Selected Publications

Please see Google Scholar and DBLP for a more complete list.

Pre-prints/Extended versions

• De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors
  A. Banerjee, T. Chen, and Y. Zhou
  Preprint, 2020 (arxiv).
  
  
• R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting
  H. Goel, I. Melnyk, and A. Banerjee
  Preprint, 2017, (arXiv).
  
  
• Randomized Block Coordinate Descent for Online and Stochastic Optimization
  H. Wang and A. Banerjee
  Preprint, 2014 (arxiv).
  
  Selected Papers
  • Contextual Bandits with Online Neural Regression
    R. Deb, Y. Ban, S. Zuo, J. He, and A. Banerjee
    International Conference on Learning Representations (ICLR), 2024.
    Extended Version (arXiv).
    
    
  • SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
    A. J. Stewart, N. Lehmann, I. A. Corley, Y. Wang, Y. Chang, N. Braham, S. Sehgal, C. Robinson, and A. Banerjee
    Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2023.
    Extended version (arXiv).
    
    
  • AmbientFlow: Invertible generative models from incomplete, noisy measurements
    V. A. Kelkar, R. Deshpande, A. Banerjee, and M. A. Anastasio
    NeurIPS'23 Workhop on Deep Learning and Inverse Problems
    Transactions of Machine Learning Research (TMLR) , 2023. (arXiv)
    
    
  • Neural tangent kernel at initialization: Linear width suffices
    A. Banerjee, P. Cisneros-Velarde, L. Zhu, and M. Belkin
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.(pmlr)
    
    
  • 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.
    Extended Version (arXiv)
    
    
  • 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.(pmlr)
    
    
  • 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.
    Extended Version (arxiv).
    
    
  • 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)
    
    
  • Discovering Client and Intervention Patterns in Home Visiting Data
    K. Monsen, A. Banerjee, and P. Das.
    Western Journal of Nursing Research , 36(4), 2010.
    
    
  • Residual Bayesian Co-clustering for Matrix Approximation
    H. Shan and A. Banerjee.
    SIAM International Conference on Data Mining (SDM), 2010 (pdf).
    
    
  • Sparsity-cognizant overlapping co-clustering for behavior inference in social networks
    H. Zhu, G. Mateos, G. B. Giannakis, N. D. Sidiropoulus, and A. Banerjee
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , 2010 (pdf).
    
    
  • Discriminative Mixed-membership Models
    H. Shan, A. Banerjee, and N. Oza.
    IEEE International Conference on Data Mining (ICDM), (2009) (pdf).
    
    
  • Bayesian Overlapping Subspace Clustering
    Q. Fu and A. Banerjee.
    IEEE International Conference on Data Mining (ICDM), (2009) (pdf).
    
    
  • Approximation Algorithms for Tensor Clustering
    S. Jegelka, S. Sra, and A. Banerjee.
    The 20th International Conference on Algorithmic Learning Theory (ALT), (2009) (pdf).
    
    
  • Anomaly Detection: A Survey
    V. Chandola, A. Banerjee, and V. Kumar.
    ACM Computing Surveys, 41(3), Article 15, (2009) (pdf).
    
    
  • Anomaly Detection in Transportation Corridors using Manifold Embedding
    A. Agovic, A. Banerjee, A. Ganguly, and V. Protopopescu.
    Intelligent Data Analysis, 13(3), 435-455, (2009).
    
    
  • Symmetrized Bregman Divergences and Metrics
    A. Banerjee, D. Boley, and S. Acharyya
    Snowbird Learning Workshop, (2009).
    
    
  • Bayesian Cluster Ensembles
    H. Wang, H. Shan, A. Banerjee.
    SIAM International Conference on Data Mining (SDM), (2009) (pdf).
    
    
  • Semi-Supervised Learning of User-Preferred Travel Schedules
    A. Agovic, M. Gini, A. Banerjee.
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), (2009).
    
    
  • Use of computerized datasets and data mining methods to predict public health nurse home visiting client outcomes
    K. A. Monsen, M. J. Kerr, K. Abe, K. S. Martin, and A. Banerjee.
    World Academy of Nursing Science, (2009).
    
    
  • Discovering Effective Models for Home Visiting Practice
    K. A. Monsen, A. Banerjee, V. K. Ramadoss, P. Das, and K. Savik.
    Midwest Nursing Research Society Annual Meeting, (2009).
    
    
  • Bayesian Co-clustering
    H. Shan, A. Banerjee.
    IEEE International Conference on Data Mining (ICDM), (2008) (pdf).
    
    
  • Multiplicative Mixture Models for Overlapping Clustering
    Q. Fu, A. Banerjee.
    IEEE International Conference on Data Mining (ICDM), (2008) (pdf).
    
    
  • A Social Query Model for Distributed Search
    A. Banerjee, S. Basu.
    2nd ACM Workshop on Social Network Mining and Analysis (SNAKDD), (2008) (pdf).
    
    
  • Social Topic Models for Community Extraction
    N. Pathak, C. DeLong, K. Erickson, A. Banerjee.
    2nd ACM Workshop on Social Network Mining and Analysis (SNAKDD), (2008) (pdf).
    
    
  • Clustering with Balancing Constraints
    A. Banerjee, J. Ghosh.
    Constrained Clustering: Advances in Algorithms, Theory, and Applications, CRC Press, (2008).
    
    
  • Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection
    J. Wan, S. Kang, C. Tang, J. Yan, Y. Ren, J. Liu, X. Gao, A. Banerjee, L. Ellis, T. Li.
    Nucleic Acids Research (NAR), (2008).
    
    
  • I/O Scalable Bregman Clustering
    K. Hsu, A. Banerjee, J. Srivastava.
    Pacific-Asian Conference on Knowledge Discovery and Data Mining (PAKDD), (2008).
    
    
  • A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
    A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha.
    Journal of Machine Learning Research (JMLR), (2007), (pdf).
    
    
  • Latent Dirichlet Conditional Naive Bayes Models
    A. Banerjee and H. Shan.
    IEEE International Conference on Data Mining (ICDM) (2007) (pdf).
    
    
  • Anomaly Detection in Transportation Corridors using Manifold Embedding
    A. Agovic, A. Banerjee, A. Ganguly, and V. Protopopescu.
    1st International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD) (2007).
    
    
  • An Analysis of Logistic Models: Exponential Family Connections and Online Performance
    A. Banerjee.
    SIAM International Conference on Data Mining (SDM) (2007) (pdf).
    
    
  • Multi-way Clustering on Relation Graphs
    A. Banerjee, S. Basu, S. Merugu.
    SIAM International Conference on Data Mining (SDM) (2007) (pdf).
    (Best of SDM)
    
    
  • Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
    A. Banerjee, S. Basu.
    SIAM International Conference on Data Mining (SDM) (2007) (pdf, Longer version).
    
    
  • On Bayesian Bounds
    A. Banerjee.
    International Conference on Machine Learning (ICML) (2006) (pdf).
    
    
  • Scalable Clustering with Balancing Constraints
    A. Banerjee and J. Ghosh.
    Data Mining and Knowledge Discovery (2006) (pdf).
    
    
  • Probabilistic Semi-supervised Clustering with Constraints
    S. Basu, M. Bilenko, A. Banerjee, and R. Mooney
    Semi-Supervised Learning MIT Press, (2006).
    
    
  • A Clustering Based Approach to Perceptual Image Hashing
    V. Monga, A. Banerjee and B. Evans.
    IEEE Transactions on Information Forensics and Security (2006).
    
    
  • Model Based Overlapping Clustering
    A. Banerjee, C. Krumpelman, S. Basu, R. Mooney and J. Ghosh.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2005) (ps,pdf).
    
    
  • Clustering with Bregman Divergences
    A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh.
    Journal of Machine Learning Research (JMLR) (2005) (pdf).
    (Best Research Paper Award, University of Texas, Austin)
    
    
  • Clustering on the Unit Hypersphere using Von Mises-Fisher Distributions
    A. Banerjee, I. Dhillon, J. Ghosh and S. Sra.
    Journal of Machine Learning Research (JMLR) (2005) (pdf).
    
    
  • On the Optimality of Conditional Expectation as a Bregman Predictor
    A. Banerjee, X. Guo and H. Wang.
    IEEE Transactions on Information Theory, 51(7), 2664-2669 (2005) (pdf).
    
    
  • An Objective Evaluation Crietrion for Clustering
    A. Banerjee and J. Langford.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2004) (ps).
    
    
  • A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
    A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2004).
    
    
  • An Information Theoretic Analysis of Maximum Likelihood Mixture Estimation for Exponential Families
    A. Banerjee, I. Dhillon, J. Ghosh and S. Merugu.
    International Conference on Machine Learning (ICML) (2004). (ps).
    
    
  • Frequency Sensitive Competitive Learning for Balanced Clustering on High-dimensional Hyperspheres
    A. Banerjee, and J. Ghosh.
    IEEE Transactions on Neural Networks (2004) (ps).
    
    
  • Rate Distortion, Bregman Divergences and Maximum Likelihood Mixture Estimation
    A. Banerjee, I. Dhillon, J. Ghosh and S. Merugu.
    The Learning Workshop at Snowbird (2004).
    
    
  • Optimal Bregman Prediction and Jensen's Equality
    A. Banerjee, X. Guo and H. Wang.
    IEEE International Symposium on Information Theory (ISIT) (2004) (pdf).
    
    
  • Clustering with Bregman Divergences
    A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh.
    SIAM International Conference on Data Mining (SDM) (2004) (ps, pdf).
    (Best Paper Award)
    
    
  • Active Semi-supervision for Pairwise Constrained Clustering
    S. Basu, A. Banerjee and R. Mooney.
    SIAM International Conference on Data Mining (SDM) (2004) (pdf).
    
    
  • Mean Model Clustering
    A. Banerjee, and J. Ghosh. The Learning Workshop at Snowbird (2003) (ps).
    
    
  • Generative Model-based Clustering of Directional Data
    A. Banerjee, I. Dhillon, J. Ghosh and S. Sra.
    International Conference on Knowledge Discovery and Data Mining (KDD) (2003) (pdf).
    
    
  • Competitive Learning Mechanisms for Scalable, Incremental and Balanced Clustering of Streaming Texts
    A. Banerjee, and J. Ghosh.
    International Joint Conference on Neural Networks(IJCNN): Special Session on Incremental Learning (2003).
    
    
  • Semi-supervised Clustering by Seeding
    S. Basu, A. Banerjee, and R. Mooney.
    Proceedings of the International Conference on Machine Learning (ICML) (2002) (ps, pdf).
    
    
  • On Scaling Up Balanced Clustering Algorithms
    A. Banerjee, and J. Ghosh.
    Proceedings of the 2nd SIAM International Conference on Data Mining (SDM) (2002) (ps, pdf).
    
    
  • Frequency Sensitive Competitive Learning for Clustering on High Dimensional Hyperspheres
    A. Banerjee, and J. Ghosh.
    Proceedings of the International Joint Conference on Neural Networks (IJCNN) (2002) (ps, pdf).
    
    
  • Characterizing Visitors to a Website Across Multiple Sessions
    A. Banerjee and J. Ghosh.
    Proceedings of the National Science Foundation(NSF) Workshop on Next Generation Data Mining (2002).
    
    
  • Clickstream Clustering using Weighted Longest Common Subsequence
    A. Banerjee, and J. Ghosh.
    Proceedings of the 1st SIAM International Conference on Data Mining: Workshop on Web Mining (2001) (ps, pdf).