Dr. Arindam Banerjee is a Founder Professor at the Department of Computer Science, University of Illinois Urbana-Champaign. His research interests are in machine learning. His current research focuses on computational and statistical aspects of over-parameterized models including deep learning, spatial and temporal data analysis, generative models, and sequential decision-making problems. His work also focuses on applications of machine learning in complex real-world and scientific domains including problems in climate science and ecology. He has won several awards, including the NSF CAREER award (2010), the IBM Faculty Award (2013), and seven best paper awards in top-tier venues.
Research Interests:
- Machine Learning
- Artificial Intelligence
- Data Mining.
- Ph.D., University of Texas at Austin August 2005, Dept of Electrical and Computer Engineering.
- M.Tech., Indian Institute of Technology, Kanpur, May 1999, Dept of Electrical Engineering.
- B.E., Jadavpur University, May 1997, Dept of Electronics and Telecommunication Engineering
- Restricted Strong Convexity of Deep Learning Models with Smooth Activations
A. Banerjee, P. Cisneros-Velarde, L. Zhu, and M. Belkin
International Conference on Learning Representations (ICLR), 2023.
Extended version (arxiv). - TorchGeo: Deep Learning with Geospatial Data
A. J. Stewart, C. Robinson, I. A. Corley, A. Ortiz, J. M. Lavista Ferres, and A. Banerjee
ACM SIGSPATIAL International Conference in Geographic Information Systems (SIGSPATIAL), 2022.
[Best Paper Award, Runner Up]
(arxiv, github). - Improved Algorithms for Neural Active Learning
Y. Ban, Y. Zhang, H. Tong, A. Banerjee, and J. He
Advances in Neural Information Processing Systems (NeurIPS), 2022. - Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
A. Banerjee, T. Chen, X. Li, Y. Zhou
International Conference on Machine Learning (ICML), 2022.
Extended version (arXiv). - Smoothed Adversarial Linear Contextual Bandits with Knapsacks
V. Sivakumar, S. Zuo, and A. Banerjee
International Conference on Machine Learning (ICML), 2022. - EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Y. Ban, Y. Yan, A. Banerjee, J. He
International Conference on Learning Representations (ICLR) [Spotlight], 2022. - The number of tree species on Earth
R. Gatti, P. Reich, et al.
Proceedings of the National Academy of Science (PNAS) , 2022 (journal version). - Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration
S. He, X. Li, L. Trenary, B. A. Cash, T. DelSole, and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2022.
Extended version (arxiv). - Noisy Truncated SGD: Optimization and Generalization
Y. Zhou, X. Li, and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2022.
Extended version (arXiv). - Updated respiration routines alter spatio-temporal patterns of carbon cycling in a global land surface model
E. E. Butler, K. R. Wythers, H. Flores-Moreno, M. Chen, A. Datta, D. M. Ricciuto, O. K. Atkin, J. Kattge, P. E. Thornton, A. Banerjee, and Peter B Reich
Environmental Research Letters, 16(10), 2021. - Subseasonal Climate Prediction in the Western US using Bayesian Spatial Models
V. Srinivasan, J. Khim, A. Banerjee, and P. Ravikumar.
Conference on Uncertainty in Artificial Intelligence (UAI), 2021. - The causes and consequences of plant biodiversity across scales in a rapidly changing world
J. Cavender-Bares, et al.
Research Ideas and Outcomes, (journal version), 2021. - Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Y. Zhou, S. Wu, and A. Banerjee
International Conference on Learning Representations (ICLR), 2021.
Extended version (arxiv). - Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
S. He, X. Li, T. DelSole, P. Ravikumar, and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2021.
Extended version (arxiv). - Gradient Boosted Normalizing Flows
R. Giaquinto and A. Banerjee
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Extended version, 2020 (arXiv). - Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
V. Sivakumar, S. Wu, and A. Banerjee
International Conference on Machine Learning (ICML), 2020.
Extended version (arXiv). - Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
X. Li, Q. Gu, Y. Zhou, T. Chen, and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2020.
Extended version (arXiv). - TRY plant trait database–enhanced coverage and open access
J. Kattge, et al.
Global Change Biology (journal version), 2020. - Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
A. Banerjee, Q. Gu, V. Sivakumar, and S. Wu
Advances in Neural Information Processing Systems (NeurIPS), 2019 (pdf).
Extended version (arXiv). - Adversarial Attacks on an Oblivious Recommender
K. Christakopoulou and A. Banerjee
ACM Recommender Systems Conference (RecSys) (long paper), 2019 (pdf). - Sketched Iterative Algorithms for Structured Generalized Linear Models
Q. Gu and A. Banerjee
International Joint Conference on Artificial Intelligence (IJCAI) (oral), 2019 (pdf). - Robustness of trait connections across environmental gradients and growth forms
H. Florese-Moreno, F. Fazayeli, A. Banerjee, A. Datta, J. Kattge, E. Butler, O. Atkin, K. Whythers, M. Chen, M. Anand, M. Bahn, Michael, C. Byun, J. Cornelissen, J. Craine, A. González-Melo, W. Hattingh, S. Jansen, N. Kraft, K. Kramer, D. Laughlin, V. Minden, Ü. Niinemets, V. Onipchenko, J. Penuelas, N. Soudzilovskaia, R. Dalrymple, P. Reich
Global Ecology and Biogeography (GEB), 2019 (paper). - Intelligent systems for geosciences: an essential research agenda
Y. Gil, S. A. Pierce, H. A. Babaie, A. Banerjee, K. D. Borne, G. Bust, M. Cheatham, I. Ebert-Uphoff, C. Gomes, M. Hill, J. Horel, L. Hsu, J. Kinter, C. A. Knoblock, D. Krum, V. Kumar, P. Lermusiaux, Y. Liu, C. North, V. Pankratius, S. Peters, B. Plale, A. Pope, S. Ravela, J. Restrepo, A. J. Ridley, H. Samet, and S. Shekhar
Communications of the ACM (CACM), 62(1):76-84, 2019 (paper). - Scalable Algorithms for Locally Low-Rank Matrix Modeling
Q. Gu, J. Trzasko, and A. Banerjee
Knowledge and Information Systems (KAIS), 2019 (pdf).
Invited for journal publication as Best of ICDM'17. - Interpretable Predictive Modeling for Climate Variables with Weighted Lasso
S. He, X. Li, V. Sivakumar, and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2019 (pdf). - An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
S. Chen and A. Banerjee
Advances in Neural Information Processing Systems (NeurIPS), 2018 (pdf). - Stable Gradient Descent
Y. Xue, S. Chen, and A. Banerjee
Conference on Uncertainty in Artificial Intelligence (UAI), 2018 (pdf). - Modeling Alzheimers Disease Progression with Fused Laplacian Sparse Group Lasso
X. Liu, P. Cao, A. R. Goncalves, D. Zhao, and A. Banerjee
ACM Transactions on Knowledge Discovery from Data (TKDD), 2018 (pdf). - Modeling Alzheimer's Disease Cognitive Scores using Multi-task Sparse Group Lasso
X. Liu, A. R. Goncalves, P. Cao, D. Zhao, and A. Banerjee
Computerized Medical Imaging and Graphics, 66:100-114, 2018 (pdf). - DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora
R. Giaquinto and A. Banerjee
International Conference on Data Mining (ICDM), 2018 (arXiv). - Learning to Interact with Users: A Collaborative-Bandit Approach
K. Christakopoulou and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2018 (pdf). - Time Series Deinterleaving of DNS Traffic
A. Asiaee T., H. Goel, S. Ghosh, V. Yegneswaran, and A. Banerjee
1st Deep Learning and Security Workshop, 2018. - Sparse Linear Isotonic Models
S. Chen and A. Banerjee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
Extended version (arXiv). - Topic Modeling on Health Journals with Regularized Variational Inference
R. Giaquinto and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2018 (pdf). - Mapping local and global variability in plant trait distributions
E. E. Butler, A. Datta, et al.
Proceedings of the National Academy of Sciences (PNAS), 2017 (journal version). - Alternating Estimation for Structured High-Dimensional Multi-Response Models
S. Chen and A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2017.
Extended version (arXiv). - Scalable Algorithms for Locally Low-Rank Matrix Modeling
Q. Gu, J. Trzasko, and A. Banerjee
International Conference on Data Mining (ICDM), 2017 (pdf). - High-Dimensional Dependency Structure Learning for Physical Processes
J. Golmohammadi, I. Ebert-Uphoff, S. He, Y. Deng, and A. Banerjee
International Conference on Data Mining (ICDM), 2017.
Extended version (arXiv). - Recommendation with Capacity Constraints
K. Christakopoulou, J. Kawale, and A. Banerjee
International Conference on Information and Knowledge Management (CIKM), 2017 (pdf)
Extended Version (arXiv). - High-Dimensional Structured Quantile Regression
V. Sivakumar and A. Banerjee
International Conference on Machine Learning (ICML), 2017 (pdf). - Robust Structured Estimation with Single-Index Models
S. Chen and A. Banerjee
International Conference on Machine Learning (ICML), 2017 (pdf). - A Spectral Algorithm for Inference in Hidden semi-Markov Models
I. Melnyk and A. Banerjee
Journal of Machine Learning Research (JMLR), 2017 (journal version). - Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning
A. R. Gonçalves, A. Banerjee, and F. J. Von Zuben
AAAI Conference on Artificial Intelligence (AAAI), 2017.
Extended version (from AAAI'17), 2017 (arxiv). - Statistical Seasonal Prediction Based on Regularized Regression
T. DelSole and A. Banerjee
Journal of Climate, 2017 (journal version). - High Dimensional Structured Superposition Models
Q. Gu, A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2016.
Extended version (from NIPS'16), 2017 (arxiv). - Structured Matrix Recovery via the Generalized Dantzig Selector
S. Chen, A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2016.
Extended version, 2016 (arxiv). - Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems
I. Melnyk, A. Banerjee, B. Matthews, and N. Oza
International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
Extended version (from KDD'16), 2016 (arxiv). - Generalized Direct Change Estimation in Ising Model Structure
F. Fazayeli and A. Banerjee
International Conference on Machine Learning (ICML), 2016 (pdf).
Extended version (from ICML'16), 2016 (arxiv). - Estimating Structured Vector Autoregressive Model
I. Melnyk and A. Banerjee
International Conference on Machine Learning (ICML), 2016.
Extended version (from ICML'16), 2016 (arxiv). - Multi-task Sparse Structure Learning with Gaussian Copula Models
A. Goncalves, F. J. Von Zuben, and A. Banerjee
Journal of Machine Learning Research (JMLR), 2016 (pdf) . - The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications
F. Fazayeli and A. Banerjee
European Conference on Machine Learning (ECML-PKDD), 2016.
Extended version (from ECML'16), 2016 (arxiv). - High Dimensional Structured Estimation with Noisy Designs
A. Taheri, S. Chatterjee, and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2016 (pdf). - Multi-task Spare Group Lasso for Characterizing Alzheimer's Disease
X. Liu, P. Cao, D. Zhao, and A. Banerjee
Workshop on Data Mining for Medicine and Healthcare (DMMH), SDM, 2016. - Understanding Dominant Factors for Precipitation over the Great Lakes Region
S. Chatterjee, S. Liess, A. Banerjee, and V. Kumar
AAAI Conference on Artificial Intelligence (AAAI), 2016 (pdf). - Vector Autoregressive Model-based Anomaly Detection in Aviation Systems
I. Melnyk, B. Matthews, H. Valizadegan, A. Banerjee, and Nikunj Oza
Journal of Aerospace Information Systems (JAIS), 2016. - Structured Estimation with Atomic Norms: General Bounds and Applications
S. Chen and A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2015 (pdf). - Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
V. Sivakumar, A. Banerjee, and P. Ravikumar
Advances in Neural Information Processing Systems (NIPS), 2015 (pdf). - Unified View of Matrix Completion under General Structural Constraints
S. Gunasekar, A. Banerjee, and J. Ghosh
Advances in Neural Information Processing Systems (NIPS), 2015.
Extended version (from NIPS'15), 2016 (arxiv). - BHPMF -- a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography
F. Schrodt, et al.,
Global Ecology and Biogeography (GEB), 2015 (pdf). - A Multitask Learning View on the Earth System Model Ensemble
A. Goncalves, F. J. Von Zuben, and A. Banerjee
Computing in Science & Engineering, 2015 (pdf). - Accelerated Alternating Direction Method of Multipliers
M. Kadkhodaie, K. Christakopoulou, M. Sanjabi, and A. Banerjee
International Conference on Knowledge Discovery and Data Mining (KDD), 2015 (pdf). - Structured Hedging for Resource Allocations with Leverage
N. Johnson and A. Banerjee
International Conference on Knowledge Discovery and Data Mining (KDD), 2015 (pdf). - Revisiting Non-Progressive Influence Models: Scalable Influence Maximization in Social Networks
G. Golnari, A. Taheri, A. Banerjee, and Z.-L. Zhang
Conference on Uncertainty in Artificial Intelligence (UAI), 2015 (pdf). - Muti-label structure learning with Ising model selection
A. Goncalves, F. Von Zuben, and A. Banerjee
International Joint Conference on Artificial Intelligence (IJCAI), 2015 (pdf). - Collaborative Ranking with a Push at the Top
K. Christakopoulou and A. Banerjee
International World Wide Web Conference (WWW), 2015 (pdf). - Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective
C. Jin , Q. Fu, H. Wang, W. Hendrix, Z. Chen, A. Agrawal, A. Banerjee, A. Choudhary
15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2015 (pdf). - A Spectral Algorithm for Inference in Hidden Semi-Markov Models
I. Melnyk and A. Banerjee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015 (pdf). (Oral)
Extended version (arxiv). - One-bit Compressed Sensing with the k-Support Norm
S. Chen and A. Banerjee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015(pdf). - Link Prediction Using Multiple Sources
K. Subbian, A. Banerjee, and S. Basu.
SIAM International Conference on Data Mining (SDM), 2015. - Online Resource Allocation with Structured Diversification
N. Johnson and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2015 (pdf). - Estimation with Norm Regularization
A. Banerjee, S. Chen, F. Fazayeli, and V. Sivakumar
Advances in Neural Information Processing Systems (NIPS), 2014 (pdf).
Extended version (from NIPS'14), 2015 (arxiv). - Generalized Dantzig Selector: Application to the k-support norm
S. Chatterjee, S. Chen, and A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2014 (pdf).
Extended version, 2014 (arxiv). - Parallel Direction Method of Multipliers
H. Wang, A. Banerjee, and Z.-Q. Luo
Advances in Neural Information Processing Systems (NIPS), 2014 (pdf).
Extended version, 2014 (pdf). - Bregman Alternating Direction Method of Multipliers
H. Wang and A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2014 (pdf).
Extended version, 2014 (arxiv). - Multi-task Sparse Structure Learning
A. R. Goncalves, P. Das, S. Chatterjee, V. Sivakumar, F. J. Von Zuben, A. Banerjee
International Conference on Information and Knowledge Management (CIKM), 2014 (pdf).
Extended version, 2014 (arxiv). - Online Portfolio Selection with Group Sparsity
P. Das, N. Johnson, and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2014 (pdf). - Gaussian Copula Precision Estimation with Missing Values
H. Wang, F. Fazayeli, S. Chatterjee, and A. Banerjee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2014 (pdf). - Climate Informatics
C. Monteleoni, G. A. Schmidt, F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser, M. Tippett, A. Banerjee, M. B. Blumenthal, A. R. Ganguly, J. E. Smerdon, M. Tedesco
Computational Intelligent Data Analysis for Sustainable Development, T. Yu, N. Chawla and S. Simoff, editors, 2013 (pdf). - Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty
A. R. Ganguly, E. Kodra, S. Chatterjee, A. Banerjee, and H. N Najm
Computational Intelligent Data Analysis for Sustainable Development, T. Yu, N. Chawla and S. Simoff, editors, 2013 (pdf). - Large Scale Distributed Sparse Precision Estimation
H. Wang, A. Banerjee, C. Hsieh, P. Ravikumar, and I. Dhillon
Advances in Neural Information Processing Systems (NIPS), 2013 (pdf). - Solving Combinatorial Optimization Problems using Relaxed Linear Programming: A High Performance Computing Perspective
C. Jin, Q. Fu, H. Wang, A. Agrawal, W. Hendrix, W.-K. Liao, M. A. Patwary, A. Banerjee, and A. Choudhary
BigMine workshop (KDD), 2013 (pdf).
(Best Paper Award) - Bethe-ADMM for Tree Decomposition based Parallel MAP inference
Q. Fu, H. Wang, and A. Banerjee
Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (pdf).
(Oral) - Online Lazy Updates for Portfolio Selection with Transaction Costs
P. Das, N. Johnson, and A. Banerjee
AAAI Conference on Artificial Intelligence (AAAI), 2013 (pdf). - Bregman Divergences and Triangle Inequality
S. Acharyya, A. Banerjee, and D. Boley
SIAM International Conference on Data Mining (SDM), 2013 (pdf). - Climate Multi-model Regression Using Spatial Smoothing
K. Subbian and A. Banerjee
SIAM International Conference on Data Mining (SDM), 2013 (pdf).
(Best Application Paper Award) - Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices
A. Cherian, S. Sra, A. Banerjee, and N. Papanikolopoulos
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 (pdf). - Online L1-Dictionary Learning with Application to Novel Document Detection
S. Kasiviswanathan, H. Wang, A. Banerjee, P. Melville
Advances in Neural Information Processing Systems (NIPS), 2012 (pdf, longer version). - A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation
C. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee
Advances in Neural Information Processing Systems (NIPS), 2012 (pdf). - If You are Happy and You Know It ... Tweet
A. A. Taheri, M. Tepper, A. Banerjee, and G. Sapiro.
ACM Conference on Information and Knowledge Management (CIKM), 2012 (pdf). - Online Alternating Direction Method
H. Wang and A. Banerjee.
International Conference on Machine Learning (ICML), 2012 (pdf).
Extended version, 2013 (arxiv). - Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization
H. Shan, J. Kattge, P. B. Reich, A. Banerjee, F. Schrodt, and M. Reichstein.
International Conference on Machine Learning (ICML), 2012 (pdf). - MAP Inference on Million Node Graphical Models: KL-divergence based Alternating Directions Method
Qiang Fu, Huahua Wang, Arindam Banerjee, Stefan Liess, and Peter K. Snyder
Technical Report TR-12-007
Department of Computer Science & Engineering, University of Minnesota, Twin Cities, 2012 (pdf). - Online Quadratically Constrained Convex Optimization with Applications to Risk Adjusted Portfolio Selection
Puja Das and Arindam Banerjee
Technical Report TR-12-008
Department of Computer Science & Engineering, University of Minnesota, Twin Cities, 2012 (pdf). - Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information
T. Zhou, H. Shan, A. Banerjee, and G. Sapiro.
SIAM International Conference on Data Mining (SDM), 2012 (pdf). - Sparse Group Lasso: Consistency and Climate Applications
S. Chatterjee, K. Steinhaeuser, A. Banerjee, S. Chatterjee, and A. Ganguly.
SIAM International Conference on Data Mining (SDM), 2012.
(Best Student Paper Award) - Drought Detection for the Last Century: A MRF-based Approach
Q. Fu, A. Banerjee, S. Liess, and P. Snyder.
SIAM International Conference on Data Mining (SDM), 2012 (pdf). - Emerging Topic Detection using Dictionary Learning
S. Kasiviswanathan, P. Melville, A. Banerjee, and V. Sindhwani.
ACM Conference on Information and Knowledge Management (CIKM), 2011 (pdf). - Efficient Similarity Search for Covariance Matrices via the Jensen-Bregman LogDet Divergence
A. Cherian, S. Sra, A. Banerjee, and N. Papanikolopoulos
International Conference on Computer Vision (ICCV), 2011 (pdf). - Common Component Analysis for Multiple Covariance Matrices
H. Wang, A. Banerjee, and D. Boley.
International Conference on Knowledge Discovery and Data Mining (KDD), 2011 (pdf, longer version). - Meta Optimization and its Application to Portfolio Selection
P. Das and A. Banerjee.
International Conference on Knowledge Discovery and Data Mining (KDD), 2011 (pdf). - Probabilistic Matrix Addition
A. Agovic, A. Banerjee, and S. Chatterjee.
International Conference on Machine Learning (ICML), 2011 (pdf). - Diagnosing Endometrial Carcinoma via Computer-Assisted Image Analysis
R. Sivalingam, G. Somasundaram, A. Ragipindi, A. Banerjee, V. Morellas, N. Papanikolopoulos, and A. Truskinovsky.
Annual Meeting of the United States & Canadian Academy of Pathology (USCAP), 2011. - Mixed-Membership Naive Bayes Models
H. Shan and A. Banerjee.
Data Mining and Knowledge Discovery (DMKD), 23(1), 1-62, 2011. - Bayesian Cluster Ensembles
H. Wang, H. Shan, and A. Banerjee.
Statistical Analysis and Data Mining, 4(1), 54-70, 2011. - Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
H. Shan and A. Banerjee.
IEEE International Conference on Data Mining (ICDM), 2010 (pdf,longer version). - A Generalized Linear Threshold Model for Multiple Cascades
N. Pathak, A. Banerjee, and J. Srivastava.
IEEE International Conference on Data Mining (ICDM), 2010 (pdf).
A related earlier Tech Report. - Anomaly Detection for Discrete Sequences: A Survey
V. Chandola, A. Banerjee, and V. Kumar.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2010 (to appear). - Analyzing aviation safety reports: From topic modeling to scalable multi-label classification
A. Agovic, H. Shan, and A. Banerjee.
Conference on Intelligent Data Understanding (CIDU), 2010 (pdf). - Gaussian Process Topic Models
A. Agovic and A. Banerjee.
Conference on Uncertainty in Artificial Intelligence (UAI), 2010 (pdf). - Keep it Simple with Time: A re-examination of Probabilistic Topic Detection Models
Q. He, K. Chang, E.-P. Lim, and A. Banerjee.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 32(10), 1795-1808, 2010. (pdf)