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ORCIDVasant G. Honavar
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- affiliation: Pennsylvania State University, University Park, Pennsylvania, USA
- affiliation: Iowa State University, Ames, Iowa, USA
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2020 – today
- 2025
[c186]Erik Rauer, Samik Basu, Vasant G. Honavar:
Checking Consistency of CP-Theory Preferences in Polynomial Time. AAAI 2025: 15126-15133- [c185]Teng Xiao, Yige Yuan, Zhengyu Chen, Mingxiao Li, Shangsong Liang, Zhaochun Ren, Vasant G. Honavar:
SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters. ICLR 2025 - [c184]Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar:
On a Connection Between Imitation Learning and RLHF. ICLR 2025 - [c183]Huaisheng Zhu, Teng Xiao, Vasant G. Honavar:
DSPO: Direct Score Preference Optimization for Diffusion Model Alignment. ICLR 2025 - [c182]Abhishek Dalvi
, Vasant G. Honavar:
Hyperdimensional Representation Learning for Node Classification and Link Prediction. WSDM 2025: 88-97 - [i43]Weijieying Ren, Tianxiang Zhao, Yuqing Huang, Vasant G. Honavar:
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions. CoRR abs/2501.03540 (2025) - [i42]Abhishek Dalvi, Neil Ashtekar, Vasant G. Honavar:
C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data. CoRR abs/2501.16562 (2025) - [i41]Teng Xiao, Yige Yuan, Zhengyu Chen, Mingxiao Li, Shangsong Liang, Zhaochun Ren, Vasant G. Honavar:
SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters. CoRR abs/2502.00883 (2025) - [i40]Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar:
On a Connection Between Imitation Learning and RLHF. CoRR abs/2503.05079 (2025) - [i39]Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood:
A practical guide to machine learning interatomic potentials - Status and future. CoRR abs/2503.09814 (2025) - 2024
- [c181]Junjie Liang, Weijieying Ren, Hanifi Sahar, Vasant G. Honavar:
Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data. AAAI 2024: 13736-13743 - [c180]Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar:
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules. BIBM 2024: 1250-1256 - [c179]Abhishek Dalvi, Neil Ashtekar, Vasant G. Honavar:
Causal Matching using Random Hyperplane Tessellations. CLeaR 2024: 688-702 - [c178]Teng Xiao, Mingxiao Li, Yige Yuan, Huaisheng Zhu, Chao Cui, Vasant G. Honavar:
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective. EMNLP 2024: 13413-13426 - [c177]Weijieying Ren, Xiaoting Li, Huiyuan Chen, Vineeth Rakesh, Zhuoyi Wang, Mahashweta Das, Vasant G. Honavar:
TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules. ICML 2024 - [c176]Teng Xiao, Huaisheng Zhu, Zhiwei Zhang, Zhimeng Guo, Charu C. Aggarwal, Suhang Wang, Vasant G. Honavar:
Efficient Contrastive Learning for Fast and Accurate Inference on Graphs. ICML 2024 - [c175]Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant G. Honavar:
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment. NeurIPS 2024 - [c174]Weijieying Ren, Vasant G. Honavar:
EsaCL: An Efficient Continual Learning Algorithm. SDM 2024: 163-171 - [i38]Weijieying Ren, Vasant G. Honavar:
EsaCL: Efficient Continual Learning of Sparse Models. CoRR abs/2401.05667 (2024) - [i37]Abhishek Dalvi, Vasant G. Honavar:
One-Shot Graph Representation Learning Using Hyperdimensional Computing. CoRR abs/2402.17073 (2024) - [i36]Huaisheng Zhu, Teng Xiao, Vasant G. Honavar:
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs. CoRR abs/2403.07179 (2024) - [i35]Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar:
MolBind: Multimodal Alignment of Language, Molecules, and Proteins. CoRR abs/2403.08167 (2024) - [i34]Abhishek Dalvi, Neil Ashtekar, Vasant G. Honavar:
Causal Effect Estimation Using Random Hyperplane Tessellations. CoRR abs/2404.10907 (2024) - [i33]Ryan Jacobs, Maciej P. Polak, Lane E. Schultz, Hamed Mahdavi, Vasant G. Honavar, Dane Morgan:
Regression with Large Language Models for Materials and Molecular Property Prediction. CoRR abs/2409.06080 (2024) - [i32]Teng Xiao, Mingxiao Li, Yige Yuan, Huaisheng Zhu, Chao Cui, Vasant G. Honavar:
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective. CoRR abs/2410.10093 (2024) - [i31]Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar:
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules. CoRR abs/2411.10821 (2024) - [i30]Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant G. Honavar:
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment. CoRR abs/2412.14516 (2024) - 2023
- [j59]Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant G. Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang:
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities. IEEE J. Biomed. Health Informatics 27(7): 3645-3656 (2023) - [c173]Samik Basu, Vasant G. Honavar, Ganesh Ram Santhanam, Jia Tao:
Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries. ECAI 2023: 206-213 - [c172]Neil Ashtekar, Vasant G. Honavar:
A Simple, Fast Algorithm for Continual Learning from High-Dimensional Data. Tiny Papers @ ICLR 2023 - [i29]Samik Basu, Vasant G. Honavar, Ganesh Ram Santhanam, Jia Tao:
Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries. CoRR abs/2307.16307 (2023) - 2022
- [j58]Michael G. Kallitsis, Rupesh Prajapati, Vasant G. Honavar, Dinghao Wu, John Yen:
Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes. IEEE Trans. Inf. Forensics Secur. 17: 3611-3625 (2022) - [i28]Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant G. Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang:
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities. CoRR abs/2211.02789 (2022) - 2021
- [c171]Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant G. Honavar:
Longitudinal Deep Kernel Gaussian Process Regression. AAAI 2021: 8556-8564 - [c170]Rupesh Prajapati, Vasant G. Honavar, Dinghao Wu, John Yen, Michalis Kallitsis:
Shedding light into the darknet: scanning characterization and detection of temporal changes. CoNEXT 2021: 469-470 - [c169]Junjie Liang, Wenbo Guo, Tongbo Luo, Vasant G. Honavar, Gang Wang, Xinyu Xing:
FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data. NDSS 2021 - [c168]Tsung-Yu Hsieh, Yiwei Sun, Suhang Wang, Vasant G. Honavar:
Functional Autoencoders for Functional Data Representation Learning. SDM 2021: 666-674 - [c167]Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant G. Honavar:
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals. WSDM 2021: 607-615 - [c166]Tsung-Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G. Honavar:
SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series. WWW 2021: 2270-2280 - [i27]Michalis Kallitsis, Vasant G. Honavar, Rupesh Prajapati, Dinghao Wu, John Yen:
Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes. CoRR abs/2108.00079 (2021) - 2020
- [j57]Cunliang Geng, Yong Jung, Nicolas Renaud, Vasant G. Honavar, Alexandre M. J. J. Bonvin, Li C. Xue:
iScore: a novel graph kernel-based function for scoring protein-protein docking models. Bioinform. 36(1): 112-121 (2020) - [j56]Manish Parashar, Anthony Simonet, Ivan Rodero, Forough Ghahramani, Grace Agnew, Ron Jantz, Vasant G. Honavar:
The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research. Comput. Sci. Eng. 22(3): 79-92 (2020) - [j55]Nicolas Renaud, Yong Jung, Vasant G. Honavar, Cunliang Geng, Alexandre M. J. J. Bonvin, Li C. Xue:
iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines. SoftwareX 11: 100462 (2020) - [c165]Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant G. Honavar:
LMLFM: Longitudinal Multi-Level Factorization Machine. AAAI 2020: 4811-4818 - [c164]Aria Khademi, Vasant G. Honavar:
Algorithmic Bias in Recidivism Prediction: A Causal Perspective (Student Abstract). AAAI 2020: 13839-13840 - [c163]Thanh Le, Vasant G. Honavar:
Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data. FODS 2020: 183-188 - [c162]Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant G. Honavar:
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. WWW 2020: 673-683 - [i26]Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant G. Honavar:
Longitudinal Deep Kernel Gaussian Process Regression. CoRR abs/2005.11770 (2020) - [i25]Aria Khademi, Vasant G. Honavar:
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution. CoRR abs/2008.00357 (2020) - [i24]Christopher Seto, Aria Khademi, Corina Graif, Vasant G. Honavar:
Commuting Network Spillovers and COVID-19 Deaths Across US Counties. CoRR abs/2010.01101 (2020) - [i23]Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant G. Honavar:
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals. CoRR abs/2011.11631 (2020)
2010 – 2019
- 2019
- [c161]Saravanan Kandasamy, Arnab Bhattacharyya, Vasant G. Honavar:
Minimum Intervention Cover of a Causal Graph. AAAI 2019: 2876-2885 - [c160]Tsung-Yu Hsieh, Yiwei Sun, Suhang Wang, Vasant G. Honavar:
Adaptive Structural Co-regularization for Unsupervised Multi-view Feature Selection. ICBK 2019: 87-96 - [c159]Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant G. Honavar:
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding. IJCAI 2019: 3527-3533 - [c158]Sanghack Lee, Vasant G. Honavar:
Towards Robust Relational Causal Discovery. UAI 2019: 345-355 - [c157]Yimin Zhou, Yiwei Sun, Vasant G. Honavar:
Improving Image Captioning by Leveraging Knowledge Graphs. WACV 2019: 283-293 - [c156]Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar:
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. WWW 2019: 2907-2914 - [i22]Yimin Zhou, Yiwei Sun, Vasant G. Honavar:
Improving Image Captioning by Leveraging Knowledge Graphs. CoRR abs/1901.08942 (2019) - [i21]Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar:
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. CoRR abs/1903.11719 (2019) - [i20]Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant G. Honavar:
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding. CoRR abs/1909.01084 (2019) - [i19]Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant G. Honavar:
Node Injection Attacks on Graphs via Reinforcement Learning. CoRR abs/1909.06543 (2019) - [i18]Thanh Le, Vasant G. Honavar:
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario. CoRR abs/1909.11630 (2019) - [i17]Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant G. Honavar:
LMLFM: Longitudinal Multi-Level Factorization Machine. CoRR abs/1911.04062 (2019) - [i16]Aria Khademi, Vasant G. Honavar:
Algorithmic Bias in Recidivism Prediction: A Causal Perspective. CoRR abs/1911.10640 (2019) - [i15]Sanghack Lee, Vasant G. Honavar:
Towards Robust Relational Causal Discovery. CoRR abs/1912.02390 (2019) - 2018
- [j54]Jinlong Hu, Junjie Liang, Yuezhen Kuang, Vasant G. Honavar:
A user similarity-based Top-N recommendation approach for mobile in-application advertising. Expert Syst. Appl. 111: 51-60 (2018) - [c155]Mostafa M. Abbas, Thanh Le, Halima Bensmail, Vasant G. Honavar, Yasser El-Manzalawy:
Microbiomarkers Discovery in Inflammatory Bowel Diseases using Network-Based Feature Selection. BCB 2018: 172-177 - [c154]Aria Khademi, Yasser El-Manzalawy, Orfeu M. Buxton, Vasant G. Honavar:
Toward personalized sleep-wake prediction from actigraphy. BHI 2018: 414-417 - [c153]Junjie Liang, Jinlong Hu, Shoubin Dong, Vasant G. Honavar:
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems. IEEE BigData 2018: 1052-1058 - [c152]Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant G. Honavar:
Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement. ICDM Workshops 2018: 1006-1013 - [c151]Tsung-Yu Hsieh, Yasser El-Manzalawy, Yiwei Sun, Vasant G. Honavar:
Compositional Stochastic Average Gradient for Machine Learning and Related Applications. IDEAL (1) 2018: 740-752 - [c150]Shlomit Gur, Vasant G. Honavar:
PATENet: Pairwise Alignment of Time Evolving Networks. MLDM (1) 2018: 85-98 - [e9]Robert Dyer, Vasant G. Honavar, Gary T. Leavens, Hoan Anh Nguyen, Tien N. Nguyen, Hridesh Rajan:
Proceedings of the 1st ACM SIGSOFT International Workshop on Automated Specification Inference, WASPI@ESEC/SIGSOFT FSE, Lake Buena Vista, FL, USA, November 9, 2018. ACM 2018, ISBN 978-1-4503-6057-9 [contents] - [r3]Cornelia Caragea, Vasant G. Honavar:
Machine Learning in Computational Biology. Encyclopedia of Database Systems (2nd ed.) 2018 - [i14]Tsung-Yu Hsieh, Yasser El-Manzalawy, Yiwei Sun, Vasant G. Honavar:
Compositional Stochastic Average Gradient for Machine Learning and Related Applications. CoRR abs/1809.01225 (2018) - [i13]Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant G. Honavar:
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement. CoRR abs/1811.02616 (2018) - [i12]Junjie Liang, Jinlong Hu, Shoubin Dong, Vasant G. Honavar:
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems. CoRR abs/1812.04109 (2018) - 2017
- [j53]Li C. Xue, João P. G. L. M. Rodrigues, Drena Dobbs, Vasant G. Honavar, Alexandre M. J. J. Bonvin:
Template-based protein-protein docking exploiting pairwise interfacial residue restraints. Briefings Bioinform. 18(3): 458-466 (2017) - [c149]Yasser El-Manzalawy, Orfeu M. Buxton, Vasant G. Honavar:
Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. BIBM 2017: 718-723 - [c148]Sanghack Lee, Vasant G. Honavar:
Self-Discrepancy Conditional Independence Test. UAI 2017 - [c147]Sanghack Lee, Vasant G. Honavar:
Towards Conditional Independence Test for Relational Data. UAI 2017 - [i11]Solon Barocas, Elizabeth Bradley, Vasant G. Honavar, Foster J. Provost:
Big Data, Data Science, and Civil Rights. CoRR abs/1706.03102 (2017) - [i10]Vasant G. Honavar, Katherine A. Yelick, Klara Nahrstedt, Holly E. Rushmeier, Jennifer Rexford, Mark D. Hill, Elizabeth Bradley, Elizabeth D. Mynatt:
Advanced Cyberinfrastructure for Science, Engineering, and Public Policy. CoRR abs/1707.00599 (2017) - [i9]Gregory D. Hager, Randal E. Bryant, Eric Horvitz, Maja J. Mataric, Vasant G. Honavar:
Advances in Artificial Intelligence Require Progress Across all of Computer Science. CoRR abs/1707.04352 (2017) - 2016
- [b1]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Representing and Reasoning with Qualitative Preferences: Tools and Applications. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2016, ISBN 978-3-031-00445-2 - [j52]Ngot Bui, John Yen, Vasant G. Honavar:
Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network. IEEE Trans. Comput. Soc. Syst. 3(2): 75-87 (2016) - [c146]Sanghack Lee, Vasant G. Honavar:
On Learning Causal Models from Relational Data. AAAI 2016: 3263-3270 - [c145]Ngot Bui, Thanh Le, Vasant G. Honavar:
Labeling actors in multi-view social networks by integrating information from within and across multiple views. IEEE BigData 2016: 616-625 - [c144]Drena Dobbs, Steven E. Brenner, Vasant G. Honavar, Robert L. Jernigan, Alain Laederach, Quaid Morris:
Session Introduction. PSB 2016: 429-432 - [c143]Sanghack Lee, Vasant G. Honavar:
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. UAI 2016 - [i8]Vasant G. Honavar, Mark D. Hill, Katherine A. Yelick:
Accelerating Science: A Computing Research Agenda. CoRR abs/1604.02006 (2016) - 2015
- [c142]Harris T. Lin, Ngot Bui, Vasant G. Honavar:
Learning classifiers from remote RDF data stores augmented with RDFS subclass hierarchies. IEEE BigData 2015: 1807-1813 - [c141]Vasant G. Honavar:
Discovery Informatics in Biological and Biomedical Sciences: Research Challenges and Opportunities. Pacific Symposium on Biocomputing 2015: 482 - [c140]Ngot Bui, John Yen, Vasant G. Honavar:
Temporal Causality of Social Support in an Online Community for Cancer Survivors. SBP 2015: 13-23 - [c139]Sanghack Lee, Vasant G. Honavar:
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. ACI@UAI 2015: 56-65 - [i7]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
CRISNER: A Practically Efficient Reasoner for Qualitative Preferences. CoRR abs/1507.08559 (2015) - [i6]Sanghack Lee, Vasant G. Honavar:
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. CoRR abs/1508.02103 (2015) - 2014
- [j51]Jia Tao, Giora Slutzki, Vasant G. Honavar:
A Conceptual Framework for Secrecy-preserving Reasoning in Knowledge Bases. ACM Trans. Comput. Log. 16(1): 3:1-3:32 (2014) - [c138]Kannan Sankar, Rasna R. Walia, Carla M. Mann, Robert L. Jernigan, Vasant G. Honavar, Drena Dobbs:
An analysis of conformational changes upon RNA-protein binding. BCB 2014: 592-593 - [c137]Ngot Bui, Vasant G. Honavar:
Labeling Actors in Social Networks Using a Heterogeneous Graph Kernel. SBP 2014: 27-34 - [e8]Jimmy Lin, Jian Pei, Xiaohua Hu, Wo Chang, Raghunath Nambiar, Charu C. Aggarwal, Nick Cercone, Vasant G. Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne:
2014 IEEE International Conference on Big Data (IEEE BigData 2014), Washington, DC, USA, October 27-30, 2014. IEEE Computer Society 2014, ISBN 978-1-4799-5665-4 [contents] - [i5]Facundo Bromberg, Dimitris Margaritis, Vasant G. Honavar:
Efficient Markov Network Structure Discovery Using Independence Tests. CoRR abs/1401.3478 (2014) - [i4]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Representing and Reasoning with Qualitative Preferences for Compositional Systems. CoRR abs/1401.3899 (2014) - 2013
- [c136]Sanghack Lee, Vasant G. Honavar:
m-Transportability: Transportability of a Causal Effect from Multiple Environments. AAAI 2013: 583-590 - [c135]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Verifying Preferential Equivalence and Subsumption via Model Checking. ADT 2013: 324-335 - [c134]Ngot Bui, Vasant G. Honavar:
On the utility of abstraction in labeling actors in social networks. ASONAM 2013: 692-698 - [c133]Harris T. Lin, Vasant G. Honavar:
Learning Classifiers from Chains of Multiple Interlinked RDF Data Stores. BigData Congress 2013: 94-101 - [c132]Harris T. Lin, Sanghack Lee, Ngot Bui, Vasant G. Honavar:
Learning Classifiers from Distributional Data. BigData Congress 2013: 302-309 - [c131]Letao Qi, Harris T. Lin, Vasant G. Honavar:
Clustering remote RDF data using SPARQL update queries. ICDE Workshops 2013: 236-242 - [c130]Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments. NIPS 2013: 136-144 - [c129]Sanghack Lee, Vasant G. Honavar:
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. UAI 2013 - [c128]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Preference Based Service Adaptation Using Service Substitution. Web Intelligence 2013: 487-493 - [i3]Sanghack Lee, Vasant G. Honavar:
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. CoRR abs/1309.6842 (2013) - 2012
- [j50]Rafael A. Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinform. 13: 41 (2012) - [j49]Rasna R. Walia, Cornelia Caragea, Benjamin A. Lewis, Fadi Towfic, Michael Terribilini, Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinform. 13: 89 (2012) - [j48]Fadi Towfic, Shakti Gupta, Vasant G. Honavar, Shankar Subramaniam:
B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis. Genom. Proteom. Bioinform. 10(3): 142-152 (2012) - [j47]Jia Tao, Giora Slutzki, Vasant G. Honavar:
PSPACE Tableau Algorithms for Acyclic Modalized $\boldsymbol{\mathcal{ALC}}$. J. Autom. Reason. 49(4): 551-582 (2012) - [c127]Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Predicting protective bacterial antigens using random forest classifiers. BCB 2012: 426-433 - [c126]Vasant G. Honavar:
Learning predictive models from large distributed autonomous data sources. CIDU 2012: 1 - [c125]Kewei Tu, Vasant G. Honavar:
Unambiguity Regularization for Unsupervised Learning of Probabilistic Grammars. EMNLP-CoNLL 2012: 1324-1334 - [c124]Zachary J. Oster, Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Model Checking of Qualitative Sensitivity Preferences to Minimize Credential Disclosure. FACS 2012: 205-223 - 2011
- [j46]Li C. Xue, Drena Dobbs, Vasant G. Honavar:
HomPPI: A Class of Sequence Homology Based Protein-Protein Interface Prediction Methods. BMC Bioinform. 12: 244 (2011) - [j45]Usha Muppirala, Vasant G. Honavar, Drena Dobbs:
Predicting RNA-Protein Interactions Using Only Sequence Information. BMC Bioinform. 12: 489 (2011) - [j44]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Representing and Reasoning with Qualitative Preferences for Compositional Systems. J. Artif. Intell. Res. 42: 211-274 (2011) - [j43]Benjamin A. Lewis, Rasna R. Walia, Michael Terribilini, Jeff Ferguson, Charles Zheng, Vasant G. Honavar, Drena Dobbs:
PRIDB: a protein-RNA interface database. Nucleic Acids Res. 39(Database-Issue): 277-282 (2011) - [j42]Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Predicting MHC-II Binding Affinity Using Multiple Instance Regression. IEEE ACM Trans. Comput. Biol. Bioinform. 8(4): 1067-1079 (2011) - [c123]Ganesh Ram Santhanam, Yuly Suvorov, Samik Basu, Vasant G. Honavar:
Verifying Intervention Policies to Counter Infection Propagation over Networks: A Model Checking Approach. AAAI 2011: 1408-1414 - [c122]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Identifying Sustainable Designs Using Preferences over Sustainability Attributes. AAAI Spring Symposium: Artificial Intelligence and Sustainable Design 2011 - [c121]Li C. Xue, Rafael A. Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Ranking docked models of protein-protein complexes using predicted partner-specific protein-protein interfaces: a preliminary study. BCB 2011: 441-445 - [c120]Li C. Xue, Rasna R. Walia, Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Improving protein-RNA interface prediction by combining sequence homology based method with a naive Bayes classifier: preliminary results. BCB 2011: 556-558 - [c119]Adrian Silvescu, Vasant G. Honavar:
Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction. Algorithmic Probability and Friends 2011: 339-350 - [c118]Oksana Yakhnenko, Vasant G. Honavar:
Multi-Instance Multi-Label Learning for Image Classification with Large Vocabularies. BMVC 2011: 1-12 - [c117]Kewei Tu, Vasant G. Honavar:
On the Utility of Curricula in Unsupervised Learning of Probabilistic Grammars. IJCAI 2011: 1523-1528 - [c116]Kewei Tu, Xixiu Ouyang, Dingyi Han, Vasant G. Honavar:
Exemplar-based Robust Coherent Biclustering. SDM 2011: 884-895 - [c115]Harris T. Lin, Neeraj Koul, Vasant G. Honavar:
Learning Relational Bayesian Classifiers from RDF Data. ISWC (1) 2011: 389-404 - [i2]Adrian Silvescu, Vasant G. Honavar:
Abstraction Super-structuring Normal Forms: Towards a Theory of Structural Induction. CoRR abs/1107.0434 (2011) - 2010
- [j41]Fadi Towfic, Susan VanderPlas, Casey A. Oliver, Oliver Couture, Christopher K. Tuggle, M. Heather West Greenlee, Vasant G. Honavar:
Detection of gene orthology from gene co-expression and protein interaction networks. BMC Bioinform. 11(S-3): 7 (2010) - [j40]Cornelia Caragea, Doina Caragea, Adrian Silvescu, Vasant G. Honavar:
Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models. BMC Bioinform. 11(S-8): S6 (2010) - [j39]Fadi Towfic, Cornelia Caragea, David C. Gemperline, Drena Dobbs, Vasant G. Honavar:
Struct-NB: predicting protein-RNA binding sites using structural features. Int. J. Data Min. Bioinform. 4(1): 21-43 (2010) - [c114]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Dominance Testing via Model Checking. AAAI 2010: 357-362 - [c113]Cornelia Caragea, Adrian Silvescu, Doina Caragea, Vasant G. Honavar:
Semi-supervised sequence classification using abstraction augmented Markov models. BCB 2010: 257-264 - [c112]Yasser El-Manzalawy, Vasant G. Honavar:
A framework for developing epitope prediction tools. BCB 2010: 660-662 - [c111]Neeraj Koul, Ngot Bui, Vasant G. Honavar:
Scalable, updatable predictive models for sequence data. BIBM 2010: 681-685 - [c110]Cornelia Caragea, Adrian Silvescu, Doina Caragea, Vasant G. Honavar:
Abstraction Augmented Markov Models. ICDM 2010: 68-77 - [c109]Sushain Pandit, Vasant G. Honavar:
Ontology-guided Extraction of Complex Nested Relationships. ICTAI (2) 2010: 173-178 - [c108]Hongyu Sun, Samik Basu, Vasant G. Honavar, Robyn R. Lutz:
Automata-Based Verification of Security Requirements of Composite Web Services. ISSRE 2010: 348-357 - [c107]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Efficient Dominance Testing for Unconditional Preferences. KR 2010 - [c106]Bhavesh Sanghvi, Neeraj Koul, Vasant G. Honavar:
Identifying and Eliminating Inconsistencies in Mappings across Hierarchical Ontologies. OTM Conferences (2) 2010: 999-1008 - [c105]Jia Tao, Giora Slutzki, Vasant G. Honavar:
Secrecy-Preserving Query Answering for Instance Checking in EL\mathcal{EL}. RR 2010: 195-203 - [c104]Neeraj Koul, Vasant G. Honavar:
Learning in Presence of Ontology Mapping Errors. Web Intelligence 2010: 291-296 - [c103]Dinanath Nadkarni, Samik Basu, Vasant G. Honavar, Robyn R. Lutz:
Failure Analysis for Composition of Web Services Represented as Labeled Transition Systems. WS-FM 2010: 161-175
2000 – 2009
- 2009
- [j38]Cornelia Caragea, Jivko Sinapov, Drena Dobbs, Vasant G. Honavar:
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling. BMC Bioinform. 10(S-4) (2009) - [j37]Facundo Bromberg, Dimitris Margaritis, Vasant G. Honavar:
Efficient Markov Network Structure Discovery Using Independence Tests. J. Artif. Intell. Res. 35: 449-484 (2009) - [c102]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
Web Service Substitution Based on Preferences Over Non-functional Attributes. IEEE SCC 2009: 210-217 - [c101]Fadi Towfic, M. Heather West Greenlee, Vasant G. Honavar:
Detection of Gene Orthology Based on Protein-Protein Interaction Networks. BIBM 2009: 48-53 - [c100]Oksana Yakhnenko, Vasant G. Honavar:
Multiple label prediction for image annotation with multiple Kernel correlation models. CVPR Workshops 2009: 8-15 - [c99]Yasser El-Manzalawy, Vasant G. Honavar:
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio. Discovery Science 2009: 80-91 - [c98]Adrian Silvescu, Cornelia Caragea, Vasant G. Honavar:
Combining Super-Structuring and Abstraction on Sequence Classification. ICDM 2009: 986-991 - [c97]Neeraj Koul, Vasant G. Honavar:
Design and Implementation of a Query Planner for Data Integration. ICTAI 2009: 214-218 - [c96]Cornelia Caragea, Doina Caragea, Vasant G. Honavar:
Learning Link-Based Classifiers from Ontology-Extended Textual Data. ICTAI 2009: 354-361 - [c95]Cornelia Caragea, Doina Caragea, Vasant G. Honavar:
Learning Link-Based Naïve Bayes Classifiers from Ontology-Extended Distributed Data. OTM Conferences (2) 2009: 1139-1146 - [c94]Oksana Yakhnenko, Vasant G. Honavar:
Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence. SDM 2009: 283-293 - [c93]Fadi Towfic, M. Heather West Greenlee, Vasant G. Honavar:
Aligning Biomolecular Networks Using Modular Graph Kernels. WABI 2009: 345-361 - [p4]Jie Bao, George Voutsadakis, Giora Slutzki, Vasant G. Honavar:
Package-Based Description Logics. Modular Ontologies 2009: 349-371 - [r2]Doina Caragea, Vasant G. Honavar:
Knowledge Acquisition from Semantically Heterogeneous Data. Encyclopedia of Data Warehousing and Mining 2009: 1110-1116 - [r1]Cornelia Caragea, Vasant G. Honavar:
Machine Learning in Computational Biology. Encyclopedia of Database Systems 2009: 1663-1667 - 2008
- [j36]Myron Peto, Andrzej Kloczkowski, Vasant G. Honavar, Robert L. Jernigan:
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable. BMC Bioinform. 9 (2008) - [j35]Jyotishman Pathak, Samik Basu, Robyn R. Lutz, Vasant G. Honavar:
MOSCOE: an Approach for Composing Web Services through Iterative Reformulation of Functional Specifications. Int. J. Artif. Intell. Tools 17(1): 109-138 (2008) - [c92]Jyotishman Pathak, Samik Basu, Vasant G. Honavar:
Composing Web Services through Automatic Reformulation of Service Specifications. IEEE SCC (1) 2008: 361-369 - [c91]Jie Bao, George Voutsadakis, Giora Slutzki, Vasant G. Honavar:
On the Decidability of Role Mappings between Modular Ontologies. AAAI 2008: 400-405 - [c90]Vasant G. Honavar, Doina Caragea:
Towards Semantics-Enabled Distributed Infrastructure for Knowledge Acquisition. AAAI Spring Symposium: Semantic Scientific Knowledge Integration 2008: 29-35 - [c89]Cornelia Caragea, Jivko Sinapov, Drena Dobbs, Vasant G. Honavar:
Using Global Sequence Similarity to Enhance Biological Sequence Labeling. BIBM 2008: 104-111 - [c88]Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar:
Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information. BIBM 2008: 289-292 - [c87]Neeraj Koul, Jim Lathrop, Jack H. Lutz, Vasant G. Honavar:
Complexes of on-line self assembly. EIT 2008: 448-452 - [c86]Kewei Tu, Vasant G. Honavar:
Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering. ICGI 2008: 224-237 - [c85]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
TCP-Compose* - A TCP-Net Based Algorithm for Efficient Composition of Web Services Using Qualitative Preferences. ICSOC 2008: 453-467 - [c84]Hyunsung Jo, Yong-chan Na, Byonghwa Oh, Jihoon Yang, Vasant G. Honavar:
Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm. ICTAI (1) 2008: 393-400 - [c83]Jae-Hyung Lee, Michael Hamilton, Colin Gleeson, Cornelia Caragea, Peter Zaback, Jeffry D. Sander, Li C. Xue, Feihong Wu, Michael Terribilini, Vasant G. Honavar, Drena Dobbs:
Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning Approaches. Pacific Symposium on Biocomputing 2008: 501-512 - [c82]Ganesh Ram Santhanam, Samik Basu, Vasant G. Honavar:
On Utilizing Qualitative Preferences in Web Service Composition: A CP-net Based Approach. SERVICES I 2008: 538-544 - [c81]George Voutsadakis, Giora Slutzki, Vasant G. Honavar, Jie Bao:
Federated ALCI: Preliminary Report. Web Intelligence 2008: 575-578 - [c80]Neeraj Koul, Cornelia Caragea, Vasant G. Honavar, Vikas Bahirwani, Doina Caragea:
Learning Classifiers from Large Databases Using Statistical Queries. Web Intelligence 2008: 923-926 - [p3]Vasant G. Honavar, Doina Caragea:
Toward Semantics-Enabled Infrastructure for Knowledge Acquisition from Distributed Data. Next Generation of Data Mining 2008 - [p2]Doina Caragea, Dianne Cook, Hadley Wickham, Vasant G. Honavar:
Visual Methods for Examining SVM Classifiers. Visual Data Mining 2008: 136-153 - [e7]Bernardo Cuenca Grau, Vasant G. Honavar, Anne Schlicht, Frank Wolter:
Proceedings of the 2nd International Workshop on Modular Ontologies, WoMO 2007, Whistler, Canada, October 28, 2007. CEUR Workshop Proceedings 315, CEUR-WS.org 2008 [contents] - 2007
- [j34]Joshua Clifford Bongard, Derek P. Brock, Samuel G. Collins, Ramani Duraiswami, Tim Finin, Ian Harrison, Vasant G. Honavar, Gregory S. Hornby, Ari K. Jónsson, Mike Kassoff, David Kortenkamp, Sanjeev Kumar, Ken Murray, Alexander I. Rudnicky, Goran Trajkovski:
Reports on the 2006 AAAI Fall Symposia. AI Mag. 28(1): 88-92 (2007) - [j33]Carson M. Andorf, Drena Dobbs, Vasant G. Honavar:
Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. BMC Bioinform. 8 (2007) - [j32]Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, Drena Dobbs, Vasant G. Honavar:
Glycosylation site prediction using ensembles of Support Vector Machine classifiers. BMC Bioinform. 8 (2007) - [j31]Guy G. Helmer, Johnny Wong, Mark Slagell, Vasant G. Honavar, Les Miller, Yanxin Wang, Xia Wang, Natalia Stakhanova:
Software fault tree and coloured Petri net-based specification, design and implementation of agent-based intrusion detection systems. Int. J. Inf. Comput. Secur. 1(1/2): 109-142 (2007) - [j30]Michael Terribilini, Jeffry D. Sander, Jae-Hyung Lee, Peter Zaback, Robert L. Jernigan, Vasant G. Honavar, Drena Dobbs:
RNABindR: a server for analyzing and predicting RNA-binding sites in proteins. Nucleic Acids Res. 35(Web-Server-Issue): 578-584 (2007) - [c79]Jie Bao, Giora Slutzki, Vasant G. Honavar:
A Semantic Importing Approach to Knowledge Reuse from Multiple Ontologies. AAAI 2007: 1304-1309 - [c78]Cornelia Caragea, Jivko Sinapov, Vasant G. Honavar, Drena Dobbs:
Assessing the Performance of Macromolecular Sequence Classifiers. BIBE 2007: 320-326 - [c77]Feihong Wu, Fadi Towfic, Drena Dobbs, Vasant G. Honavar:
Analysis of Protein Protein Dimeric Interfaces. BIBM 2007: 35 - [c76]James D. McCalley, Vasant G. Honavar, Sarah M. Ryan, William Q. Meeker, Daji Qiao, Ronald A. Roberts, Yuan Li, Jyotishman Pathak, Mujing Ye, Yili Hong:
Integrated Decision Algorithms for Auto-steered Electric Transmission System Asset Management. International Conference on Computational Science (1) 2007: 1066-1073 - [c75]Jyotishman Pathak, Samik Basu, Vasant G. Honavar:
On Context-Specific Substitutability of Web Services. ICWS 2007: 192-199 - [c74]Jie Bao, Giora Slutzki, Vasant G. Honavar:
Privacy-Preserving Reasoning on the SemanticWeb. Web Intelligence 2007: 791-797 - [p1]Jyotishman Pathak, Samik Basu, Vasant G. Honavar:
Assembling Composite Web Services from Autonomous Components. Emerging Artificial Intelligence Applications in Computer Engineering 2007: 394-405 - [e6]Peter Haase, Vasant G. Honavar, Oliver Kutz, York Sure, Andrei Tamilin:
Proceedings of the 1st International Workshop on Modular Ontologies, WoMO'06, co-located with the International Semantic Web Conference, ISWC'06 November 5, 2006, Athens, Georgia, USA. CEUR Workshop Proceedings 232, CEUR-WS.org 2007 [contents] - 2006
- [j29]Changhui Yan, Michael Terribilini, Feihong Wu, Robert L. Jernigan, Drena Dobbs, Vasant G. Honavar:
Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinform. 7: 262 (2006) - [j28]Yanxin Wang, Smruti Ranjan Behera, Johnny Wong, Guy G. Helmer, Vasant G. Honavar, Les Miller, Robyn R. Lutz, Mark Slagell:
Towards the automatic generation of mobile agents for distributed intrusion detection system. J. Syst. Softw. 79(1): 1-14 (2006) - [j27]Jun Zhang, Dae-Ki Kang, Adrian Silvescu, Vasant G. Honavar:
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data. Knowl. Inf. Syst. 9(2): 157-179 (2006) - [c73]Doina Caragea, Jie Bao, Vasant G. Honavar:
A General Strategy for Knowledge Acquisition from Semantically Heterogeneous Data Sources. AAAI Fall Symposium: Semantic Web for Collaborative Knowledge Acquisition 2006: 1-8 - [c72]Jie Bao, Vasant G. Honavar:
Representing and Reasoning with Modular Ontologies. AAAI Fall Symposium: Semantic Web for Collaborative Knowledge Acquisition 2006: 86-89 - [c71]Keunjoon Lee, Jinu Joo, Jihoon Yang, Vasant G. Honavar:
Experimental Comparison of Feature Subset Selection Using GA and ACO Algorithm. ADMA 2006: 465-472 - [c70]Jie Bao, Doina Caragea, Vasant G. Honavar:
Modular Ontologies - A Formal Investigation of Semantics and Expressivity. ASWC 2006: 616-631 - [c69]Jie Bao, Doina Caragea, Vasant G. Honavar:
Towards Collaborative Environments for Ontology Construction and Sharing. CTS 2006: 99-108 - [c68]Doina Caragea, Jun Zhang, Jyotishman Pathak, Vasant G. Honavar:
Learning Classifiers from Distributed, Ontology-Extended Data Sources. DaWaK 2006: 363-373 - [c67]Jie Bao, Zhiliang Hu, Doina Caragea, James M. Reecy, Vasant G. Honavar:
A Tool for Collaborative Construction of Large Biological Ontologies. DEXA Workshops 2006: 191-195 - [c66]Jyotishman Pathak, Samik Basu, Robyn R. Lutz, Vasant G. Honavar:
Parallel Web Service Composition in MoSCoE: A Choreography-Based Approach. ECOWS 2006: 3-12 - [c65]Jyotishman Pathak, Yong Jiang, Vasant G. Honavar, James D. McCalley:
Condition Data Aggregation with Application to Failure Rate Calculation of Power Transformers. HICSS 2006 - [c64]James D. McCalley, Vasant G. Honavar, Sarah M. Ryan, William Q. Meeker, Ronald A. Roberts, Daji Qiao, Yuan Li:
Auto-steered Information-Decision Processes for Electric System Asset Management. International Conference on Computational Science (3) 2006: 440-447 - [c63]Jyotishman Pathak, Samik Basu, Robyn R. Lutz, Vasant G. Honavar:
MoSCoE: A Framework for Modeling Web Service Composition and Execution. ICDE Workshops 2006: 143 - [c62]Jyotishman Pathak, Yuan Li, Vasant G. Honavar, James D. McCalley:
A Service-Oriented Architecture for Electric Power Transmission System Asset Management. ICSOC Workshops 2006: 26-37 - [c61]Jyotishman Pathak, Samik Basu, Vasant G. Honavar:
Modeling Web Services by Iterative Reformulation of Functional and Non-functional Requirements. ICSOC 2006: 314-326 - [c60]Jyotishman Pathak, Samik Basu, Robyn R. Lutz, Vasant G. Honavar:
Selecting and Composing Web Services through Iterative Reformulation of Functional Specifications. ICTAI 2006: 445-454 - [c59]Adrian Silvescu, Vasant G. Honavar:
Independence, Decomposability and Functions which Take Values into an Abelian Group. AI&M 2006 - [c58]Jie Bao, Vasant G. Honavar:
Adapt OWL as a Modular Ontology Language. OWLED 2006 - [c57]Dae-Ki Kang, Adrian Silvescu, Vasant G. Honavar:
RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification. PAKDD 2006: 45-54 - [c56]Flavian Vasile, Adrian Silvescu, Dae-Ki Kang, Vasant G. Honavar:
TRIPPER: Rule Learning Using Taxonomies. PAKDD 2006: 55-59 - [c55]Facundo Bromberg, Dimitris Margaritis, Vasant G. Honavar:
Efficient Markov Network Structure Discovery using Independence Tests. SDM 2006: 141-152 - [c54]Jie Bao, Doina Caragea, Vasant G. Honavar:
On the Semantics of Linking and Importing in Modular Ontologies. ISWC 2006: 72-86 - [c53]Jie Bao, Doina Caragea, Vasant G. Honavar:
Package-Based Description Logics - Preliminary Results. ISWC 2006: 967-969 - [c52]Jie Bao, Vasant G. Honavar:
Divide and Conquer Semantic Web with Modular Ontologies - A Brief Review of Modular Ontology Language Formalisms. WoMO 2006 - [c51]Jie Bao, Doina Caragea, Vasant G. Honavar:
A Tableau-Based Federated Reasoning Algorithm for Modular Ontologies. Web Intelligence 2006: 404-410 - [e5]Vasant G. Honavar, Tim Finin:
Semantic Web for Collaborative Knowledge Acquisition, Papers from the 2006 AAAI Fall Symposium, Washington, DC, USA, October 13-15, 2006. AAAI Technical Report FS-06-06, AAAI Press 2006 [contents] - 2005
- [c50]Cornelia Caragea, Doina Caragea, Vasant G. Honavar:
Learning Support Vector Machines from Distributed Data Sources. AAAI 2005: 1602-1603 - [c49]Doina Caragea, Jun Zhang, Jie Bao, Jyotishman Pathak, Vasant G. Honavar:
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources. ALT 2005: 13-44 - [c48]Doina Caragea, Jie Bao, Jyotishman Pathak, Adrian Silvescu, Carson M. Andorf, Drena Dobbs, Vasant G. Honavar:
Information Integration from Semantically Heterogeneous Biological Data Sources. DEXA Workshops 2005: 580-584 - [c47]Doina Caragea, Jyotishman Pathak, Jie Bao, Adrian Silvescu, Carson M. Andorf, Drena Dobbs, Vasant G. Honavar:
Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources. DILS 2005: 175-190 - [c46]Doina Caragea, Jun Zhang, Jie Bao, Jyotishman Pathak, Vasant G. Honavar:
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources. Discovery Science 2005: 14 - [c45]Jun Zhang, Doina Caragea, Vasant G. Honavar:
Learning Ontology-Aware Classifiers. Discovery Science 2005: 308-321 - [c44]Oksana Yakhnenko, Adrian Silvescu, Vasant G. Honavar:
Discriminatively Trained Markov Model for Sequence Classification. ICDM 2005: 498-505 - [c43]Dae-Ki Kang, Doug Fuller, Vasant G. Honavar:
Learning Classifiers for Misuse Detection Using a Bag of System Calls Representation. ISI 2005: 511-516 - [c42]Dae-Ki Kang, Jun Zhang, Adrian Silvescu, Vasant G. Honavar:
Multinomial Event Model Based Abstraction for Sequence and Text Classification. SARA 2005: 134-148 - [c41]Feihong Wu, Jun Zhang, Vasant G. Honavar:
Learning Classifiers Using Hierarchically Structured Class Taxonomies. SARA 2005: 313-320 - [c40]Jyotishman Pathak, Neeraj Koul, Doina Caragea, Vasant G. Honavar:
A framework for semantic web services discovery. WIDM 2005: 45-50 - [i1]Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, Susan Carpenter, Vasant G. Honavar, Drena Dobbs:
Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data. CoRR abs/cs/0511075 (2005) - 2004
- [j26]Taner Z. Sen, Andrzej Kloczkowski, Robert L. Jernigan, Changhui Yan, Vasant G. Honavar, Kai-Ming Ho, Cai-Zhuang Wang, Yungok Ihm, Haibo Cao, Xun Gu, Drena Dobbs:
Predicting binding sites of hydrolase-inhibitor complexes by combining several methods. BMC Bioinform. 5: 205 (2004) - [j25]Doina Caragea, Adrian Silvescu, Vasant G. Honavar:
A Framework for Learning from Distributed Data Using Sufficient Statistics and Its Application to Learning Decision Trees. Int. J. Hybrid Intell. Syst. 1(2): 80-89 (2004) - [j24]Changhui Yan, Vasant G. Honavar, Drena Dobbs:
Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach. Neural Comput. Appl. 13(2): 123-129 (2004) - [c39]Doina Caragea, Jyotishman Pathak, Vasant G. Honavar:
Learning Classifiers from Semantically Heterogeneous Data. CoopIS/DOA/ODBASE (2) 2004: 963-980 - [c38]Jinu Joo, Jun Zhang, Jihoon Yang, Vasant G. Honavar:
Generating AVTs Using GA for Learning Decision Tree Classifiers with Missing Data. Discovery Science 2004: 347-354 - [c37]Jie Bao, Vasant G. Honavar:
Collaborative Ontology Building with Wiki@nt - A Multi-agent Based Ontology Building Environment. EON 2004 - [c36]Dae-Ki Kang, Adrian Silvescu, Jun Zhang, Vasant G. Honavar:
Generation of Attribute Value Taxonomies from Data for Data-Driven Construction of Accurate and Compact Classifiers. ICDM 2004: 130-137 - [c35]Jun Zhang, Vasant G. Honavar:
AVT-NBL: An Algorithm for Learning Compact and Accurate Naïve Bayes Classifiers from Attribute Value Taxonomies and Data. ICDM 2004: 289-296 - [c34]Jie Bao, Yu Cao, Wallapak Tavanapong, Vasant G. Honavar:
Integration of Domain-Specific and Domain-Independent Ontologies for Colonoscopy Video Database Annotation. IKE 2004: 82-90 - [c33]Changhui Yan, Drena Dobbs, Vasant G. Honavar:
A two-stage classifier for identification of protein-protein interface residues. ISMB/ECCB (Supplement of Bioinformatics) 2004: 371-378 - [c32]Jyotishman Pathak, Doina Caragea, Vasant G. Honavar:
Ontology-Extended Component-Based Workflows : A Framework for Constructing Complex Workflows from Semantically Heterogeneous Software Components. SWDB 2004: 41-56 - 2003
- [j23]Xiangyun Wang, Diane Schroeder, Drena Dobbs, Vasant G. Honavar:
Automated data-driven discovery of motif-based protein function classifiers. Inf. Sci. 155(1-2): 1-18 (2003) - [j22]Guy G. Helmer, Johnny S. Wong, Vasant G. Honavar, Les Miller, Yanxin Wang:
Lightweight agents for intrusion detection. J. Syst. Softw. 67(2): 109-122 (2003) - [c31]Doina Caragea, Dianne Cook, Vasant G. Honavar:
Towards Simple, Easy-to-Understand, yet Accurate Classifiers. ICDM 2003: 497-500 - [c30]Jun Zhang, Vasant G. Honavar:
Learning from Attribute Value Taxonomies and Partially Specified Instances. ICML 2003: 880-887 - [c29]Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vasant G. Honavar:
Statistics Gathering for Learning from Distributed, Heterogeneous and Autonomous Data Sources. IIWeb 2003: 99-104 - [c28]Anna Atramentov, Hector Leiva, Vasant G. Honavar:
A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments. ILP 2003: 38-56 - [c27]Jaime Reinoso, Adrian Silvescu, Doina Caragea, Jyotishman Pathak, Vasant G. Honavar:
Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources : A Federated Ontology-Driven Query-Centric Approach. IRI 2003: 183-191 - [c26]Doina Caragea, Adrian Silvescu, Vasant G. Honavar:
Learning Decision Trees form Distributed Heterogeneous Autonomous Data. MAICS 2003: 10-17 - 2002
- [j21]Guy G. Helmer, Johnny S. Wong, Vasant G. Honavar, Les Miller:
Automated discovery of concise predictive rules for intrusion detection. J. Syst. Softw. 60(3): 165-175 (2002) - [j20]Guy G. Helmer, Johnny S. Wong, Mark Slagell, Vasant G. Honavar, Les Miller, Robyn R. Lutz:
A Software Fault Tree Approach to Requirements Analysis of an Intrusion Detection System. Requir. Eng. 7(4): 207-220 (2002) - [c25]Xiangyun Wang, Diane Schroeder, Drena Dobbs, Vasant G. Honavar:
Data-Driven Discovery of Protein Function Classifiers: Decision Trees Based on MEME Motifs outperform PROSITE Patterns and Profiles on Peptidase Families. JCIS 2002: 1193-1199 - [c24]Carson M. Andorf, Drena Dobbs, Vasant G. Honavar:
Discovering Protein Function Classification Rules from Reduced Alphabet Representations of Protein Sequences. JCIS 2002: 1200-1206 - [c23]Jun Zhang, Adrian Silvescu, Vasant G. Honavar:
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. SARA 2002: 316-323 - [e4]William B. Langdon, Erick Cantú-Paz, Keith E. Mathias, Rajkumar Roy, David Davis, Riccardo Poli, Karthik Balakrishnan, Vasant G. Honavar, Günter Rudolph, Joachim Wegener, Larry Bull, Mitchell A. Potter, Alan C. Schultz, Julian F. Miller, Edmund K. Burke, Natasa Jonoska:
GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002. Morgan Kaufmann 2002, ISBN 1-55860-878-8 [contents] - [e3]H. John Caulfield, Shu-Heng Chen, Heng-Da Cheng, Richard J. Duro, Vasant G. Honavar, Etienne E. Kerre, Mi Lu, Manuel Graña Romay, Timothy K. Shih, Dan Ventura, Paul P. Wang, Yuanyuan Yang:
Proceedings of the 6th Joint Conference on Information Science, March 8-13, 2002, Research Triangle Park, North Carolina, USA. JCIS / Association for Intelligent Machinery, Inc. 2002, ISBN 0-9707890-1-7 [contents] - 2001
- [j19]Adrian Silvescu, Vasant G. Honavar:
Temporal Boolean Network Models of Genetic Networks and their Inference from Gene Expression Time Series. Complex Syst. 13(1) (2001) - [j18]Johnny Wong, Guy G. Helmer, Venkatraman Naganathan, Sriniwas Polavarapu, Vasant G. Honavar, Les Miller:
SMART mobile agent facility. J. Syst. Softw. 56(1): 9-22 (2001) - [j17]Armin R. Mikler, Vasant G. Honavar, Johnny S. Wong:
Autonomous agents for coordinated distributed parameterized heuristic routing in large dynamic communication networks. J. Syst. Softw. 56(3): 231-246 (2001) - [j16]Vasant G. Honavar, Colin de la Higuera:
Introduction. Mach. Learn. 44(1/2): 5-7 (2001) - [j15]Rajesh Parekh, Vasant G. Honavar:
Learning DFA from Simple Examples. Mach. Learn. 44(1/2): 9-35 (2001) - [j14]Robi Polikar, L. Upda, S. S. Upda, Vasant G. Honavar:
Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C 31(4): 497-508 (2001) - [c22]Robi Polikar, Ruth Shinar, Vasant G. Honavar, Lalita Udpa, Marc D. Porter:
Detection and identification of odorants using an electronic nose. ICASSP 2001: 3137-3140 - [c21]Doina Caragea, Dianne Cook, Vasant G. Honavar:
Gaining insights into support vector machine pattern classifiers using projection-based tour methods. KDD 2001: 251-256 - [c20]Doina Caragea, Adrian Silvescu, Vasant G. Honavar:
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. Emergent Neural Computational Architectures Based on Neuroscience 2001: 547-559 - 2000
- [j13]Rajesh Parekh, Jihoon Yang, Vasant G. Honavar:
An empirical comparison of the performance so single-layer algorithms for training threshold logic units. Neural Parallel Sci. Comput. 8(3-4): 415-438 (2000) - [j12]Rajesh Parekh, Jihoon Yang, Vasant G. Honavar:
Constructive neural-network learning algorithms for pattern classification. IEEE Trans. Neural Networks Learn. Syst. 11(2): 436-451 (2000) - [c19]Doina Caragea, Adrian Silvescu, Vasant G. Honavar:
Incremental and Distributed Learning with Support Vector Machines. AAAI/IAAI 2000: 1067 - [c18]Robi Polikar, Lalita Udpa, Satish S. Udpa, Vasant G. Honavar:
LEARN++: an incremental learning algorithm for multilayer perceptron networks. ICASSP 2000: 3414-3417 - [c17]Rajesh Parekh, Vasant G. Honavar:
On the Relationship between Models for Learning in Helpful Environments. ICGI 2000: 207-220 - [c16]Prashant Pai, Leslie L. Miller, Vasant G. Honavar, Johnny Wong, Sree Nilakanta:
Supporting organizational knowledge management with agents. IRMA Conference 2000: 325-329 - [c15]Mokdong Chug, Vasant G. Honavar:
A Negotiation Model in Agent-mediated Electronic Commerce. ISMSE 2000: 403-410
1990 – 1999
- 1999
- [j11]Karthik Balakrishnan, Olivier Bousquet, Vasant G. Honavar:
Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots. Adapt. Behav. 7(2): 173-216 (1999) - [j10]Ron Sun, Vasant G. Honavar, Gregg C. Oden:
Integration of cognitive systems across disciplinary boundaries. Cogn. Syst. Res. 1(1): 1-3 (1999) - [j9]Jihoon Yang, Rajesh Parekh, Vasant G. Honavar:
DistAl: An inter-pattern distance-based constructive learning algorithm. Intell. Data Anal. 3(1): 55-73 (1999) - [j8]Chun-Hsien Chen, Vasant G. Honavar:
A neural-network architecture for syntax analysis. IEEE Trans. Neural Networks 10(1): 94-114 (1999) - [c14]Guy G. Helmer, Johnny S. Wong, Vasant G. Honavar, Les Miller:
Feature Selection Using a Genetic Algorithm for Intrusion Detection. GECCO 1999: 1781 - [c13]Rajesh Parekh, Vasant G. Honavar:
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples. ICML 1999: 298-306 - [c12]Jihoon Yang, Rajesh Parekh, Vasant G. Honavar, Drena Dobbs:
Data-Driven Theory Refinement Using KBDistAl. IDA 1999: 331-342 - [c11]Jihoon Yang, Rajesh Parekh, Vasant G. Honavar, Drena Dobbs:
Data-driven theory refinement algorithms for bioinformatics. IJCNN 1999: 4064-4068 - [e2]Wolfgang Banzhaf, Jason M. Daida, A. E. Eiben, Max H. Garzon, Vasant G. Honavar, Mark J. Jakiela, Robert E. Smith:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), 13-17 July 1999, Orlando, Florida, USA. Morgan Kaufmann 1999, ISBN 1-55860-611-4 [contents] - 1998
- [j7]Jihoon Yang, Vasant G. Honavar:
Feature Subset Selection Using a Genetic Algorithm. IEEE Intell. Syst. 13(2): 44-49 (1998) - [j6]Armin R. Mikler, Johnny S. K. Wong, Vasant G. Honavar:
An object oriented approach to simulating large communication networks. J. Syst. Softw. 40(2): 151-164 (1998) - [c10]Rajesh Parekh, Codrin M. Nichitiu, Vasant G. Honavar:
A Polynominal Time Incremental Algorithm for Learning DFA. ICGI 1998: 37-49 - [e1]Vasant G. Honavar, Giora Slutzki:
Grammatical Inference, 4th International Colloquium, ICGI-98, Ames, Iowa, USA, July 12-14, 1998, Proceedings. Lecture Notes in Computer Science 1433, Springer 1998, ISBN 3-540-64776-7 [contents] - 1997
- [j5]Armin R. Mikler, Johnny S. Wong, Vasant G. Honavar:
Quo Vadis - A Framework for Intelligent Routing in Large Communication Networks. J. Syst. Softw. 37(1): 61-73 (1997) - [c9]Rajesh Parekh, Vasant G. Honavar:
Learning DFA from Simple Examples. ALT 1997: 116-131 - [c8]Rajesh Parekh, Jihoon Yang, Vasant G. Honavar:
MUpstart-a constructive neural network learning algorithm for multi-category pattern classification. ICNN 1997: 1924-1929 - [c7]Rajesh Parekh, Jihoon Yang, Vasant G. Honavar:
Pruning strategies for the MTiling constructive learning algorithm. ICNN 1997: 1960-1965 - 1996
- [c6]Armin R. Mikler, Vasant G. Honavar, Johnny S. Wong:
Analysis of Utility-Theoretic Heuristics for Intelligent Adaptive Network Routing. AAAI/IAAI, Vol. 1 1996: 96-101 - [c5]Karthik Balakrishnan, Vasant G. Honavar:
Experiments in Evolutionary Synthesis of Robotic Neurocontrollers. AAAI/IAAI, Vol. 2 1996: 1378 - [c4]Rajesh Parekh, Vasant G. Honavar:
An Incremental Interactive Algorithm for Regular Grammar Inference. AAAI/IAAI, Vol. 2 1996: 1397 - [c3]Rajesh Parekh, Jihoon Yang, Vasant G. Honavar:
Constructive Neural Network Learning Algorithms. AAAI/IAAI, Vol. 2 1996: 1398 - [c2]Rajesh Parekh, Vasant G. Honavar:
An incremental interactive algorithm for grammar inference. ICGI 1996: 238-249 - 1995
- [j4]Chun-Hsien Chen, Vasant G. Honavar:
A Neural Architecture for Content as well as Address-based Storage and Recall: Theory and Applications. Connect. Sci. 7(3-4): 281-300 (1995) - 1993
- [j3]Vasant G. Honavar, Leonard Uhr:
Generative learning structures and processes for generalized connectionist networks. Inf. Sci. 70(1-2): 75-108 (1993) - 1992
- [j2]Vasant G. Honavar:
Neural Network Design and the Complexity of Learning (Book Review). Mach. Learn. 9: 95-98 (1992) - 1990
- [j1]Vasant G. Honavar, Leonard Uhr:
Coordination and control structures and processes: possibilities for connectionist networks (CN). J. Exp. Theor. Artif. Intell. 2(4): 277-302 (1990)
1980 – 1989
- 1989
- [c1]Vasant G. Honavar, Leonard Uhr:
Generation, Local Receptive Fields and Global Convergence Improve Perceptual Learning in Connectionist Networks. IJCAI 1989: 180-185
Coauthor Index
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