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Copyright notice
Preprints/Submissions
“A Tensor Spectral Approach to Learning Mixed Membership Community Models” by A. Anandkumar, R. Ge, D. Hsu, and S.M. Kakade. Conference on Learning Theory, June 2013.
Download: PDF. Slides.
“Tensor Decompositions for Learning Latent Variable Models” by A. Anandkumar, R. Ge, D. Hsu, S.M. Kakade and M. Telgarsky. Preprint, October 2012.
Download: PDF.
“Learning Linear Bayesian Networks with Latent Variables” by A. Anandkumar, D. Hsu, A. Javanmard and S.M. Kakade. Preprint, September 2012.
Download: PDF. ICML-version
“Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation” by A. Anandkumar, D. P. Foster, D. Hsu, S.M. Kakade and Y.K. Liu. Preprint, April 2012. An abridged version appears in NIPS 2012.
Download: PDF. NIPS-version
“Learning High-Dimensional Mixtures of Graphical Models ”
by A. Anandkumar, D. Hsu, and S.M. Kakade.
Accepted to Annals of Statistics. April 2013. An abridged version appears in NIPS 2012.
Download: PDF. NIPS-version
“High-Dimensional Covariance Decomposition into Sparse Markov
and Independence Domains.”
by M. Janzamin and A. Anandkumar.
Preprint, Feb. 2012. An abridged version appears in the Proc. of ICML, June 2012.
Download: PDF. ICML-version. Slides.
“Spectral Methods for Learning Multivariate Latent Tree Structure”
by A. Anandkumar, K. Chaudhuri, D. Hsu, S.M. Kakade, L. Song and T. Zhang
Preprint, July 2011. An abridged version appears in the Proc. of NIPS, Dec. 2011.
Details. Download: PDF.
Journal Publications
“Learning Loopy Graphical Models with Latent Variables: Efficient
Methods and Guarantees ”
by A. Anandkumar and R. Valluvan.
Ann. Statist. Volume 41, Number 2 (2013), 401-435. An abridged version appears in NIPS 2012.
Download:PDF. Supplement. Slides NIPS-version. Software code and datasets. Videolecture.
“High-Dimensional Structure Learning of Ising Models:
Local Separation Criterion”
by A. Anandkumar, V.Y.F Tan, F. Huang, and A.S. Willsky.
Annals of Statistics, Volume 40, Number 3 (2012), 1346-1375. An abridged version appears in the Proc. of NIPS, Dec. 2011.
Details. Download: PDF. Supplementary file. Software code and datasets. Short (NIPS) Version Videolecture NIPS-Slides
“High-Dimensional Gaussian Graphical Model Selection:
Walk-Summability and Local Separation Criterion”
by A. Anandkumar, V.Y.F Tan, F. Huang, and A.S. Willsky.
J. Machine Learning Research,
13:2293–2337, Aug. 2012.. An abridged version appears in the Proc. of NIPS, Dec. 2011.
Details. Download: PDF. Software code and datasets. Short (NIPS) Version Videolecture NIPS-Slides
“Feedback Message Passing for Inference in
Gaussian Graphical Models”
by Y. Liu, V. Chandrasekaran, A. Anandkumar, and A. Willsky.
IEEE Tran. on Signal Processing, 60(8):4135–4150, Aug. 2012.
Details. Download: PDF.
“Robust Rate Maximization Game Under Bounded Channel Uncertainty”
by AJG. Anandkumar, A. Anandkumar, S. Lambotharan, J.A. Chambers,
Vehicular Technology, IEEE Transactions on, volume 60, issue 9, pp. 4471 - 4486, Nov. 2011.
Details. Download: PDF.
“Learning Latent Tree Graphical Models”
by M.J. Choi, V. Tan, A. Anandkumar, and A. Willsky.
Journal of Machine Learning Research, volume 12, pp. 1771−1812, May 2011.
Details. Download: PDF. Project Webpage. Code. Allerton Version. Slides. Seminar Slides.
“Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates”
by V. Tan, A. Anandkumar, and A. Willsky.
Journal of Machine Learning Research, volume 12, pp. 1617-1653, May 2011.
Details. Download: PDF. Allerton Version. Slides.
“Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret”
by A. Anandkumar, N. Michael, A.K. Tang, and A. Swami.
IEEE JSAC on Advances in Cognitive Radio Networking and Communications, Apr. 2011, volume 29, no. 4, pp. 781-745. Featured in the Best Readings on Cognitive Radio by IEEE Comsoc society. link.
Details. Download: PDF.
“Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures”
by V. Tan, A. Anandkumar, and A. Willsky.
IEEE Tran. on Signal Processing, vol. 58, no. 5, May 2010, pp. 2701-2714.
Details.
Download: PDF. slides (PDF). Code. MIT News. ACM Technews. KDnuggets.
“Seeing Through Black Boxes : Tracking Transactions through Queues
under Monitoring Resource Constraints”
by A. Anandkumar, T. He, C. Bisdikian, and D. Agrawal.
Accepted to Elsevier Performance Evaluation, Feb. 2010.
Details.
Download: Paper
“A Large-Deviation Analysis of the
Maximum-Likelihood Learning of Markov Tree Structures”
by V.Y.F. Tan, A. Anandkumar, L. Tong, and A.S. Willsky.
IEEE Tran. on Information Theory, Vol. 57, No. 3, March. 2011, pp. 1714-1735.
Details.
Download: Paper. Project Highlights
“Energy Scaling Laws for Distributed Inference in Random Fusion Networks”
by A. Anandkumar, J.E.Yukich, L. Tong, and A. Swami.
IEEE J. Selec. Area Comm., vol. 27, no. 7, pp.1203-1217, Sept. 2009.
Details.
Download: Paper.
“Detection of Gauss-Markov random fields with nearest-neighbor dependency”
by A. Anandkumar, L. Tong, and A. Swami.
IEEE Trans. Information Theory, vol. 55, no. 2, Feb. 2009.
Details.
Download: Paper
“Optimal Node Density for Detection in Energy Constrained Random Networks”
by A. Anandkumar, L. Tong, and A. Swami.
IEEE Trans. Signal Proc., vol. 56, no. 10, Oct. 2008.
Details.
Download: Paper
“Distributed Estimation Via Random Access”
by A. Anandkumar, L. Tong, and A. Swami.
Information Theory, IEEE Transactions on, vol. 54, no. 7, July 2008, pp. 3175-3181.
Details.
Download: Paper.
“Type-Based Random Access for Distributed Detection over Multiaccess Fading Channels”
by A. Anandkumar and L. Tong.
IEEE Trans. Signal Proc., vol. 55, no. 10, Oct. 2007, pp. 5032-5043. (2008 IEEE Signal Proc. Society Young Author Best Paper Award).
Details.
Download: Paper.
Conferences: Long Papers
“A Method of Moments for Mixture Models and Hidden
Markov Models. ”
by A. Anandkumar, D. Hsu, and S.M. Kakade.
Proc. of COLT, June 2012.
Download: PDF. COLT-version
“Topology Discovery of Sparse Random Graphs With Few Participants ”
by A. Anandkumar, A. Hassidim, and J. Kelner.
Proc. of ACM SIGMETRICS, (San Jose, USA), June 2011. Best Paper Award
Extended version accepted to J. on Random Structures and Algorithms, Dec. 2011.
Details. Download: PDF. Full Version. Talk
“Fast, Concurrent and Distributed Load Balancing
under Switching Costs and Imperfect Observations”
by F. Huang and A. Anandkumar.
Proc. of IEEE INFOCOM, Apr. 2013.
Download: PDF
“Energy-Latency Tradeoff for In-Network Function Computation in Random Networks”
by P. Balister, B. Bollobas, A. Anandkumar, and A.S. Willsky.
Proc. of IEEE INFOCOM, (Shanghai, China), Apr. 2011.
Details. Download: PDF, Long Version Slides
“Index-Based Sampling Policies for Tracking Dynamic Networks under Sampling Constraints” by T. He, A. Anandkumar, and D. Agrawal.
Proc. of IEEE INFOCOM, (Shanghai, China), Apr. 2011.
Details. Download: PDF. Supplementary material
“Opportunistic Spectrum Access with Multiple Users: Learning under Competition”
by A. Anandkumar, N. Michael, and A.K. Tang.
Proc. of IEEE INFOCOM, (San Deigo, USA), Mar. 2010. (AR 17%)
Details. Download: Paper, Slides.
“Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference”
by A. Anandkumar, M. Wang, L. Tong, and A. Swami.
In Proc. of IEEE INFOCOM, (Rio De Janeiro, Brazil), Apr. 2009. (AR 20%)
Details. Download: PDF, Tech Report.
“Tracking in a Spaghetti Bowl: Monitoring Transactions Using Footprints”
by A. Anandkumar, C. Bisdikian, and D. Agrawal.
In Proc. ACM Intl. Conf. on Measurement & Modeling of Computer Systems (Sigmetrics), (Annapolis, Maryland, USA ), June 2008. (AR 18%)
Details.
Download: Paper
“Minimum Cost Data Aggregation with Localized Processing for Statistical Inference”
by A. Anandkumar, L. Tong, A. Swami, and A. Ephremides.
In Proc. of IEEE INFOCOM, (Phoenix, USA), Apr. 2008, pp. 780-788. (AR 20%)
Details.
Download: Paper
Conferences: Short Papers (Limited List)
“High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions”
by A. Anandkumar, V. Y. F. Tan and A. S. Willsky
In Proc. of NIPS, (Granada, Spain), Dec. 2011.
Selected for full oral presentation (20 papers out of 1400 submissions).
Download: PDF.
“Spectral Methods for Learning Multivariate Latent Tree Structure”
by A. Anandkumar, K. Chaudhuri, D. Hsu, S.M. Kakade, L. Song and T. Zhang
In Proc. of NIPS, (Granada, Spain), Dec. 2011.
Selected for poster presentation (305 papers out of 1400 submissions).
Download: PDF.
“Summary Based Structures with Improved
Sublinear Recovery for Compressed Sensing”
by M. A. Khajehnejad, J. Yoo, A. Anandkumar and B. Hassibi
In Proc. of IEEE ISIT, (St. Petersburg, Russia), July 2011.
Download: PDF.
“Scaling Laws for Random Spatial Graphical Models”
by A. Anandkumar, J.E. Yukich, and A. Willsky.
In Proc. of IEEE ISIT, (Austin, USA), June 2010.
Details.
Download: PDF. Slides
“Feedback Message Passing for Inference in Gaussian Graphical Models”
by Y. Liu, V. Chandrasekaran, A. Anandkumar, and A. Willsky.
In Proc. of IEEE ISIT, (Austin, USA), June 2010.
Details.
Download: PDF. Slides
“Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions”
by V. Tan, A. Anandkumar, and A. Willsky.
In Proc. of IEEE ISIT, (Austin, USA), June 2010.
Details.
Download: PDF. Slides Proofs
“Robust Rate Maximization Game Under Bounded Channel Uncertainty”
by A.JG. Anandkumar, A. Anandkumar, S. Lambotharan, and J. Chambers.
In Proc. of IEEE ICASSP, (Dallas, USA), Mar. 2010.
Details.
Download: PDF.
“Selectively Retrofitting Monitoring in Distributed Systems”
by A. Anandkumar, C. Bisdikian, T. He, and D. Agrawal,
In Proc. of the Eleventh Workshop on Mathematical Performance Modeling and Analysis (MAMA). (Seattle, USA).
June 2009.
Details.
Download: PDF.
“Detection Error Exponent for Spatially Dependent Samples in Random Networks”
by A. Anandkumar, J.E. Yukich, L. Tong, and A. Willsky.
In Proc. of IEEE ISIT, (Seoul, S. Korea), July 2009.
Details.
Download: PDF.
“A Large Deviation Analysis of Detection over Multi-Access Channels with Random Number of Sensors”
by A. Anandkumar and L. Tong.
In Proc. of ICASSP'06, (Toulouse, France), May 2006, pp. 1097-1101. (Best Paper Award).
Details.
Download: Paper.
Thesis
Scalable Algorithms for Distributed Statistical Inference
by A. Anandkumar.
July, 2009.
Details.
Download: Thesis. Slides
Book Chapters
Routing for Statistical Inference in Sensor Networks
by A. Anandkumar, A. Ephremides, A. Swami, and L. Tong.
In Handbook on Array Processing and Sensor Networks, (S. Haykin and R. Liu, eds.), 2009.
Details.
Download: Bookchapter.
Invention Disclosures
"Selective instrumentation for distributed applications for transaction monitoring" by A. Anandkumar, D. Agrawal, C. Bisdikian, T. He, and S. Perelman, Filed, Aug. 2009. Link |