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ORCIDArindam Banerjee 0001
Person information
- affiliation: University of Illinois Urbana-Champaign, USA
- affiliation (former): University of Minnesota, Twin Cities
Other persons with the same name
- Arindam Banerjee — disambiguation page
- Arindam Banerjee 0002
![[0000-0002-1212-9704]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-1212-9704")
— Lehigh University, Department of Mechanical Engineering and Mechanics, Bethlehem, PA, USA - Arindam Banerjee 0003 — JIS College of Engineering, Kalyani, Nadia, West Bengal, India
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2020 – today
- 2025
[c116]Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee:
Conservative Contextual Bandits: Beyond Linear Representations. ICLR 2025- 2024
- [c115]Rohan Deb, Aadirupa Saha, Arindam Banerjee:
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. AISTATS 2024: 4546-4554 - [c114]Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee:
Contextual Bandits with Online Neural Regression. ICLR 2024 - [c113]Yunzhe Qi, Yikun Ban, Arindam Banerjee, Jingrui He:
Robust Neural Contextual Bandit against Adversarial Corruptions. NeurIPS 2024 - [i48]Arindam Banerjee, Qiaobo Li, Yingxue Zhou:
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees. CoRR abs/2406.07712 (2024) - [i47]Yi-Chia Chang, Adam J. Stewart, Favyen Bastani, Piper Wolters, Shreya Kannan, George R. Huber, Jingtong Wang, Arindam Banerjee:
On the Generalizability of Foundation Models for Crop Type Mapping. CoRR abs/2409.09451 (2024) - [i46]Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee:
Conservative Contextual Bandits: Beyond Linear Representations. CoRR abs/2412.06165 (2024) - 2023
- [c112]Adam J. Stewart, Nils Lehmann, Isaac A. Corley, Yi Wang, Yi-Chia Chang, Nassim Ait Ali Braham, Shradha Sehgal, Caleb Robinson, Arindam Banerjee:
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery. NeurIPS 2023 - [i45]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
Neural Exploitation and Exploration of Contextual Bandits. CoRR abs/2305.03784 (2023) - [i44]Adam J. Stewart, Nils Lehmann, Isaac A. Corley, Yi Wang, Yi-Chia Chang, Nassim Ait Ali Braham, Shradha Sehgal, Caleb Robinson, Arindam Banerjee:
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery. CoRR abs/2306.09424 (2023) - [i43]Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee:
Contextual Bandits with Online Neural Regression. CoRR abs/2312.07145 (2023) - 2022
- [c111]Sijie He, Xinyan Li, Laurie Trenary, Benjamin A. Cash, Timothy DelSole, Arindam Banerjee:
Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration. AAAI 2022: 4495-4503 - [c110]Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee:
TorchGeo: deep learning with geospatial data. SIGSPATIAL/GIS 2022: 19:1-19:12 - [c109]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022 - [c108]Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou:
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics. ICML 2022: 1412-1449 - [c107]Vidyashankar Sivakumar, Shiliang Zuo, Arindam Banerjee:
Smoothed Adversarial Linear Contextual Bandits with Knapsacks. ICML 2022: 20253-20277 - [c106]Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He:
Improved Algorithms for Neural Active Learning. NeurIPS 2022 - [c105]Yingxue Zhou, Xinyan Li, Arindam Banerjee:
Noisy Truncated SGD: Optimization and Generalization. SDM 2022: 468-476 - [e3]Arindam Banerjee, Zhi-Hua Zhou, Evangelos E. Papalexakis, Matteo Riondato:
Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022, Alexandria, VA, USA, April 28-30, 2022. SIAM 2022, ISBN 978-1-61197-717-2 [contents] - [i42]Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou:
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics. CoRR abs/2201.03064 (2022) - [i41]Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He:
Improved Algorithms for Neural Active Learning. CoRR abs/2210.00423 (2022) - 2021
- [c104]Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee:
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. AAAI 2021: 169-177 - [c103]Yingxue Zhou, Steven Wu, Arindam Banerjee:
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. ICLR 2021 - [c102]Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar:
Subseasonal climate prediction in the western US using Bayesian spatial models. UAI 2021: 961-970 - [e2]Arindam Banerjee, Kenji Fukumizu:
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event. Proceedings of Machine Learning Research 130, PMLR 2021 [contents] - [i40]Xinyan Li, Arindam Banerjee:
Experiments with Rich Regime Training for Deep Learning. CoRR abs/2102.13522 (2021) - [i39]Yingxue Zhou, Xinyan Li, Arindam Banerjee:
Noisy Truncated SGD: Optimization and Generalization. CoRR abs/2103.00075 (2021) - [i38]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. CoRR abs/2110.03177 (2021) - [i37]Sijie He, Xinyan Li, Laurie Trenary, Benjamin A. Cash, Timothy DelSole, Arindam Banerjee:
Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration. CoRR abs/2110.05196 (2021) - [i36]Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee:
TorchGeo: deep learning with geospatial data. CoRR abs/2111.08872 (2021) - 2020
- [c101]Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee:
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. ICML 2020: 9026-9035 - [c100]Robert A. Giaquinto, Arindam Banerjee:
Gradient Boosted Normalizing Flows. NeurIPS 2020 - [c99]Xinyan Li, Qilong Gu, Yingxue Zhou, Tiancong Chen, Arindam Banerjee:
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization. SDM 2020: 190-198 - [i35]Arindam Banerjee, Tiancong Chen, Yingxue Zhou:
De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors. CoRR abs/2002.09956 (2020) - [i34]Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee:
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. CoRR abs/2002.11332 (2020) - [i33]Robert A. Giaquinto, Arindam Banerjee:
Gradient Boosted Flows. CoRR abs/2002.11896 (2020) - [i32]Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee:
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. CoRR abs/2006.07972 (2020) - [i31]Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee:
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds. CoRR abs/2006.13501 (2020) - [i30]Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee:
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. CoRR abs/2007.03813 (2020)
2010 – 2019
- 2019
- [j25]Yolanda Gil, Suzanne A. Pierce, Hassan A. Babaie, Arindam Banerjee, Kirk D. Borne, Gary S. Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla P. Gomes, Mary C. Hill, John D. Horel, Leslie Hsu, Jim Kinter, Craig A. Knoblock, David M. Krum, Vipin Kumar, Pierre F. J. Lermusiaux, Yan Liu, Chris North, Victor Pankratius, Shanan Peters, Beth Plale, Allen Pope, Sai Ravela, Juan Restrepo, Aaron J. Ridley, Hanan Samet, Shashi Shekhar:
Intelligent systems for geosciences: an essential research agenda. Commun. ACM 62(1): 76-84 (2019) - [j24]Qilong Gu, Joshua D. Trzasko, Arindam Banerjee:
Scalable algorithms for locally low-rank matrix modeling. Knowl. Inf. Syst. 61(3): 1457-1484 (2019) - [c98]Sijie He, Xinyan Li, Vidyashankar Sivakumar, Arindam Banerjee:
Interpretable Predictive Modeling for Climate Variables with Weighted Lasso. AAAI 2019: 1385-1392 - [c97]Qilong Gu, Arindam Banerjee:
Sketched Iterative Algorithms for Structured Generalized Linear Models. IJCAI 2019: 2392-2398 - [c96]Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu:
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. NeurIPS 2019: 12578-12588 - [c95]Konstantina Christakopoulou, Arindam Banerjee:
Adversarial attacks on an oblivious recommender. RecSys 2019: 322-330 - [i29]André R. Gonçalves, Xiaoli Liu, Arindam Banerjee:
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence. CoRR abs/1907.04524 (2019) - [i28]Xinyan Li, Qilong Gu, Yingxue Zhou, Tiancong Chen, Arindam Banerjee:
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization. CoRR abs/1907.10732 (2019) - [i27]Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu:
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. CoRR abs/1910.04930 (2019) - 2018
- [j23]Xiaoli Liu, André R. Gonçalves, Peng Cao, Dazhe Zhao, Arindam Banerjee:
Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso. Comput. Medical Imaging Graph. 66: 100-114 (2018) - [j22]Lisa Singh, Amol Deshpande, Wenchao Zhou, Arindam Banerjee, Alex J. Bowers, Sorelle A. Friedler, H. V. Jagadish, George Karypis, Zoran Obradovic, Anil Vullikanti, Wangda Zuo:
NSF BIGDATA PI Meeting - Domain-Specific Research Directions and Data Sets. SIGMOD Rec. 47(3): 32-35 (2018) - [j21]Xiaoli Liu, Peng Cao, André R. Gonçalves, Dazhe Zhao, Arindam Banerjee:
Modeling Alzheimer's Disease Progression with Fused Laplacian Sparse Group Lasso. ACM Trans. Knowl. Discov. Data 12(6): 65:1-65:35 (2018) - [c94]Robert A. Giaquinto, Arindam Banerjee:
Topic Modeling on Health Journals With Regularized Variational Inference. AAAI 2018: 3021-3028 - [c93]Sheng Chen, Arindam Banerjee:
Sparse Linear Isotonic Models. AISTATS 2018: 1270-1279 - [c92]Robert A. Giaquinto, Arindam Banerjee:
DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora. ICDM 2018: 971-976 - [c91]Sheng Chen, Arindam Banerjee:
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression. NeurIPS 2018: 6617-6628 - [c90]Konstantina Christakopoulou, Arindam Banerjee:
Learning to Interact with Users: A Collaborative-Bandit Approach. SDM 2018: 612-620 - [c89]Amir Asiaee T., Hardik Goel, Shalini Ghosh, Vinod Yegneswaran, Arindam Banerjee:
Time Series Deinterleaving of DNS Traffic. IEEE Symposium on Security and Privacy Workshops 2018: 103-108 - [c88]Yingxue Zhou, Sheng Chen, Arindam Banerjee:
Stable Gradient Descent. UAI 2018: 766-775 - [i26]Robert A. Giaquinto, Arindam Banerjee:
Topic Modeling on Health Journals with Regularized Variational Inference. CoRR abs/1801.04958 (2018) - [i25]Amir Asiaee T., Samet Oymak, Kevin R. Coombes, Arindam Banerjee:
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient. CoRR abs/1806.04047 (2018) - [i24]Amir Asiaee T., Hardik Goel, Shalini Ghosh, Vinod Yegneswaran, Arindam Banerjee:
Time Series Deinterleaving of DNS Traffic. CoRR abs/1807.05650 (2018) - [i23]Konstantina Christakopoulou, Arindam Banerjee:
Adversarial Recommendation: Attack of the Learned Fake Users. CoRR abs/1809.08336 (2018) - [i22]Robert A. Giaquinto, Arindam Banerjee:
DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora. CoRR abs/1811.01931 (2018) - 2017
- [j20]Rosana Veroneze, Arindam Banerjee, Fernando J. Von Zuben:
Enumerating all maximal biclusters in numerical datasets. Inf. Sci. 379: 288-309 (2017) - [j19]Igor Melnyk, Arindam Banerjee:
A Spectral Algorithm for Inference in Hidden semi-Markov Models. J. Mach. Learn. Res. 18: 35:1-35:39 (2017) - [j18]Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael S. Steinbach, Arindam Banerjee, Auroop R. Ganguly, Shashi Shekhar, Nagiza F. Samatova, Vipin Kumar:
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Trans. Knowl. Data Eng. 29(10): 2318-2331 (2017) - [c87]André R. Gonçalves, Arindam Banerjee, Fernando J. Von Zuben:
Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning. AAAI 2017: 4509-4515 - [c86]Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee:
Recommendation with Capacity Constraints. CIKM 2017: 1439-1448 - [c85]Qilong Gu, Joshua D. Trzasko, Arindam Banerjee:
Scalable Algorithms for Locally Low-Rank Matrix Modeling. ICDM 2017: 137-146 - [c84]Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He, Yi Deng, Arindam Banerjee:
High-Dimensional Dependency Structure Learning for Physical Processes. ICDM 2017: 883-888 - [c83]Sheng Chen, Arindam Banerjee:
Robust Structured Estimation with Single-Index Models. ICML 2017: 712-721 - [c82]Vidyashankar Sivakumar, Arindam Banerjee:
High-Dimensional Structured Quantile Regression. ICML 2017: 3220-3229 - [c81]Sheng Chen, Arindam Banerjee:
Alternating Estimation for Structured High-Dimensional Multi-Response Models. NIPS 2017: 2838-2848 - [r4]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Active Learning. Encyclopedia of Machine Learning and Data Mining 2017: 42-56 - [r3]Arindam Banerjee, Hanhuai Shan:
Model-Based Clustering. Encyclopedia of Machine Learning and Data Mining 2017: 848-852 - [i21]Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee:
Recommendation under Capacity Constraints. CoRR abs/1701.05228 (2017) - [i20]André R. Gonçalves, Arindam Banerjee, Fernando J. Von Zuben:
Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning. CoRR abs/1701.08840 (2017) - [i19]Qilong Gu, Arindam Banerjee:
High Dimensional Structured Superposition Models. CoRR abs/1705.10886 (2017) - [i18]Hardik Goel, Igor Melnyk, Arindam Banerjee:
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting. CoRR abs/1709.03159 (2017) - [i17]Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He, Yi Deng, Arindam Banerjee:
High-Dimensional Dependency Structure Learning for Physical Processes. CoRR abs/1709.03891 (2017) - 2016
- [j17]Igor Melnyk, Bryan L. Matthews, Hamed Valizadegan, Arindam Banerjee, Nikunj C. Oza:
Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems. J. Aerosp. Inf. Syst. 13(4): 161-173 (2016) - [j16]André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee:
Multi-task Sparse Structure Learning with Gaussian Copula Models. J. Mach. Learn. Res. 17: 33:1-33:30 (2016) - [c80]Soumyadeep Chatterjee, Stefan Liess, Arindam Banerjee, Vipin Kumar:
Understanding Dominant Factors for Precipitation over the Great Lakes Region. AAAI 2016: 3821-3827 - [c79]Igor Melnyk, Arindam Banerjee:
Estimating Structured Vector Autoregressive Models. ICML 2016: 830-839 - [c78]Farideh Fazayeli, Arindam Banerjee:
Generalized Direct Change Estimation in Ising Model Structure. ICML 2016: 2281-2290 - [c77]Igor Melnyk, Arindam Banerjee, Bryan L. Matthews, Nikunj C. Oza:
Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems. KDD 2016: 1065-1074 - [c76]Sheng Chen, Arindam Banerjee:
Structured Matrix Recovery via the Generalized Dantzig Selector. NIPS 2016: 3252-3260 - [c75]Qilong Gu, Arindam Banerjee:
High Dimensional Structured Superposition Models. NIPS 2016: 3684-3692 - [c74]Farideh Fazayeli, Arindam Banerjee:
The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications. ECML/PKDD (1) 2016: 648-664 - [c73]Amir Asiaee T., Soumyadeep Chatterjee, Arindam Banerjee:
High Dimensional Structured Estimation with Noisy Designs. SDM 2016: 801-809 - [i16]Igor Melnyk, Arindam Banerjee, Bryan L. Matthews, Nikunj C. Oza:
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems. CoRR abs/1602.06550 (2016) - [i15]Farideh Fazayeli, Arindam Banerjee:
Generalized Direct Change Estimation in Ising Model Structure. CoRR abs/1606.05302 (2016) - [i14]Nicholas Johnson, Vidyashankar Sivakumar, Arindam Banerjee:
Structured Stochastic Linear Bandits. CoRR abs/1606.05693 (2016) - [i13]Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael S. Steinbach, Arindam Banerjee, Auroop R. Ganguly, Shashi Shekhar, Nagiza F. Samatova, Vipin Kumar:
Theory-guided Data Science: A New Paradigm for Scientific Discovery. CoRR abs/1612.08544 (2016) - 2015
- [j15]André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee:
A Multitask Learning View on the Earth System Model Ensemble. Comput. Sci. Eng. 17(6): 35-42 (2015) - [c72]Sheng Chen, Arindam Banerjee:
One-bit Compressed Sensing with the k-Support Norm. AISTATS 2015 - [c71]Igor Melnyk, Arindam Banerjee:
A Spectral Algorithm for Inference in Hidden semi-Markov Models. AISTATS 2015 - [c70]Chen Jin, Qiang Fu, Huahua Wang, William Hendrix, Zhengzhang Chen, Ankit Agrawal, Arindam Banerjee, Alok N. Choudhary:
Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective. CCGRID 2015: 565-575 - [c69]Arindam Banerjee, Sheng Chen, Vidyashankar Sivakumar:
Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs. COLT 2015: 1752-1755 - [c68]André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee:
Multi-Label Structure Learning with Ising Model Selection. IJCAI 2015: 3525-3531 - [c67]Nicholas Johnson, Arindam Banerjee:
Structured Hedging for Resource Allocations with Leverage. KDD 2015: 477-486 - [c66]Mojtaba Kadkhodaie, Konstantina Christakopoulou, Maziar Sanjabi, Arindam Banerjee:
Accelerated Alternating Direction Method of Multipliers. KDD 2015: 497-506 - [c65]Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh:
Unified View of Matrix Completion under General Structural Constraints. NIPS 2015: 1180-1188 - [c64]Vidyashankar Sivakumar, Arindam Banerjee, Pradeep Ravikumar:
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs. NIPS 2015: 2206-2214 - [c63]Sheng Chen, Arindam Banerjee:
Structured Estimation with Atomic Norms: General Bounds and Applications. NIPS 2015: 2908-2916 - [c62]Karthik Subbian, Arindam Banerjee, Sugato Basu:
PLUMS: Predicting Links Using Multiple Sources. SDM 2015: 370-378 - [c61]Nicholas Johnson, Arindam Banerjee:
Online Resource Allocation with Structured Diversification. SDM 2015: 595-603 - [c60]Golshan Golnari, Amir Asiaee T., Arindam Banerjee, Zhi-Li Zhang:
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization in Social Networks. UAI 2015: 316-325 - [c59]Konstantina Christakopoulou, Arindam Banerjee:
Collaborative Ranking with a Push at the Top. WWW 2015: 205-215 - [i12]Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar:
Estimation with Norm Regularization. CoRR abs/1505.02294 (2015) - 2014
- [j14]James H. Faghmous, Arindam Banerjee, Shashi Shekhar, Michael S. Steinbach, Vipin Kumar, Auroop R. Ganguly, Nagiza F. Samatova:
Theory-Guided Data Science for Climate Change. Computer 47(11): 74-78 (2014) - [c58]Puja Das, Nicholas Johnson, Arindam Banerjee:
Online Portfolio Selection with Group Sparsity. AAAI 2014: 1185-1191 - [c57]Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee:
Gaussian Copula Precision Estimation with Missing Values. AISTATS 2014: 978-986 - [c56]André R. Gonçalves, Puja Das, Soumyadeep Chatterjee, Vidyashankar Sivakumar, Fernando J. Von Zuben, Arindam Banerjee:
Multi-task Sparse Structure Learning. CIKM 2014: 451-460 - [c55]Farideh Fazayeli, Arindam Banerjee, Jens Kattge, Franziska Schrodt, Peter B. Reich:
Uncertainty Quantified Matrix Completion Using Bayesian Hierarchical Matrix Factorization. ICMLA 2014: 312-317 - [c54]Huahua Wang, Arindam Banerjee, Zhi-Quan Luo:
Parallel Direction Method of Multipliers. NIPS 2014: 181-189 - [c53]Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar:
Estimation with Norm Regularization. NIPS 2014: 1556-1564 - [c52]Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee:
Generalized Dantzig Selector: Application to the k-support norm. NIPS 2014: 1934-1942 - [c51]Huahua Wang, Arindam Banerjee:
Bregman Alternating Direction Method of Multipliers. NIPS 2014: 2816-2824 - [e1]Mohammed Javeed Zaki, Zoran Obradovic, Pang-Ning Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy:
Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014. SIAM 2014, ISBN 978-1-61197-344-0 [contents] - [r2]Hanhuai Shan, Arindam Banerjee:
Discriminative Mixed Membership Models. Handbook of Mixed Membership Models and Their Applications 2014: 325-350 - [i11]Rosana Veroneze, Arindam Banerjee, Fernando J. Von Zuben:
Enumerating all maximal biclusters in real-valued datasets. CoRR abs/1403.3562 (2014) - [i10]Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee:
Generalized Dantzig Selector: Application to the k-support norm. CoRR abs/1406.5291 (2014) - [i9]Huahua Wang, Arindam Banerjee:
Randomized Block Coordinate Descent for Online and Stochastic Optimization. CoRR abs/1407.0107 (2014) - [i8]Igor Melnyk, Arindam Banerjee:
A Spectral Algorithm for Inference in Hidden Semi-Markov Models. CoRR abs/1407.3422 (2014) - [i7]André R. Gonçalves, Puja Das, Soumyadeep Chatterjee, Vidyashankar Sivakumar, Fernando J. Von Zuben, Arindam Banerjee:
Multi-task Sparse Structure Learning. CoRR abs/1409.0272 (2014) - [i6]Golshan Golnari, Amir Asiaee T., Arindam Banerjee, Zhi-Li Zhang:
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization. CoRR abs/1412.5718 (2014) - 2013
- [j13]Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos:
Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices. IEEE Trans. Pattern Anal. Mach. Intell. 35(9): 2161-2174 (2013) - [c50]Puja Das, Nicholas Johnson, Arindam Banerjee:
Online Lazy Updates for Portfolio Selection with Transaction Costs. AAAI 2013: 202-208 - [c49]Igor Melnyk, Pranjul Yadav, Michael S. Steinbach, Jaideep Srivastava, Vipin Kumar, Arindam Banerjee:
Detection of Precursors to Aviation Safety Incidents Due to Human Factors. ICDM Workshops 2013: 407-412 - [c48]Chen Jin, Qiang Fu, Huahua Wang, Ankit Agrawal, William Hendrix, Wei-keng Liao, Md. Mostofa Ali Patwary, Arindam Banerjee, Alok N. Choudhary:
Solving combinatorial optimization problems using relaxed linear programming: a high performance computing perspective. BigMine 2013: 39-46 - [c47]Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Large Scale Distributed Sparse Precision Estimation. NIPS 2013: 584-592 - [c46]Karthik Subbian, Arindam Banerjee:
Climate Multi-model Regression Using Spatial Smoothing. SDM 2013: 324-332 - [c45]Sreangsu Acharyya, Arindam Banerjee, Daniel Boley:
Bregman Divergences and Triangle Inequality. SDM 2013: 476-484 - [c44]Qiang Fu, Huahua Wang, Arindam Banerjee:
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference. UAI 2013 - [p2]Amrudin Agovic, Arindam Banerjee:
Semisupervised Clustering. Data Clustering: Algorithms and Applications 2013: 505-534 - [i5]Huahua Wang, Arindam Banerjee:
Bregman Alternating Direction Method of Multipliers. CoRR abs/1306.3203 (2013) - [i4]Huahua Wang, Arindam Banerjee:
Online Alternating Direction Method (longer version). CoRR abs/1306.3721 (2013) - [i3]Qiang Fu, Huahua Wang, Arindam Banerjee:
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference. CoRR abs/1309.6829 (2013) - 2012
- [j12]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Anomaly Detection for Discrete Sequences: A Survey. IEEE Trans. Knowl. Data Eng. 24(5): 823-839 (2012) - [c43]Amir Asiaee T., Mariano Tepper, Arindam Banerjee, Guillermo Sapiro:
If you are happy and you know it... tweet. CIKM 2012: 1602-1606 - [c42]Hanhuai Shan, Jens Kattge, Peter B. Reich, Arindam Banerjee, Franziska Schrodt, Markus Reichstein:
Gap Filling in the Plant Kingdom - Trait Prediction Using Hierarchical Probabilistic Matrix Factorization. ICML 2012 - [c41]Huahua Wang, Arindam Banerjee:
Online Alternating Direction Method. ICML 2012 - [c40]Shiva Prasad Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville:
Online L1-Dictionary Learning with Application to Novel Document Detection. NIPS 2012: 2267-2275 - [c39]Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee:
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. NIPS 2012: 2339-2347 - [c38]Qiang Fu, Arindam Banerjee, Stefan Liess, Peter K. Snyder:
Drought Detection of the Last Century: An MRF-based Approach. SDM 2012: 24-34 - [c37]Soumyadeep Chatterjee, Karsten Steinhaeuser, Arindam Banerjee, Snigdhansu Chatterjee, Auroop R. Ganguly:
Sparse Group Lasso: Consistency and Climate Applications. SDM 2012: 47-58 - [c36]Tinghui Zhou, Hanhuai Shan, Arindam Banerjee, Guillermo Sapiro:
Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information. SDM 2012: 403-414 - [i2]Amrudin Agovic, Arindam Banerjee:
Gaussian Process Topic Models. CoRR abs/1203.3462 (2012) - 2011
- [j11]Hanhuai Shan, Arindam Banerjee:
Mixed-membership naive Bayes models. Data Min. Knowl. Discov. 23(1): 1-62 (2011) - [j10]Hongjun Wang, Hanhuai Shan, Arindam Banerjee:
Bayesian cluster ensembles. Stat. Anal. Data Min. 4(1): 54-70 (2011) - [c35]Shiva Prasad Kasiviswanathan, Prem Melville, Arindam Banerjee, Vikas Sindhwani:
Emerging topic detection using dictionary learning. CIKM 2011: 745-754 - [c34]Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos:
Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence. ICCV 2011: 2399-2406 - [c33]Soumyadeep Chatterjee, Arindam Banerjee, Snigdhansu Chatterjee, Auroop R. Ganguly:
Sparse Group Lasso for Regression on Land Climate Variables. ICDM Workshops 2011: 1-8 - [c32]Amrudin Agovic, Arindam Banerjee, Snigdhansu Chatterjee:
Probabilistic Matrix Addition. ICML 2011: 1025-1032 - [c31]Huahua Wang, Arindam Banerjee, Daniel Boley:
Common component analysis for multiple covariance matrices. KDD 2011: 956-964 - [c30]Puja Das, Arindam Banerjee:
Meta optimization and its application to portfolio selection. KDD 2011: 1163-1171 - 2010
- [j9]Qi He, Kuiyu Chang, Ee-Peng Lim, Arindam Banerjee:
Keep It Simple with Time: A Reexamination of Probabilistic Topic Detection Models. IEEE Trans. Pattern Anal. Mach. Intell. 32(10): 1795-1808 (2010) - [c29]Amrudin Agovic, Hanhuai Shan, Arindam Banerjee:
Analyzing Aviation Safety Reports: From Topic Modeling to Scalable Multi-Label Classification. CIDU 2010: 83-97 - [c28]Hao Zhu, Gonzalo Mateos, Georgios B. Giannakis, Nicholas D. Sidiropoulos, Arindam Banerjee:
Sparsity-cognizant overlapping co-clustering for behavior inference in social networks. ICASSP 2010: 3534-3537 - [c27]Nishith Pathak, Arindam Banerjee, Jaideep Srivastava:
A Generalized Linear Threshold Model for Multiple Cascades. ICDM 2010: 965-970 - [c26]Hanhuai Shan, Arindam Banerjee:
Generalized Probabilistic Matrix Factorizations for Collaborative Filtering. ICDM 2010: 1025-1030 - [c25]Hanhuai Shan, Arindam Banerjee:
Residual Bayesian Co-clustering for Matrix Approximation. SDM 2010: 223-234 - [c24]Amrudin Agovic, Arindam Banerjee:
Gaussian Process Topic Models. UAI 2010: 10-19 - [r1]Arindam Banerjee, Hanhuai Shan:
Model-Based Clustering. Encyclopedia of Machine Learning 2010: 686-689
2000 – 2009
- 2009
- [j8]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Anomaly detection: A survey. ACM Comput. Surv. 41(3): 15:1-15:58 (2009) - [j7]Amrudin Agovic, Arindam Banerjee, Auroop R. Ganguly, Vladimir Protopopescu:
Anomaly detection using manifold embedding and its applications in transportation corridors. Intell. Data Anal. 13(3): 435-455 (2009) - [c23]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Tensor Clustering. ALT 2009: 368-383 - [c22]Amrudin Agovic, Maria L. Gini, Arindam Banerjee:
Semi-supervised learning of user-preferred travel schedules. AAMAS (2) 2009: 1151-1152 - [c21]Hanhuai Shan, Arindam Banerjee, Nikunj C. Oza:
Discriminative Mixed-Membership Models. ICDM 2009: 466-475 - [c20]Qiang Fu, Arindam Banerjee:
Bayesian Overlapping Subspace Clustering. ICDM 2009: 776-781 - [c19]Hongjun Wang, Hanhuai Shan, Arindam Banerjee:
Bayesian Cluster Ensembles. SDM 2009: 211-222 - 2008
- [c18]Hanhuai Shan, Arindam Banerjee:
Bayesian Co-clustering. ICDM 2008: 530-539 - [c17]Qiang Fu, Arindam Banerjee:
Multiplicative Mixture Models for Overlapping Clustering. ICDM 2008: 791-796 - [c16]Kuo-Wei Hsu, Arindam Banerjee, Jaideep Srivastava:
I/O Scalable Bregman Co-clustering. PAKDD 2008: 896-903 - [i1]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Bregman Co-clustering and Tensor Clustering. CoRR abs/0812.0389 (2008) - 2007
- [j6]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha:
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation. J. Mach. Learn. Res. 8: 1919-1986 (2007) - [c15]Arindam Banerjee, Hanhuai Shan:
Latent Dirichlet Conditional Naive-Bayes Models. ICDM 2007: 421-426 - [c14]Arindam Banerjee, Sugato Basu, Srujana Merugu:
Multi-way Clustering on Relation Graphs. SDM 2007: 145-156 - [c13]Arindam Banerjee:
An Analysis of Logistic Models: Exponential Family Connections and Online Performance. SDM 2007: 204-215 - [c12]Arindam Banerjee, Sugato Basu:
Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning. SDM 2007: 431-436 - 2006
- [j5]Arindam Banerjee, Joydeep Ghosh:
Scalable Clustering Algorithms with Balancing Constraints. Data Min. Knowl. Discov. 13(3): 365-395 (2006) - [j4]Vishal Monga, Arindam Banerjee, Brian L. Evans:
A clustering based approach to perceptual image hashing. IEEE Trans. Inf. Forensics Secur. 1(1): 68-79 (2006) - [c11]Arindam Banerjee:
On Bayesian bounds. ICML 2006: 81-88 - [p1]Sugato Basu, Mikhail Bilenko, Arindam Banerjee, Raymond J. Mooney:
Probabilistic Semi-Supervised Clustering with Constraints. Semi-Supervised Learning 2006: 73-102 - 2005
- [j3]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra:
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions. J. Mach. Learn. Res. 6: 1345-1382 (2005) - [j2]Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh:
Clustering with Bregman Divergences. J. Mach. Learn. Res. 6: 1705-1749 (2005) - [c10]Arindam Banerjee, Chase Krumpelman, Joydeep Ghosh, Sugato Basu, Raymond J. Mooney:
Model-based overlapping clustering. KDD 2005: 532-537 - 2004
- [j1]Arindam Banerjee, Joydeep Ghosh:
Frequency-sensitive competitive learning for scalable balanced clustering on high-dimensional hyperspheres. IEEE Trans. Neural Networks 15(3): 702-719 (2004) - [c9]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu:
An information theoretic analysis of maximum likelihood mixture estimation for exponential families. ICML 2004 - [c8]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha:
A generalized maximum entropy approach to bregman co-clustering and matrix approximation. KDD 2004: 509-514 - [c7]Arindam Banerjee, John Langford:
An objective evaluation criterion for clustering. KDD 2004: 515-520 - [c6]Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh:
Clustering with Bregman Divergences. SDM 2004: 234-245 - [c5]Sugato Basu, Arindam Banerjee, Raymond J. Mooney:
Active Semi-Supervision for Pairwise Constrained Clustering. SDM 2004: 333-344 - 2003
- [c4]Arindam Banerjee, Joydeep Ghosh:
Competitive learning mechanisms for scalable, incremental and balanced clustering of streaming texts. IJCNN 2003: 2697-2702 - [c3]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra:
Generative model-based clustering of directional data. KDD 2003: 19-28 - 2002
- [c2]Sugato Basu, Arindam Banerjee, Raymond J. Mooney:
Semi-supervised Clustering by Seeding. ICML 2002: 27-34 - [c1]Arindam Banerjee, Joydeep Ghosh:
On Scaling Up Balanced Clustering Algorithms. SDM 2002: 333-349
Coauthor Index
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