CaltechAUTHORS
  A Caltech Library Service

Items where Person is "Anandkumar-A"

Up a level
Export as [feed] Atom [feed] RSS 1.0 [feed] RSS 2.0
Group by: Date | Item Type | First Author | No Grouping
Jump to: 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006
Number of items: 107.

2019

Huang, Yujia and Dai, Sihui and Nguyen, Tan et al. (2019) Out-of-Distribution Detection Using Neural Rendering Generative Models. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154251302

Ross, Zachary E. and Trugman, Daniel T. and Azizzadenesheli, Kamyar et al. (2019) Directivity Modes of Earthquake Populations with Unsupervised Learning. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154247884

Zhang, Amy and Lipton, Zachary C. and Pineda, Luis et al. (2019) Learning Causal State Representations of Partially Observable Environments. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154244448

Liu, Anqi and Shi, Guanya and Chung, Soon-Jo et al. (2019) Robust Regression for Safe Exploration in Control. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154307157

Schäfer, Florian and Anandkumar, Anima (2019) Competitive Gradient Descent. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154241002

Shi, Guanya and Shi, Xichen and O'Connell, Michael et al. (2019) Neural Lander: Stable Drone Landing Control using Learned Dynamics. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 9784-9790. ISBN 978-1-5386-6027-0. http://resolver.caltech.edu/CaltechAUTHORS:20190205-100744248

Hu, Peiyun and Lipton, Zachary C. and Anandkumar, Anima et al. (2019) Active Learning with Partial Feedback. In: 7th International Conference on Learning Representations (ICLR 2019), 6-9 May 2019, New Orleans, LA. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085746172

Azizzadenesheli, Kamyar and Liu, Anqi and Yang, Fanny et al. (2019) Regularized Learning for Domain Adaptation under Label Shifts. In: Seventh International Conference on Learning Representations (ICLR 2019), 6-9 May 2019, New Orleans, LA. (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085824665

Kolbeinsson, Arinbjörn and Kossaifi, Jean and Panagakis, Yannis et al. (2019) Robust Deep Networks with Randomized Tensor Regression Layers. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154237568

Kolbeinsson, Arinbjörn and Kossaifi, Jean and Panagakis, Yannis et al. (2019) Stochastically Rank-Regularized Tensor Regression Networks. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-162823824

Kossaifi, Jean and Panagakis, Yannis and Anandkumar, Animashree et al. (2019) TensorLy: Tensor Learning in Python. Journal of Machine Learning Research, 20 (26). pp. 1-6. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20190228-133230688

Shi, Yang and Anandkumar, Animashree (2019) Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190905-154234092

Shi, Yang and Anandkumar, Animashree (2019) Multi-dimensional Tensor Sketch. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085821224

Lale, Sahin and Azizzadenesheli, Kamyar and Anandkumar, Anima et al. (2019) Stochastic Linear Bandits with Hidden Low Rank Structure. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085817695

2018

Ho, Nhat and Nguyen, Tan and Patel, Ankit et al. (2018) Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085814265

Cvitkovic, Milan and Singh, Badal and Anandkumar, Anima (2018) Open Vocabulary Learning on Source Code with a Graph-Structured Cache. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085810844

Azizzadenesheli, Kamyar and Bera, Manish Kumar and Anandkumar, Animashree (2018) Trust Region Policy Optimization for POMDPs. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085807408

Bernstein, Jeremy and Zhao, Jiawei and Azizzadenesheli, Kamyar et al. (2018) signSGD with Majority Vote is Communication Efficient And Fault Tolerant. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085803968

Furlanello, Tommaso and Lipton, Zachary C. and Tschannen, Michael et al. (2018) Born Again Neural Networks. Proceedings of Machine Learning Research, 80 . pp. 1607-1616. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085757099

Tschannen, Michael and Khanna, Aran and Anandkumar, Animashree (2018) StrassenNets: Deep Learning with a Multiplication Budget. Proceedings of Machine Learning Research, 80 . pp. 4985-4994. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085739295

Bernstein, Jeremy and Wang, Yu-Xiang and Azizzadenesheli, Kamyar et al. (2018) signSGD: Compressed Optimisation for Non-Convex Problems. Proceedings of Machine Learning Research, 80 . pp. 560-569. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085742729

Athiwaratkun, Ben and Wilson, Andrew Gordon and Anandkumar, Anima (2018) Probabilistic FastText for Multi-Sense Word Embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics , Stroudsburg, PA, Art. No. P18-1001 . ISBN 978-1-948087-32-2. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085800530

Arabshahi, Forough and Singh, Sameer and Anandkumar, Animashree (2018) Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085732435

Khetan, Ashish and Lipton, Zachary C. and Anandkumar, Animashree (2018) Learning From Noisy Singly-labeled Data. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085735866

Dhillon, Guneet S. and Azizzadenesheli, Kamyar and Lipton, Zachary C. et al. (2018) Stochastic Activation Pruning for Robust Adversarial Defense. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085749625

Shi, Yang and Furlanello, Tommaso and Zha, Sheng et al. (2018) Question Type Guided Attention in Visual Question Answering. In: Computer Vision – ECCV 2018. Lecture Notes in Computer Science. Vol.IV. No.11208. Springer Nature , Cham, Switzerland, pp. 158-175. ISBN 978-3-030-01224-3. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085753056

Shen, Yanyao and Yun, Hyokun and Lipton, Zachary C. et al. (2018) Deep Active Learning for Named Entity Recognition. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085725408

Azizzadenesheli, Kamyar and Brunskill, Emma and Anandkumar, Animashree (2018) Efficient Exploration Through Bayesian Deep Q-Networks. In: 2018 Information Theory and Applications Workshop (ITA). IEEE , Piscataway, NJ, pp. 1-9. ISBN 9781728101248. http://resolver.caltech.edu/CaltechAUTHORS:20181101-121222226

2017

Yu, Rose and Zheng, Stephan and Anandkumar, Anima et al. (2017) Long-term Forecasting using Tensor-Train RNNs. . (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20190205-113450468

Kossaifi, Jean and Lipton, Zachary and Khanna, Aran et al. (2017) Tensor Regression Networks. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085728859

Anandkumar, Anima and Deng, Yuan and Ge, Rong et al. (2017) Homotopy Analysis for Tensor PCA. Proceedings of Machine Learning Research, 65 . pp. 79-104. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190401-123333151

Kossaifi, Jean and Khanna, Aran and Lipton, Zachary et al. (2017) Tensor Contraction Layers for Parsimonious Deep Nets. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE , Piscataway, NJ, pp. 1940-1946. ISBN 978-1-5386-0733-6. http://resolver.caltech.edu/CaltechAUTHORS:20180321-103123441

Shi, Yang and Furlanello, Tommaso and Anandkumar, Animashree (2017) Compact Tensor Pooling for Visual Question Answering. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190402-104101850

Azizzadenesheli, Kamyar and Lazaric, Alessandro and Anandkumar, Animashree (2017) Experimental results: Reinforcement Learning of POMDPs using Spectral Methods. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085721979

Agarwal, Alekh and Anandkumar, Animashree and Netrapalli, Praneeth (2017) A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Transactions on Information Theory, 63 (1). pp. 575-592. ISSN 0018-9448. http://resolver.caltech.edu/CaltechAUTHORS:20170920-111802806

Anandkumar, Animashree and Ge, Rong and Janzamin, Majid (2017) Analyzing Tensor Power Method Dynamics in Overcomplete Regime. Journal of Machine Learning Research, 18 (22). pp. 1-40. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170920-110910164

2016

Agarwal, Alekh and Anandkumar, Animashree and Jain, Prateek et al. (2016) Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM Journal of Optimization, 26 (4). pp. 2775-2799. ISSN 1052-6234. http://resolver.caltech.edu/CaltechAUTHORS:20170927-090108498

Wang, Yining and Anandkumar, Animashree (2016) Online and Differentially-Private Tensor Decomposition. In: Neural Information Processing Systems 2016. Neural Information Processing Systems Foundation , La Jolla, CA, Art. No. 6498. ISBN 9781510838819. http://resolver.caltech.edu/CaltechAUTHORS:20190401-123322786

Shi, Yang and Niranjan, U. N. and Anandkumar, Animashree et al. (2016) Tensor Contractions with Extended BLAS Kernels on CPU and GPU. In: 2016 IEEE 23rd International Conference on High Performance Computing. IEEE , Piscataway, NJ, pp. 193-202. ISBN 978-1-5090-5411-4. http://resolver.caltech.edu/CaltechAUTHORS:20170920-112945692

Azizzadenesheli, Kamyar and Lazaric, Alessandro and Anandkumar, Animashree (2016) Reinforcement Learning in Rich-Observation MDPs using Spectral Methods. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190327-085718507

Gitter, Anthony and Huang, Furong and Valluvan, Ragupathyraj et al. (2016) Unsupervised learning of transcriptional regulatory networks via latent tree graphical models. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123329660

Azizzadenesheli, Kamyar and Lazaric, Alessandro and Anandkumar, Animashree (2016) Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. Proceedings of Machine Learning Research, 49 . pp. 1639-1642. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190401-123326217

Azizzadenesheli, Kamyar and Lazaric, Alessandro and Anandkumar, Animashree (2016) Reinforcement Learning of POMDPs using Spectral Methods. Proceedings of Machine Learning Research, 49 . pp. 193-256. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190401-123310700

Arabshahi, Forough and Anandkumar, Animashree (2016) Spectral Methods for Correlated Topic Models. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123319347

Sedghi, Hanie and Anandkumar, Anima (2016) Training Input-Output Recurrent Neural Networks through Spectral Methods. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123315920

Anandkumar, Anima and Ge, Rong (2016) Efficient approaches for escaping higher order saddle points in non-convex optimization. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123307245

2015

Wang, Yining and Tung, Hsiao-Yu and Smola, Alex et al. (2015) Fast and Guaranteed Tensor Decomposition via Sketching. In: NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems. Vol.1. MIT Press , Cambridge, MA, pp. 991-999. http://resolver.caltech.edu/CaltechAUTHORS:20190401-123256793

Huang, Furong and Niranjan, U. N. and Hakeem, Mohammad Umar et al. (2015) Online Tensor Methods for Learning Latent Variable Models. Journal of Machine Learning Research, 16 . pp. 2797-2835. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-111140656

Anandkumar, Animashree and Hsu, Daniel and Janzamin, Majid et al. (2015) When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. Journal of Machine Learning Research, 16 . pp. 2643-2694. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-144026647

Arabshahi, Forough and Huang, Furong and Anandkumar, Animashree et al. (2015) Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]. In: 2015 IEEE International Conference on Data Mining. IEEE , Piscataway, NJ, pp. 697-702. ISBN 978-1-4673-9504-5. http://resolver.caltech.edu/CaltechAUTHORS:20170927-083449769

Anandkumar, Animashree and Ge, Rong and Hsu, Daniel et al. (2015) Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). In: Algorithmic Learning Theory. Lecture Notes in Computer Science. No.9355. Springer , Cham, Switzerland, pp. 19-38. ISBN 978-3-319-24485-3. http://resolver.caltech.edu/CaltechAUTHORS:20170920-143816175

Anandkumar, Animashree and Jain, Prateek and Shi, Yang et al. (2015) Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123303824

Janzamin, Majid and Sedghi, Hanie and Anandkumar, Anima (2015) Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123300368

Huang, Furong and Anandkumar, Animashree (2015) Convolutional Dictionary Learning through Tensor Factorization. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-123253238

Nimmagadda, Tejaswi and Anandkumar, Anima (2015) Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-162932108

Janzamin, Majid and Sedghi, Hanie and Anandkumar, Anima (2015) Score Function Features for Discriminative Learning. In: 3rd International Conference on Learning Representations (ICLR 2015), 7-9 May 2015, San Diego, CA. http://resolver.caltech.edu/CaltechAUTHORS:20190401-162925219

Anandkumar, Animashree and Foster, Dean P. and Hsu, Daniel et al. (2015) A Spectral Algorithm for Latent Dirichlet Allocation. Algorithmica, 72 (1). pp. 193-214. ISSN 0178-4617. http://resolver.caltech.edu/CaltechAUTHORS:20170920-142816744

Anandkumar, Anima and Sedghi, Hanie (2015) Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-162928669

2014

Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity. In: Deep Learning and Representation Learning Workshop: NIPS 2014, 12 December 2014, Montreal, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190402-163306528

Sedghi, Hanie and Janzamin, Majid and Anandkumar, Anima (2014) Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. Proceedings of Machine Learning Research, 51 . pp. 1223-1231. ISSN 1938-7228. http://resolver.caltech.edu/CaltechAUTHORS:20190401-162921773

Janzamin, Majid and Sedghi, Hanie and Anandkumar, Anima (2014) Score Function Features for Discriminative Learning: Matrix and Tensor Framework. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190401-162918161

Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity. In: 3rd International Conference on Learning Representations (ICLR 2015), 7-9 May 2015, San Diego, CA. http://resolver.caltech.edu/CaltechAUTHORS:20190401-162914714

Anandkumar, Animashree and Ge, Rong and Hsu, Daniel et al. (2014) Tensor Decompositions for Learning Latent Variable Models. Journal of Machine Learning Research, 15 . pp. 2773-2832. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-134735763

Anandkumar, Animashree and Ge, Rong and Hsu, Daniel et al. (2014) A Tensor Approach to Learning Mixed Membership Community Models. Journal of Machine Learning Research, 15 . pp. 2239-2312. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-093022023

Sattari, Pegah and Kurant, Maciej and Anandkumar, Animashree et al. (2014) Active Learning of Multiple Source Multiple Destination Topologies. IEEE Transactions on Signal Processing, 62 (8). pp. 1926-1937. ISSN 1053-587X. http://resolver.caltech.edu/CaltechAUTHORS:20170925-101553300

Janzamin, Majid and Anandkumar, Animashree (2014) High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models. Journal of Machine Learning Research, 15 . pp. 1549-1591. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-142820777

2013

Anandkumar, Animashree and He, Ting and Bisdikian, Chatschik et al. (2013) Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints. Performance Evaluation , 70 (12). pp. 1090-1110. ISSN 0166-5316. http://resolver.caltech.edu/CaltechAUTHORS:20170920-142253537

Anandkumar, Animashree and Hassidim, Avinatan and Kelner, Jonathan (2013) Topology discovery of sparse random graphs with few participants. Random Structures & Algorithms, 43 (1). pp. 16-48. ISSN 1042-9832. http://resolver.caltech.edu/CaltechAUTHORS:20170920-132342501

Anandkumar, Amod J. G. and Anandkumar, Animashree and Lambotharan, Sangarapillai et al. (2013) Robust noncooperative rate-maximization game for MIMO Gaussian interference channels under bounded channel uncertainty. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE , Piscataway, NJ, pp. 4819-4823. ISBN 978-1-4799-0356-6. http://resolver.caltech.edu/CaltechAUTHORS:20170925-100945197

Huang, Furong and Anandkumar, Animashree (2013) FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations. In: 2013 Proceedings IEEE INFOCOM. IEEE , Piscataway, NJ, pp. 1896-1904. ISBN 978-1-4673-5944-3. http://resolver.caltech.edu/CaltechAUTHORS:20170925-100358224

Sattari, Pegah and Kurant, Maciej and Anandkumar, Animashree et al. (2013) Active learning of multiple source multiple destination topologies. In: 47th Annual Conference on Information Sciences and Systems. IEEE , Piscataway, NJ, pp. 1-6. ISBN 978-1-4673-5237-6. http://resolver.caltech.edu/CaltechAUTHORS:20170925-095252031

Anandkumar, Animashree and Valluvan, Ragupathyraj (2013) Learning loopy graphical models with latent variables: Efficient methods and guarantees. Annals of Statistics, 41 (2). pp. 401-435. ISSN 0090-5364. http://resolver.caltech.edu/CaltechAUTHORS:20170927-104250746

2012

Liu, Ying and Chandrasekaran, Venkat and Anandkumar, Animashree et al. (2012) Feedback Message Passing for Inference in Gaussian Graphical Models. IEEE Transactions on Signal Processing, 60 (8). pp. 4135-4150. ISSN 1053-587X. http://resolver.caltech.edu/CaltechAUTHORS:20120820-094221711

Anandkumar, Animashree and Tan, Vincent Y. F. and Huang, Furong et al. (2012) High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion. Journal of Machine Learning Research, 13 . pp. 2293-2337. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-091743601

Anandkumar, Animashree and Tan, Vincent Y. F. and Huang, Furong et al. (2012) High-dimensional structure estimation in Ising models: Local separation criterion. Annals of Statistics, 40 (3). pp. 1346-1375. ISSN 0090-5364. http://resolver.caltech.edu/CaltechAUTHORS:20170927-101515951

2011

Anandkumar, Amod J. G. and Anandkumar, Animashree and Lambotharan, Sangarapillai et al. (2011) Robust Rate Maximization Game Under Bounded Channel Uncertainty. IEEE Transactions on Vehicular Technology, 60 (9). pp. 4471-4486. ISSN 0018-9545. http://resolver.caltech.edu/CaltechAUTHORS:20170925-094601829

Khajehnejad, M. Amin and Yoo, Juhwan and Anandkumar, Animashree et al. (2011) Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing. In: 2011 IEEE International Symposium on Information Theory Proceedings. IEEE , Piscataway, NJ, pp. 1427-1431. ISBN 978-1-4577-0596-0. http://resolver.caltech.edu/CaltechAUTHORS:20120406-072754339

Tan, Vincent Y. F. and Anandkumar, Animashree and Willsky, Alan S. (2011) Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates. Journal of Machine Learning Research, 12 . pp. 1617-1653. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-144736867

Choi, Myung Jin and Tan, Vincent Y. F. and Anandkumar, Animashree et al. (2011) Learning Latent Tree Graphical Models. Journal of Machine Learning Research, 12 . pp. 1771-1812. ISSN 1533-7928. http://resolver.caltech.edu/CaltechAUTHORS:20170927-100701408

Anandkumar, Animashree and Michael, Nithin and Tang, Ao Kevin et al. (2011) Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret. IEEE Journal on Selected Areas in Communications, 29 (4). pp. 731-745. ISSN 0733-8716. http://resolver.caltech.edu/CaltechAUTHORS:20170922-133040888

Balister, Paul and Bollobás, Béla and Anandkumar, Animashree et al. (2011) Energy-latency tradeoff for in-network function computation in random networks. In: 2011 Proceedings IEEE INFOCOM. IEEE , Piscataway, NJ, pp. 1575-1583. ISBN 978-1-4244-9919-9. http://resolver.caltech.edu/CaltechAUTHORS:20170925-092119382

He, Ting and Anandkumar, Animashree and Agrawal, Dakshi (2011) Index-based sampling policies for tracking dynamic networks under sampling constraints. In: 2011 Proceedings IEEE INFOCOM. IEEE , Piscataway, NJ, pp. 1233-1241. ISBN 978-1-4244-9919-9. http://resolver.caltech.edu/CaltechAUTHORS:20170925-091212425

Tan, Vincent Y. F. and Anandkumar, Animashree and Tong, Lang et al. (2011) A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures. IEEE Transactions on Information Theory, 57 (3). pp. 1714-1735. ISSN 0018-9448. http://resolver.caltech.edu/CaltechAUTHORS:20170922-092634649

2010

Anandkumar, Amod J. G. and Anandkumar, Animashree and Lambotharan, Sangarapillai et al. (2010) Efficiency of rate-maximization game under bounded channel uncertainty. In: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers. IEEE , Piscataway, NJ, pp. 482-486. ISBN 978-1-4244-9722-5. http://resolver.caltech.edu/CaltechAUTHORS:20170922-134024046

Tan, Vincent Y. F. and Anandkumar, Animashree and Willsky, Alan S. (2010) Error exponents for composite hypothesis testing of Markov forest distributions. In: 2010 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, pp. 1613-1617. ISBN 978-1-4244-7890-3. http://resolver.caltech.edu/CaltechAUTHORS:20170922-085233225

Liu, Ying and Chandrasekaran, Venkat and Anandkumar, Animashree et al. (2010) Feedback Message Passing for Inference in Gaussian Graphical Models. In: 2010 IEEE International Symposium on Information Theory Proceedings (ISIT). IEEE , Piscataway, NJ, pp. 1683-1687. ISBN 978-1-4244-6960-4 . http://resolver.caltech.edu/CaltechAUTHORS:20121004-153356182

Anandkumar, Animashree and Yukich, Joseph and Willsky, Alan (2010) Limit laws for random spatial graphical models. In: 2010 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, pp. 1728-1732. ISBN 978-1-4244-7890-3. http://resolver.caltech.edu/CaltechAUTHORS:20170922-084526901

Tan, Vincent Y. F. and Anandkumar, Animashree and Willsky, Alan S. (2010) Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures. IEEE Transactions on Signal Processing, 58 (5). pp. 2701-2714. ISSN 1053-587X. http://resolver.caltech.edu/CaltechAUTHORS:20170922-082655078

Anandkumar, Animashree and Michael, Nithin and Tang, Ao (2010) Opportunistic Spectrum Access with Multiple Users: Learning under Competition. In: 2010 Proceedings IEEE INFOCOM. IEEE , Piscataway, NJ, pp. 1-9. ISBN 978-1-4244-5836-3. http://resolver.caltech.edu/CaltechAUTHORS:20170922-083321456

Anandkumar, Amod J. G. and Anandkumar, Animashree and Lambotharan, Sangarapillai et al. (2010) Robust rate-maximization game under bounded channel uncertainty. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE , Piscataway, NJ, pp. 3158-3161. ISBN 978-1-4244-4295-9. http://resolver.caltech.edu/CaltechAUTHORS:20170922-084018628

2009

Tan, Vincent Y. F. and Anandkumar, Animashree and Willsky, Alan S. (2009) How do the structure and the parameters of Gaussian tree models affect structure learning? In: 47th Annual Allerton Conference on Communication, Control, and Computing. IEEE , Piscataway, NJ, pp. 684-691. ISBN 978-1-4244-5870-7. http://resolver.caltech.edu/CaltechAUTHORS:20170921-160724769

Anandkumar, Animashree and Yukich, Joseph E. and Tong, Lang et al. (2009) Energy scaling laws for distributed inference in random fusion networks. IEEE Journal on Selected Areas in Communications, 27 (7). pp. 1203-1217. ISSN 0733-8716. http://resolver.caltech.edu/CaltechAUTHORS:20170921-155701400

Anandkumar, Animashree and Tong, Lang and Willsky, Alan (2009) Detection error exponent for spatially dependent samples in random networks. In: 2009 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, pp. 2882-2886. ISBN 978-1-4244-4312-3. http://resolver.caltech.edu/CaltechAUTHORS:20170921-154140237

Tan, Vincent Y. F. and Anandkumar, Animashree and Tong, Lang et al. (2009) A large-deviation analysis for the maximum likelihood learning of tree structures. In: 2009 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, pp. 1140-1144. ISBN 978-1-4244-4312-3. http://resolver.caltech.edu/CaltechAUTHORS:20170921-154915776

Anandkumar, Animashree and Wang, Meng and Tong, Lang et al. (2009) Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference. In: 28th IEEE Conference on Computer Communications. IEEE , Piscataway, NJ, pp. 2150-2158. ISBN 978-1-4244-3512-8. http://resolver.caltech.edu/CaltechAUTHORS:20170921-153522501

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2009) Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency. IEEE Transactions on Information Theory, 55 (2). pp. 816-827. ISSN 0018-9448. http://resolver.caltech.edu/CaltechAUTHORS:20170920-162015185

2008

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2008) Optimal Node Density for Detection in Energy-Constrained Random Networks. IEEE Transactions on Signal Processing, 56 (10). pp. 5232-5245. ISSN 1053-587X. http://resolver.caltech.edu/CaltechAUTHORS:20170920-160524319

Ezovski, G. Matthew and Anandkumar, Animashree and Tang, A. Kevin et al. (2008) Min-min times in peer-to-peer file sharing networks. In: 46th Annual Allerton Conference on Communication, Control, and Computing. IEEE , Piscataway, NJ, pp. 1487-1494. ISBN 978-1-4244-2925-7. http://resolver.caltech.edu/CaltechAUTHORS:20170810-112107197

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2008) Distributed Estimation Via Random Access. IEEE Transactions on Information Theory, 54 (7). pp. 3175-3181. ISSN 0018-9448. http://resolver.caltech.edu/CaltechAUTHORS:20170920-155855969

Anandkumar, Animashree and Bisdikian, Chatschik and Agrawal, Dakshi (2008) Tracking in a spaghetti bowl: monitoring transactions using footprints. In: Proceedings of the 2008 ACM SIGMETRICS international conference on measurement and modeling of computer systems. Association for Computing Machinery , New York, NY, pp. 133-144. ISBN 978-1-60558-005-0. http://resolver.caltech.edu/CaltechAUTHORS:20170927-141103230

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram et al. (2008) Minimum Cost Data Aggregation with Localized Processing for Statistical Inference. In: 27th IEEE Conference on Computer Communications. IEEE , Piscataway, NJ, pp. 1454-1462. ISBN 978-1-4244-2025-4. http://resolver.caltech.edu/CaltechAUTHORS:20170920-154142664

Sengupta, Bikram and Banerjee, Nilanjan and Anandkumar, Animashree et al. (2008) Non-intrusive transaction monitoring using system logs. In: 2008 IEEE Network Operations and Management Symposium. IEEE , Piscataway, NJ, pp. 879-882. ISBN 978-1-4244-2065-0. http://resolver.caltech.edu/CaltechAUTHORS:20170920-161153053

2007

Anandkumar, Animashree and Tong, Lang (2007) Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels. IEEE Transactions on Signal Processing, 55 (10). pp. 5032-5043. ISSN 1053-587X. http://resolver.caltech.edu/CaltechAUTHORS:20170920-153723886

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2007) Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE , Piscataway, NJ, pp. 829-832. ISBN 1-4244-0727-3. http://resolver.caltech.edu/CaltechAUTHORS:20170920-152542556

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2007) Energy Efficient Routing for Statistical Inference of Markov Random Fields. In: 41st Annual Conference on Information Sciences and Systems. IEEE , Piscataway, NJ, pp. 643-648. ISBN 1-4244-1063-3. http://resolver.caltech.edu/CaltechAUTHORS:20170920-153229570

2006

Anandkumar, Animashree and Tong, Lang (2006) A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors. In: 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings. Vol.4. IEEE , Piscataway, NJ, pp. 1097-1100. ISBN 1-4244-0469-X. http://resolver.caltech.edu/CaltechAUTHORS:20170920-144901114

Anandkumar, Animashree and Tong, Lang (2006) Distributed Statistical Inference using Type Based Random Access over Multi-access Fading Channels. In: 2006 40th Annual Conference on Information Sciences and Systems. IEEE , Piscataway, NJ, pp. 38-43. ISBN 1-4244-0349-9. http://resolver.caltech.edu/CaltechAUTHORS:20170920-145744615

This list was generated on Thu Sep 19 21:33:20 2019 PDT.