learning and Reinforcement Learning: A Unified View. Mach. Learn. 28(2-3): 169-210 (1997) - 1996
- [j28]Thomas G. Dietterich:
Machine Learning. ACM Comput. Surv. 28(4es): 3 (1996) - [j27]Thomas G. Dietterich:
Editorial. Mach. Learn. 22(1-3): 5-6 (1996) - 1995
- [j26]Thomas G. Dietterich:
Overfitting and Undercomputing in Machine Learning. ACM Comput. Surv. 27(3): 326-327 (1995) - [j25]Thomas G. Dietterich, Ghulum Bakiri:
Solving Multiclass Learning Problems via Error-Correcting Output Codes. J. Artif. Intell. Res. 2: 263-286 (1995) - [j24]Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri:
A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping. Mach. Learn. 18(1): 51-80 (1995) - [j23]Dietrich Wettschereck, Thomas G. Dietterich:
An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. Mach. Learn. 19(1): 5-27 (1995) - 1994
- [j22]Hussein Almuallim, Thomas G. Dietterich:
Learning Boolean Concepts in the Presence of Many Irrelevant Features. Artif. Intell. 69(1-2): 279-305 (1994) - [j21]Ajay N. Jain, Thomas G. Dietterich, Richard H. Lathrop, David Chapman, Roger E. Critchlow Jr., Barr E. Bauer, Teresa A. Webster, Tomás Lozano-Pérez:
Compass: A shape-based machine learning tool for drug design. J. Comput. Aided Mol. Des. 8(6): 635-652 (1994) - [j20]Thomas G. Dietterich:
Editorial: New Editorial Board Members. Mach. Learn. 16(1-2): 5-6 (1994) - 1993
- [j19]Thomas G. Dietterich:
Editorial. Mach. Learn. 10: 5 (1993) - 1992
- [j18]Thomas G. Dietterich:
Editorial. Mach. Learn. 8: 105 (1992) - 1991
- [j17]Ashok K. Goel, Tom Bylander, B. Chandrasekaran, Thomas G. Dietterich, Richard M. Keller, Chris Tong:
Knowledge Compilation: A Symposium. IEEE Expert 6(2): 71-93 (1991) - 1990
- [j16]Thomas G. Dietterich:
Exploratory Research in Machine Learning. Mach. Learn. 5: 5-9 (1990) - 1989
- [j15]Thomas G. Dietterich:
News and Notes. Mach. Learn. 3: 373-375 (1989) - [j14]Thomas G. Dietterich:
News and Notes. Mach. Learn. 4: 107-109 (1989) - [j13]Nicholas S. Flann, Thomas G. Dietterich:
A Study of Explanation-Based Methods for Inductive Learning. Mach. Learn. 4: 187-226 (1989) - 1988
- [j12]David G. Ullman, Thomas G. Dietterich, Larry A. Stauffer:
A model of the mechanical design process based on empirical data. Artif. Intell. Eng. Des. Anal. Manuf. 2(1): 33-52 (1988) - [j11]Thomas G. Dietterich:
News and Notes. Mach. Learn. 3: 247-249 (1988) - 1987
- [j10]Thomas G. Dietterich:
News and Notes. Mach. Learn. 2(1): 75-96 (1987) - [j9]Thomas G. Dietterich:
News and Notes. Mach. Learn. 2(2): 191-192 (1987) - [j8]Thomas G. Dietterich:
News and Notes. Mach. Learn. 2(3): 277-278 (1987) - [j7]Thomas G. Dietterich:
News and Notes. Mach. Learn. 2(4): 397-398 (1987) - 1986
- [j6]Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins:
News and Notes. Mach. Learn. 1(2): 227-242 (1986) - [j5]Thomas G. Dietterich:
Learning at the Knowledge Level. Mach. Learn. 1(3): 287-316 (1986) - [j4]Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich:
News and Notes. Mach. Learn. 1(3): 355-358 (1986) - [j3]Thomas G. Dietterich:
News and Notes. Mach. Learn. 1(4): 453-454 (1986) - 1985
- [j2]Thomas G. Dietterich, Ryszard S. Michalski:
Discovering Patterns in Sequences of Events. Artif. Intell. 25(2): 187-232 (1985) - 1981
- [j1]Thomas G. Dietterich, Ryszard S. Michalski:
Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. Artif. Intell. 16(3): 257-294 (1981)
Conference and Workshop Papers
- 2022
- [c131]Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, Thomas G. Dietterich:
ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation. KDD 2022: 4892-4893 - 2021
- [c130]Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chades:
K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management. AAAI 2021: 14775-14784 - [c129]Yunye Gong, Xiao Lin, Yi Yao, Thomas G. Dietterich, Ajay Divakaran, Melinda T. Gervasio:
Confidence Calibration for Domain Generalization under Covariate Shift. ICCV 2021: 8938-8947 - [c128]Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, Thomas G. Dietterich:
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA). KDD 2021: 4145-4146 - 2020
- [c127]Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chadès:
Solving K-MDPs. ICAPS 2020: 110-118 - [c126]Tadesse Zemicheal, Thomas G. Dietterich:
Conditional mixture models for precipitation data quality control. COMPASS 2020: 13-21 - 2019
- [c125]Tadesse Zemicheal, Thomas G. Dietterich:
Anomaly detection in the presence of missing values for weather data quality control. COMPASS 2019: 65-73 - [c124]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. ICLR (Poster) 2019 - [c123]Dan Hendrycks, Mantas Mazeika, Thomas G. Dietterich:
Deep Anomaly Detection with Outlier Exposure. ICLR (Poster) 2019 - [c122]Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich:
Three-quarter Sibling Regression for Denoising Observational Data. IJCAI 2019: 5960-5966 - 2018
- [c121]Si Liu, Risheek Garrepalli, Alan Fern, Thomas G. Dietterich:
Can We Achieve Open Category Detection with Guarantees? AAAI Workshops 2018: 356-363 - [c120]Majid Alkaee Taleghan, Thomas G. Dietterich:
Efficient Exploration for Constrained MDPs. AAAI Spring Symposia 2018 - [c119]Thomas G. Dietterich, George Trimponias, Zhitang Chen:
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning. ICML 2018: 1261-1269 - [c118]Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks:
Open Category Detection with PAC Guarantees. ICML 2018: 3175-3184 - [c117]Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Ryan Wright, Alec Theriault, David W. Archer:
Feedback-Guided Anomaly Discovery via Online Optimization. KDD 2018: 2200-2209 - 2017
- [c116]Yann Dujardin, Tom Dietterich, Iadine Chades:
Three New Algorithms to Solve N-POMDPs. AAAI 2017: 4495-4501 - 2016
- [c115]Shubhomoy Das, Weng-Keen Wong, Thomas G. Dietterich, Alan Fern, Andrew Emmott:
Incorporating Expert Feedback into Active Anomaly Discovery. ICDM 2016: 853-858 - [c114]Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou:
Transductive Optimization of Top k Precision. IJCAI 2016: 1781-1787 - [c113]Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Das:
Finite Sample Complexity of Rare Pattern Anomaly Detection. UAI 2016 - 2015
- [c112]Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Z. Fern, Thomas G. Dietterich, Prasad Tadepalli:
Learning Greedy Policies for the Easy-First Framework. AAAI 2015: 2339-2345 - [c111]Sean McGregor, Hailey Buckingham, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer, Thomas G. Dietterich:
MDPVIS: An Interactive Visualization for Testing Markov Decision Processes. AAAI Fall Symposia 2015: 56-58 - [c110]Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich:
ℋC-search for structured prediction in computer vision. CVPR 2015: 4923-4932 - [c109]Yann Dujardin, Tom Dietterich, Iadine Chades:
α-min: A Compact Approximate Solver For Finite-Horizon POMDPs. IJCAI 2015: 2582-2588 - [c108]Jesse Hostetler, Alan Fern, Thomas G. Dietterich:
Progressive Abstraction Refinement for Sparse Sampling. UAI 2015: 365-374 - [c107]Mohammad S. Sorower, Michael Slater, Thomas G. Dietterich:
Improving Automated Email Tagging with Implicit Feedback. UIST 2015: 201-211 - [c106]Sean McGregor, Hailey Buckingham, Thomas G. Dietterich, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer:
Facilitating testing and debugging of Markov Decision Processes with interactive visualization. VL/HCC 2015: 53-61 - [c105]Sean McGregor, Hailey Buckingham, Thomas G. Dietterich, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer:
Facilitating testing and debugging of Markov Decision Processes with interactive visualization. VL/HCC 2015: 281-282 - 2014
- [c104]John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Z. Fern, Thomas G. Dietterich:
Learning Scripts as Hidden Markov Models. AAAI 2014: 1565-1571 - [c103]Jesse Hostetler, Alan Fern, Tom Dietterich:
State Aggregation in Monte Carlo Tree Search. AAAI 2014: 2446-2452 - [c102]Chao Ma, Janardhan Rao Doppa, John Walker Orr, Prashanth Mannem, Xiaoli Z. Fern, Thomas G. Dietterich, Prasad Tadepalli:
Prune-and-Score: Learning for Greedy Coreference Resolution. EMNLP 2014: 2115-2126 - [c101]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. ICML 2014: 1602-1610 - [c100]Li-Ping Liu, Thomas G. Dietterich:
Learnability of the Superset Label Learning Problem. ICML 2014: 1629-1637 - 2013
- [c99]Kiri L. Wagstaff, Nina L. Lanza, David R. Thompson, Thomas G. Dietterich, Martha S. Gilmore:
Guiding Scientific Discovery with Explanations Using DEMUD. AAAI 2013: 905-911 - [c98]Thomas G. Dietterich, Majid Alkaee Taleghan, Mark Crowley:
PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs. AAAI 2013: 1270-1276 - [c97]Daniel Sheldon, Andrew Farnsworth, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling:
Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. AAAI 2013: 1334-1340 - [c96]Michael Lam, Janardhan Rao Doppa, Xu Hu, Sinisa Todorovic, Thomas G. Dietterich, Abigail Reft, Marymegan Daly:
Learning to Detect Basal Tubules of Nematocysts in SEM Images. ICCV Workshops 2013: 190-196 - [c95]Xu Hu, Michael Lam, Sinisa Todorovic, Thomas G. Dietterich, Maureen A. OLeary, Andrea L. Cirranello, Nancy B. Simmons, Paúl M. Velazco:
Zero-Shot Learning and Detection of Teeth in Images of Bat Skulls. ICCV Workshops 2013: 203-209 - [c94]Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich:
Approximate Inference in Collective Graphical Models. ICML (3) 2013: 1004-1012 - [c93]Ted E. Senator, Henry G. Goldberg, Alex Memory, William T. Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A. Bader, Edmond Chow, Irfan A. Essa, Joshua Jones, Vinay Bettadapura, Duen Horng Chau, Oded Green, Oguz Kaya, Anita Zakrzewska, Erica Briscoe, Rudolph L. Mappus IV, Robert McColl, Lora Weiss, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, Shubhomoy Das, Andrew Emmott, Jed Irvine, Jay Yoon Lee, Danai Koutra, Christos Faloutsos, Daniel D. Corkill, Lisa Friedland, Amanda Gentzel, David D. Jensen:
Detecting insider threats in a real corporate database of computer usage activity. KDD 2013: 1393-1401 - 2012
- [c92]Kshitij Judah, Alan Paul Fern, Thomas Glenn Dietterich:
Active Imitation Learning via Reduction to I.I.D. Active Learning. AAAI Fall Symposium: Robots Learning Interactively from Human Teachers 2012 - [c91]Thomas G. Dietterich, Ethan W. Dereszynski, Rebecca A. Hutchinson, Dan Sheldon:
Machine learning for computational sustainability. IGCC 2012: 1 - [c90]Li-Ping Liu, Thomas G. Dietterich:
A Conditional Multinomial Mixture Model for Superset Label Learning. NIPS 2012: 557-565 - [c89]Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern:
Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games. UAI 2012: 367-376 - [c88]Kshitij Judah, Alan Fern, Thomas G. Dietterich:
Active Imitation Learning via Reduction to I.I.D. Active Learning. UAI 2012: 428-437 - 2011
- [c87]Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich:
Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI 2011: 1343-1348 - [c86]Ethan W. Dereszynski, Jesse Hostetler, Alan Fern, Thomas G. Dietterich, Thao-Trang Hoang, Mark Udarbe:
Learning Probabilistic Behavior Models in Real-Time Strategy Games. AIIDE 2011 - [c85]Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, John Walker Orr, Prasad Tadepalli, Xiaoli Z. Fern:
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions. NIPS 2011: 1053-1061 - [c84]Daniel Sheldon, Thomas G. Dietterich:
Collective Graphical Models. NIPS 2011: 1161-1169 - [c83]Natalia Larios Delgado, Junyuan Lin, Mengzi Zhang, David A. Lytle, Andrew Moldenke, Linda G. Shapiro, Thomas G. Dietterich:
Stacked spatial-pyramid kernel: An object-class recognition method to combine scores from random trees. WACV 2011: 329-335 - [c82]Janardhan Rao Doppa, Shahed Sorower, Mohammad NasrEsfahani, John Walker Orr, Thomas G. Dietterich, Xiaoli Z. Fern, Prasad Tadepalli, Jed Irvine:
Learning Rules from Incomplete Examples via Implicit Mention Models. ACML 2011: 197-212 - 2010
- [c81]Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Dietterich:
Reinforcement Learning Via Practice and Critique Advice. AAAI 2010: 481-486 - [c80]Carlos Jensen, Heather Lonsdale, Eleanor Wynn, Jill Cao, Michael Slater, Thomas G. Dietterich:
The life and times of files and information: a study of desktop provenance. CHI 2010: 767-776 - [c79]Natalia Larios, Bilge Soran, Linda G. Shapiro, Gonzalo Martínez-Muñoz, Junyuan Lin, Thomas G. Dietterich:
Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification. ICPR 2010: 2624-2627 - 2009
- [c78]Thomas G. Dietterich:
Machine Learning and Ecosystem Informatics: Challenges and Opportunities. ACML 2009: 1-5 - [c77]Gonzalo Martínez-Muñoz, Natalia Larios Delgado, Eric N. Mortensen, Wei Zhang, Asako Yamamuro, Robert Paasch, Nadia Payet, David A. Lytle, Linda G. Shapiro, Sinisa Todorovic, Andrew Moldenke, Thomas G. Dietterich:
Dictionary-free categorization of very similar objects via stacked evidence trees. CVPR 2009: 549-556 - [c76]Xiaoqin Zhang, Sung Wook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek T. Green, Jinhong K. Guo, Ugur Kuter, Geoffrey Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontañón, Charles Parker, Jainarayan Radhakrishnan, Antons Rebguns, Bhavesh Shrestha, Zhexuan Song, Ethan Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor R. Lesser, Deborah L. McGuinness, Ashwin Ram, Diana F. Spears, Prasad Tadepalli, Elizabeth T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, Kenneth R. Whitebread:
An Ensemble Learning and Problem Solving Architecture for Airspace Management. IAAI 2009 - [c75]Wei Zhang, Akshat Surve, Xiaoli Z. Fern, Thomas G. Dietterich:
Learning non-redundant codebooks for classifying complex objects. ICML 2009: 1241-1248 - [c74]Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics and Sustainability. IJCAI 2009: 8-13 - [c73]Jianqiang Shen, Jed Irvine, Xinlong Bao, Michael Goodman, Stephen Kolibaba, Anh Tran, Fredric Carl, Brenton Kirschner, Simone Stumpf, Thomas G. Dietterich:
Detecting and correcting user activity switches: algorithms and interfaces. IUI 2009: 117-126 - [c72]Jianqiang Shen, Erin Fitzhenry, Thomas G. Dietterich:
Discovering frequent work procedures from resource connections. IUI 2009: 277-286 - [c71]Jianqiang Shen, Thomas G. Dietterich:
A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. SDM 2009: 164-172 - 2008
- [c70]Thomas G. Dietterich, Xinlong Bao:
Integrating Multiple Learning Components through Markov Logic. AAAI 2008: 622-627 - [c69]Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich:
Automatic discovery and transfer of MAXQ hierarchies. ICML 2008: 648-655 - [c68]Wei Zhang, Thomas G. Dietterich:
Learning visual dictionaries and decision lists for object recognition. ICPR 2008: 1-4 - [c67]Michael Wynkoop, Thomas G. Dietterich:
Learning MDP Action Models Via Discrete Mixture Trees. ECML/PKDD (2) 2008: 597-612 - 2007
- [c66]Simone Stumpf, Margaret M. Burnett, Thomas G. Dietterich:
Improving Intelligent Assistants for Desktop Activities. AAAI Spring Symposium: Interaction Challenges for Intelligent Assistants 2007: 119-121 - [c65]Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics. ALT 2007: 10-11 - [c64]Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro:
Principal Curvature-Based Region Detector for Object Recognition. CVPR 2007 - [c63]Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics. Discovery Science 2007: 9-25 - [c62]Simone Stumpf, Margaret M. Burnett, Thomas G. Dietterich:
Improving Intelligent Assistants for Desktop Activities. Interaction Challenges for Intelligent Assistants 2007: 119-121 - [c61]Jianqiang Shen, Lida Li, Thomas G. Dietterich:
Real-Time Detection of Task Switches of Desktop Users. IJCAI 2007: 2868-2873 - [c60]Simone Stumpf, Vidya Rajaram, Lida Li, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Russell Drummond, Jonathan L. Herlocker:
Toward harnessing user feedback for machine learning. IUI 2007: 82-91 - [c59]Jianqiang Shen, Thomas G. Dietterich:
Active EM to reduce noise in activity recognition. IUI 2007: 132-140 - [c58]Ethan W. Dereszynski, Thomas G. Dietterich:
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. UAI 2007: 75-82 - [c57]Natalia Larios, Hongli Deng, Wei Zhang, Matt Sarpola, Jenny Yuen, Robert Paasch, Andrew Moldenke, David A. Lytle, Ruiz Correa, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich:
Automated Insect Identification through Concatenated Histograms of Local Appearance Features. WACV 2007: 26 - 2006
- [c56]Hongli Deng, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich:
Reinforcement Matching Using Region Context. CVPR Workshops 2006: 11 - [c55]Wei Zhang, Hongli Deng, Thomas G. Dietterich, Eric N. Mortensen:
A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. ICPR (1) 2006: 778-782 - [c54]Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jonathan L. Herlocker:
A hybrid learning system for recognizing user tasks from desktop activities and email messages. IUI 2006: 86-92 - [c53]Xinlong Bao, Jonathan L. Herlocker, Thomas G. Dietterich:
Fewer clicks and less frustration: reducing the cost of reaching the right folder. IUI 2006: 178-185 - 2005
- [c52]Simone Stumpf, Xinlong Bao, Anton N. Dragunov, Thomas G. Dietterich, Jonathan L. Herlocker, Kevin Johnsrude, Lida Li, Jianqiang Shen:
The TaskTracker System. AAAI 2005: 1712-1713 - [c51]Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar:
Learning first-order probabilistic models with combining rules. ICML 2005: 609-616 - [c50]Anton N. Dragunov, Thomas G. Dietterich, Kevin Johnsrude, Matthew R. McLaughlin, Lida Li, Jonathan L. Herlocker:
TaskTracer: a desktop environment to support multi-tasking knowledge workers. IUI 2005: 75-82 - [c49]Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich:
Learning from Sparse Data by Exploiting Monotonicity Constraints. UAI 2005: 18-26 - 2004
- [c48]Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov:
Training conditional random fields via gradient tree boosting. ICML 2004 - [c47]Pengcheng Wu, Thomas G. Dietterich:
Improving SVM accuracy by training on auxiliary data sources. ICML 2004 - 2003
- [c46]Giorgio Valentini, Thomas G. Dietterich:
Low Bias Bagged Support Vector Machines. ICML 2003: 752-759 - [c45]Xin Wang, Thomas G. Dietterich:
Model-based Policy Gradient Reinforcement Learning. ICML 2003: 776-783 - 2002
- [c44]Dídac Busquets, Ramón López de Mántaras, Carles Sierra, Thomas G. Dietterich:
A Multi-agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation. CCIA 2002: 269-281 - [c43]Valentina Bayer Zubek, Thomas G. Dietterich:
Pruning Improves Heuristic Search for Cost-Sensitive Learning. ICML 2002: 19-26 - [c42]Thomas G. Dietterich, Dídac Busquets, Ramón López de Mántaras, Carles Sierra:
Action Refinement in Reinforcement Learning by Probability Smoothing. ICML 2002: 107-114 - [c41]Giorgio Valentini, Thomas G. Dietterich:
Bias-Variance Analysis and Ensembles of SVM. Multiple Classifier Systems 2002: 222-231 - [c40]Thomas G. Dietterich:
Machine Learning for Sequential Data: A Review. SSPR/SPR 2002: 15-30 - 2001
- [c39]Thomas G. Dietterich, Xin Wang:
Support Vectors for Reinforcement Learning. ECML 2001: 600 - [c38]Thomas G. Dietterich, Xin Wang:
Batch Value Function Approximation via Support Vectors. NIPS 2001: 1491-1498 - [c37]Xin Wang, Thomas G. Dietterich:
Stabilizing Value Function Approximation with the BFBP Algorithm. NIPS 2001: 1587-1594 - [c36]Thomas G. Dietterich, Xin Wang:
Support Vectors for Reinforcement Learning. PKDD 2001: 492 - 2000
- [c35]Thomas G. Dietterich:
The Divide-and-Conquer Manifesto. ALT 2000: 13-26 - [c34]Eric Chown, Thomas G. Dietterich:
A Divide and Conquer Approach to Learning from Prior Knowledge. ICML 2000: 143-150 - [c33]Dragos D. Margineantu, Thomas G. Dietterich:
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. ICML 2000: 583-590 - [c32]Tony Fountain, Thomas G. Dietterich, Bill Sudyka:
Mining IC test data to optimize VLSI testing. KDD 2000: 18-25 - [c31]Thomas G. Dietterich:
Ensemble Methods in Machine Learning. Multiple Classifier Systems 2000: 1-15 - [c30]Valentina Bayer Zubek, Thomas G. Dietterich:
A POMDP Approximation Algorithm That Anticipates the Need to Observe. PRICAI 2000: 521-532 - [c29]Thomas G. Dietterich:
An Overview of MAXQ Hierarchical Reinforcement Learning. SARA 2000: 26-44 - 1999
- [c28]Thomas G. Dietterich:
State Abstraction in MAXQ Hierarchical Reinforcement Learning. NIPS 1999: 994-1000 - 1998
- [c27]Thomas G. Dietterich:
The MAXQ Method for Hierarchical Reinforcement Learning. ICML 1998: 118-126 - 1997
- [c26]Dragos D. Margineantu, Thomas G. Dietterich:
Pruning Adaptive Boosting. ICML 1997: 211-218 - [c25]Prasad Tadepalli, Thomas G. Dietterich:
Hierarchical Explanation-Based Reinforcement Learning. ICML 1997: 358-366 - 1996
- [c24]Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour:
Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104 - 1995
- [c23]Thomas G. Dietterich, Nicholas S. Flann:
Explanation-Based Learning and Reinforcement Learning: A Unified View. ICML 1995: 176-184 - [c22]Eun Bae Kong, Thomas G. Dietterich:
Error-Correcting Output Coding Corrects Bias and Variance. ICML 1995: 313-321 - [c21]Wei Zhang, Thomas G. Dietterich:
A Reinforcement Learning Approach to job-shop Scheduling. IJCAI 1995: 1114-1120 - [c20]Wei Zhang, Thomas G. Dietterich:
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. NIPS 1995: 1024-1030 - 1993
- [c19]Dietrich Wettschereck, Thomas G. Dietterich:
Locally Adaptive Nearest Neighbor Algorithms. NIPS 1993: 184-191 - [c18]Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez:
A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction. NIPS 1993: 216-223 - [c17]Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore:
Memory-Based Methods for Regression and Classification. NIPS 1993: 1165-1166 - 1992
- [c16]Hussein Almuallim, Thomas G. Dietterich:
On Learning More Concepts. ML 1992: 11-19 - 1991
- [c15]Hussein Almuallim, Thomas G. Dietterich:
Learning with Many Irrelevant Features. AAAI 1991: 547-552 - [c14]Thomas G. Dietterich, Ghulum Bakiri:
Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs. AAAI 1991: 572-577 - [c13]Giuseppe Cerbone, Thomas G. Dietterich:
Knowledge Compilation to Speed Up Numerical Optimisation. AI*IA 1991: 208-217 - [c12]Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu:
Machine Learning in Engineering Automation. ML 1991: 577-580 - [c11]Giuseppe Cerbone, Thomas G. Dietterich:
Knowledge Compilation to Speed Up Numerical Optimization. ML 1991: 600-604 - [c10]Dietrich Wettschereck, Thomas G. Dietterich:
Improving the Performance of Radial Basis Function Networks by Learning Center Locations. NIPS 1991: 1133-1140 - 1990
- [c9]Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri:
A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. ML 1990: 24-31 - 1989
- [c8]Ritchey A. Ruff, Thomas G. Dietterich:
What Good Are Experiments?. ML 1989: 109-112 - [c7]Thomas G. Dietterich:
Limitations on Inductive Learning. ML 1989: 124-128 - 1988
- [c6]Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich:
An Efficient ATMS for Equivalence Relations. AAAI 1988: 182-187 - 1987
- [c5]Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon:
Forward Chaining Logic Programming with the ATMS. AAAI 1987: 24-29 - 1986
- [c4]Nicholas S. Flann, Thomas G. Dietterich:
Selecting Appropriate Representations for Learning from Examples. AAAI 1986: 460-466 - 1984
- [c3]Thomas G. Dietterich:
Learning About Systems That Contain State Variables. AAAI 1984: 96-100 - 1980
- [c2]Thomas G. Dietterich:
Applying General Induction Methods to the Card Game Eleusis. AAAI 1980: 218-220 - 1979
- [c1]Thomas G. Dietterich, Ryszard S. Michalski:
Learning and Generalization of Characteristic Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. IJCAI 1979: 223-231
Parts in Books or Collections
- 2010
- [p2]Paul Barford, Marc Dacier, Thomas G. Dietterich, Matt Fredrikson, Jonathon T. Giffin, Sushil Jajodia, Somesh Jha, Jason H. Li, Peng Liu, Peng Ning, Xinming Ou, Dawn Song, Laura Strater, Vipin Swarup, George P. Tadda, C. Wang, John Yen:
Cyber SA: Situational Awareness for Cyber Defense. Cyber Situational Awareness 2010: 3-13 - [p1]Thomas G. Dietterich, Xinlong Bao, Victoria Keiser, Jianqiang Shen:
Machine Learning Methods for High Level Cyber Situation Awareness. Cyber Situational Awareness 2010: 227-247
Editorship
- 2008
- [e5]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007. Dagstuhl Seminar Proceedings 07161, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - 2006
- [e4]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005. Dagstuhl Seminar Proceedings 05051, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006 [contents] - 2001
- [e3]Todd K. Leen, Thomas G. Dietterich, Volker Tresp:
Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA. MIT Press 2001 [contents] - [e2]Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani:
Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]. MIT Press 2001 [contents] - 1990
- [e1]Howard E. Shrobe, Thomas G. Dietterich, William R. Swartout:
Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, USA, July 29 - August 3, 1990, 2 Volumes. AAAI Press / The MIT Press 1990, ISBN 0-262-51057-X [contents]
Informal and Other Publications
- 2023
- [i36]George Trimponias, Thomas G. Dietterich:
Reinforcement Learning with Exogenous States and Rewards. CoRR abs/2303.12957 (2023) - 2022
- [i35]Kiri L. Wagstaff, Thomas G. Dietterich:
Hidden Heterogeneity: When to Choose Similarity-Based Calibration. CoRR abs/2202.01840 (2022) - [i34]Thomas G. Dietterich, Alexander Guyer:
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods. CoRR abs/2203.02486 (2022) - [i33]Thomas G. Dietterich, Jesse Hostetler:
Conformal Prediction Intervals for Markov Decision Process Trajectories. CoRR abs/2206.04860 (2022) - [i32]Risheek Garrepalli, Alan Fern, Thomas G. Dietterich:
Oracle Analysis of Representations for Deep Open Set Detection. CoRR abs/2209.11350 (2022) - [i31]Alexander Guyer, Thomas G. Dietterich:
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target. CoRR abs/2211.16462 (2022) - 2021
- [i30]Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich:
Three-quarter Sibling Regression for Denoising Observational Data. CoRR abs/2101.00074 (2021) - [i29]Yunye Gong, Xiao Lin, Yi Yao, Thomas G. Dietterich, Ajay Divakaran, Melinda T. Gervasio:
Confidence Calibration for Domain Generalization under Covariate Shift. CoRR abs/2104.00742 (2021) - [i28]Erich Merrill, Stefan Lee, Fuxin Li, Thomas G. Dietterich, Alan Fern:
Deep Convolution for Irregularly Sampled Temporal Point Clouds. CoRR abs/2105.00137 (2021) - 2020
- [i27]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. CoRR abs/2009.11732 (2020) - 2019
- [i26]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. CoRR abs/1903.12261 (2019) - 2018
- [i25]Thomas G. Dietterich, George Trimponias, Zhitang Chen:
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning. CoRR abs/1806.01584 (2018) - [i24]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. CoRR abs/1807.01697 (2018) - [i23]Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks:
Open Category Detection with PAC Guarantees. CoRR abs/1808.00529 (2018) - [i22]Thomas G. Dietterich, Tadesse Zemicheal:
Anomaly Detection in the Presence of Missing Values. CoRR abs/1809.01605 (2018) - [i21]John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Z. Fern, Thomas G. Dietterich:
Learning Scripts as Hidden Markov Models. CoRR abs/1809.03680 (2018) - [i20]Thomas G. Dietterich:
Robust Artificial Intelligence and Robust Human Organizations. CoRR abs/1811.10840 (2018) - [i19]Dan Hendrycks, Mantas Mazeika, Thomas G. Dietterich:
Deep Anomaly Detection with Outlier Exposure. CoRR abs/1812.04606 (2018) - 2017
- [i18]Sean McGregor, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer, Thomas G. Dietterich:
Factoring Exogenous State for Model-Free Monte Carlo. CoRR abs/1703.09390 (2017) - [i17]Sean McGregor, Rachel Houtman, Claire A. Montgomery, Ronald A. Metoyer, Thomas G. Dietterich:
Fast Optimization of Wildfire Suppression Policies with SMAC. CoRR abs/1703.09391 (2017) - [i16]Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md Amran Siddiqui:
Incorporating Feedback into Tree-based Anomaly Detection. CoRR abs/1708.09441 (2017) - 2015
- [i15]Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Weng-Keen Wong:
Sequential Feature Explanations for Anomaly Detection. CoRR abs/1503.00038 (2015) - [i14]Andrew Emmott, Shubhomoy Das, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong:
Systematic Construction of Anomaly Detection Benchmarks from Real Data. CoRR abs/1503.01158 (2015) - [i13]Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou:
Transductive Optimization of Top k Precision. CoRR abs/1510.05976 (2015) - 2014
- [i12]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. CoRR abs/1405.5156 (2014) - 2012
- [i11]Ethan W. Dereszynski, Thomas G. Dietterich:
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. CoRR abs/1206.5250 (2012) - [i10]Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich:
Learning from Sparse Data by Exploiting Monotonicity Constraints. CoRR abs/1207.1364 (2012) - [i9]Kshitij Judah, Alan Fern, Thomas G. Dietterich:
Active Imitation Learning via Reduction to I.I.D. Active Learning. CoRR abs/1210.4876 (2012) - [i8]Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern:
Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games. CoRR abs/1210.4880 (2012) - 2011
- [i7]Valentina Bayer Zubek, Thomas G. Dietterich:
Integrating Learning from Examples into the Search for Diagnostic Policies. CoRR abs/1109.2127 (2011) - 2007
- [i6]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2005
- [i5]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - [i4]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - 1999
- [i3]Thomas G. Dietterich:
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. CoRR cs.LG/9905014 (1999) - [i2]Thomas G. Dietterich:
State Abstraction in MAXQ Hierarchical Reinforcement Learning. CoRR cs.LG/9905015 (1999) - 1995
- [i1]Thomas G. Dietterich, Ghulum Bakiri:
Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR cs.AI/9501101 (1995)
Coauthor Index
Alan Fern
aka: Alan Paul Fern
aka: Alan Paul Fern
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