generated by bibbase.org
  2020 (11)
Graph-based regularization for regression problems with alignment and highly-correlated designs. Li, Y.; Mark, B.; Raskutti, G.; Willett, R.; Song, H.; and Neiman, D. 2020. accepted to SIAM Journal on Mathematics of Data Science, \hrefhttps://arxiv.org/abs/1803.07658arXiv:1803.07658
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Leveraging spatial textures, through machine learning, to identify aerosol and distinct cloud types from multispectral observations. Marais, W. J.; Holz, R. E.; Reid, J. S.; and Willett, R. M. 2020. Submitted
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Deep Learning Techniquesfor Inverse Problems in Imaging. Ongie, G.; Metzler, C.; Jalal, A.; Dimakis, A.; Baraniuk, R.; and Willett, R. 2020. Submitted
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Tensor Methods for Nonlinear Matrix Completion. Ongie, G.; Pimentel-Alarcon, D.; Balzano, L.; Nowak, R.; and Willett, R. 2020. Submitted
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Context-dependent self-exciting point processes:models, methods, and risk bounds in high dimensions. Zheng, L.; Willett, R.; and Raskutti, G. 2020. Submitted; \hrefhttps://arxiv.org/abs/2003.07429arXiv:2003.07429
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Graph-guided regularized regression of Pacific Ocean climate variables to increase predictive skill of southwestern US winter precipitation. Stevens, A.; Willett, R.; Mamalakis, A.; Foufoula-Georgiou, E.; Tejedor, A.; Randerson, J.; Smyth, P.; and Wright, S. J. 2020. Submitted
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Localizing Changes in High-Dimensional Vector Autoregressive Processes. Wang, D.; Yu, Y.; Rinaldo, A.; and Willett, R. 2020. Submitted
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An Optimal Statistical and Computational Framework for Generalized Tensor Estimation. Han, R.; Willett, R.; and Zhang, A. arXiv preprint arXiv:2002.11255. 2020.
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Predicting in situ dry matter degradability of chopped and processed corn kernels using image analysis techniques. Luck, B. D.; Drewry, J. L.; Shaver, R. D.; Willett, R. M.; and Ferraretto, L. F. 2020. Submitted.
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Detection and Description of Change in Visual Streams. Gilton, D.; Luo, R.; Willett, R.; and Shakhnarovich, G. 2020. Submitted
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A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. Ongie, G.; Willett, R.; Soudry, D.; and Srebro, N. In ICLR, 2020. arXiv preprint \hrefhttps://arxiv.org/abs/1910.01635arXiv:1910.01635
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  2019 (9)
Statistically and Computationally Efficient Change Point Localization in Regression Settings. Wang, D.; Lin, K.; and Willett, R. arXiv preprint arXiv:1906.11364. 2019.
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Neumann Networks for Inverse Problems in Imaging. Gilton, D.; Ongie, G.; and Willett, R. IEEE Transactions on Computational Imaging, 6(1): 328-343. 2019. arXiv preprint \hrefhttps://arxiv.org/abs/1901.03707arXiv:1901.03707
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Predicting kernel processing score of harvested and processed corn silage via image processing techniques. Drewry, J. L.; Luck, B. D.; Willett, R. M.; Rocha, E. M. C.; and Harmon, J. D. Computers and Electronics in Agriculture, 160: 144-152. 2019.
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A data-dependent weighted LASSO under Poisson noise. Jiang, X.; Reynaud-Bouret, P.; Rivoirard, V.; Sansonnet, L.; and Willett, R. IEEE Tranactions on Information Theory, 65(3): 1589-1613. 2019. Preprint at \hrefhttp://arxiv.org/abs/1509.08892arXiv:1509.08892
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Network Estimation from Point Process Data . Mark, B.; Raskutti, G.; and Willett, R. IEEE Transactions on Information Theory, 65. 2019. arXiv preprint \hrefarXiv:1802.04838https://arxiv.org/abs/1802.04838
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Learned Patch-Based regularization for inverse problems in imaging. Gilton, D.; Ongie, G.; and Willett, R. In IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019. r̆lhttps://cmsworkshops.com/CAMSAP2019/Papers/ViewPaper.asp?PaperNum=1171
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Bilinear Bandits with Low-Rank Structure. Jun, K.; Willett, R.; Wright, S.; and Nowak, R. In Proc. ICML, 2019. arXiv preprint \hrefhttps://arxiv.org/abs/1901.02470arXiv:1901.02470
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Estimating Network Structure from Incomplete Event Data. Mark, B.; Raskutti, G.; and Willett, R. In Proc. AISTATS, 2019. arXiv preprint \hrefhttps://arxiv.org/abs/1811.02979arXiv:1811.02979
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Response to ``Artificial Intelligence|The Revolution Hasn't Happened Yet''. Willett, R. M. Harvard Data Science Review. 2019.
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  2018 (2)
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes. Jalali, A.; and Willett, R. In Proc. American Control Conference, 2018.
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A Machine-Learning Based Drug Repurposing Approach Using Baseline Regularization. Kuang, Z.; Bao, Y.; Thomson, J.; Caldwell, M.; Peissig, P.; Stewart, R.; Willett, R.; and Page, D. In In Silico Repurposing. Methods in Molecular Biology Series. Springer, 2018.
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  2017 (17)
Engineering Human CNS Morphogenesis: Controlled Induction of Singular Neural Rosette Emergence. Knight, G. T.; Lundin, B. F.; Iyer, N.; Ashton, L. M. T.; Sethares, W. A.; Willett, R. M.; and Ashton, R. 2017. sumbitted. bioRxiv 229328; doi: r̆lhttps://doi.org/10.1101/229328
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Online learning for changing environments using coin betting. Jun, K.; Orabona, F.; Wright, S.; and Willett, R. Electronic Journal of Statistics, 11(2): 5282-5310. 2017.
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Asymmetric Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3. Borkowski, K. J.; Reynolds, S. P.; Green, D. A.; Hwang, U.; Petre, R.; Krishnamurthy, K.; and Willett, R. The Astrophysical Journal Letters, 837(1): L7. 2017.
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Scalable Generalized Linear Bandits: Online Computation and Hashing. Jun, K.; Bhargava, A.; Nowak, R.; and Willett, R. In NIPS, 2017.
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Subspace Clustering via Tangent Cones. Jalali, A.; and Willett, R. In NIPS, 2017.
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Online Thinning for High Volume Streaming Data. Hunt, X. J.; and Willett, R. In 23nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, \hrefhttp://www.kdd.org/kdd2017/workshopsWorkshop on Mining and Learning from Time Series, 2017.
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Point Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. Bao, Y.; Kwong, C.; Peissig, P.; Page, D.; and Willett, R. In Proc. \hrefhttp://mucmd.org/Machine Learning and Healthcare, 2017.
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Algebraic Variety Models for High-Rank Matrix Completion. Ongie, G.; Willett, R.; Nowak, R. D.; and Balzano, L. In Proc. ICML-2017, 2017. \hrefhttps://arxiv.org/abs/1703.09631arXiv:1703.09631
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Sparse Linear Contextual Bandits via Relevance Vector Machines. Gilton, D.; and Willett, R. In Proc. of \hrefhttp://sampta2017.ee/SampTA, 2017.
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Mixture Regression as Subspace Clustering. Pimentel-Alarcón, D.; Balzano, L.; Marcia, R.; Nowak, R.; and Willett, R. In Proc. of \hrefhttp://sampta2017.ee/SampTA, 2017.
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On learning high-dimensional structured single index models. Rao, N.; Ganti, R.; Balzano, L.; Willett, R.; and Nowak, R. In AAAI-17, 2017. \hrefhttps://arxiv.org/abs/1603.03980arXiv:1603.03980
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Low algebraic dimension matrix completion. Pimentel-Alarcón, D.; Ongie, G.; Balzano, L.; Willett, R.; and Nowak, R. In Allerton Conference on Communication, Control and Computing, 2017.
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Network Inference via Poisson ARMA Models. Mark, B.; Raskutti, G.; and Willett, R. In \hrefhttp://www.cs.huji.ac.il/conferences/CAMSAP17/IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017.
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Proximal-gradient Methods for Poisson Image Reconstruction with BM3D-based Regularization. W. Marais, R. W. In \hrefhttp://www.cs.huji.ac.il/conferences/CAMSAP17/IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017.
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Signal Representations in Modern Signal Processing. Willett, R. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017.
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Denoising Methods in Materials Science. Willett, R. In Simmons, J. P.; Graef, M. D.; Bouman, C. A.; and Drummy, L. F., editor(s), Statistical methods for materials science: Data Analytics in Microstructure Characterization. Taylor & Francis, 2017. to appear
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Subspace Clustering with Missing and Corrupted Data. Charles, Z.; Jalali, A.; and Willett, R. 2017. r̆lhttps://arxiv.org/abs/1707.02461
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  2016 (13)
Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations. Marais, W. J.; Holz, R. E.; Hu, Y. H.; Kuehn, R. E.; Eloranta, E. E.; and Willett, R. M. Applied Optics. 2016. r̆lhttps://doi-org.ezproxy.library.wisc.edu/10.1364/AO.55.008316
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Online Data Thinning via Multi-Subspace Tracking. Jiang, X.; and Willett, R. arXiv preprint arXiv:1609.03544. 2016.
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Inference of High-dimensional Autoregressive Generalized Linear Models. Hall, E. C.; Raskutti, G.; and Willett, R. to appear in \em IEEE Transactions on Information Theory, arXiv preprint arXiv:1605.02693. 2016.
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Tracking dynamic point processes on networks. Hall, E. C.; and Willett, R. M. IEEE Transactions on Information Theory, 62(7): 4327–4346. 2016.
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Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma. Fu, H. L.; Mueller, J. L.; Whitley, M. J.; Cardona, D. M.; Willett, R. M.; Kirsch, D. G.; Brown, J. Q.; and Ramanujam, N. PloS one, 11(1). 2016.
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Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting. Mueller, J. L.; Gallagher, J. E.; Chitalia, R.; Krieger, M.; Erkanli, A.; Willett, R. M.; Geradts, J.; and Ramanujam, N. Journal of cancer research and clinical oncology,1–12. 2016.
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Non-rigid registration and non-local principle component analysis to improve electron microscopy spectrum images. Yankovich, A. B.; Zhang, C.; Oh, A.; Slater, T. J. A.; Azough, F.; Freer, R.; Haigh, S. J.; Willett, R.; and Voyles, P. M. Nanotechnology, 27(36): 364001. 2016.
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Rapid staining and imaging of sub-nuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting. Mueller, J.; Gallagher, J.; Chitalia, R.; Krieger, M.; Erkanli, A.; Willett, R.; Geradts, J.; and Ramanujam, N. Journal of cancer research and clinical oncology, 142(7): 1475–1486. 2016.
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Improved Strongly Adaptive Online Learning using Coin Betting. Jun, K.; Orabona, F.; Willett, R.; and Wright, S. In Proc. AISTATS-2017, 2016. \hrefhttp://arxiv.org/abs/1610.04578arXiv:1610.04578
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Regret minimization algorithms for single-controller zero-sum stochastic games. Guan, P.; Raginsky, M.; Willett, R.; and Zois, D. In Decision and Control (CDC), 2016 IEEE 55th Conference on, pages 7075–7080, 2016. IEEE
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Inferring high-dimensional Poisson autoregressive models. Hall, E. C.; Raskutti, G.; and Willett, R. In Statistical Signal Processing Workshop (SSP), 2016 IEEE, pages 1–5, 2016. IEEE
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Group-sparse subspace clustering with missing data. Pimentel-Alarcón, D.; Balzano, L.; Marcia, R.; Nowak, R.; and Willett, R. In IEEE Statistical Signal Processing Workshop, 2016.
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Atmospheric lidar imaging and Poisson inverse problems. Marais, W.; Holz, R.; Hu, Y. H.; and Willett, R. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 983–987, 2016. IEEE
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  2015 (6)
A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins. Mueller, J.; Fu, H.; Mito, J.; Whitley, M.; Chitalia, R.; Erkanli, A.; Dodd, L.; Cardona, D.; Geradts, J.; Willett, R.; Kirsch, D.; and Ramanujam, N. International Journal of Cancer, 137(10): 2403-12. 2015.
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Online Convex Optimization in Dynamic Environments. Hall, E.; and Willett, R. IEEE Journal of Selected Topics in Signal Processing – Signal Processing for Big Data, 9(4). 2015. \hrefhttp://arxiv.org/abs/1307.5944arXiv:1307:5944, winner for 2018 IEEE Signal Processing Society Young Author Best Paper Award
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Minimax Optimal Rates for Poisson Inverse Problems with Physical Constraints. Jiang, X.; Raskutti, G.; and Willett, R. IEEE Transactions on Information Theory, 61(8). 2015. \hrefhttp://arxiv.org/abs/1403.6532arXiv:1403:6532
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Monotonic Matrix Completion. Ganti, R.; Balzano, L.; and Willett, R. In Proc. Neural Information Processing Systems, 2015.
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Online Learning of Neural Network Structure from Spike Trains. Hall, E.; and Willett, R. In Proceedings of the 7th International IEEE EMBS Neural Engineering Conference (NER'15), 2015.
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Regularized Non-Gaussian Image Denoising. Oh, A.; and Willett, R. 2015. \hrefhttp://arxiv.org/abs/1508.02971arXiv:1508.02971
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  2014 (9)
PMU based Detection of Imbalance in Three-Phase Power Systems. Routtenberg, T.; Xie, Y.; Willett, R.; and Tong, L. IEEE Transactions on Power Systems, 30(4). 2014.
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Nonuniform Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3. Borkowski, K. J.; Reynolds, S. P.; Green, D. A.; Hwang, U.; Petre, R.; Krishnamurthy, K.; and Willett, R. The Astrophysical Journal Letters, 790(2). 2014. \hrefhttp://arxiv.org/abs/1406.2287arXiv:1406.2287
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Reducing Basis Mismatch in Harmonic Signal Recovery via Alternating Convex Search. Nichols, J. M.; Oh, A. K.; and Willett, R. Signal Processing Letters, 21(8): 1007-1011. 2014. \hrefhttp://arxiv.org/abs/1406.5231arXiv:1406.5231
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Poisson noise reduction with non-local PCA. Salmon, J.; Harmany, Z.; Deledalle, C.; and Willett, R. Journal of Mathematical Imaging and Vision, 48(2): 279-294. 2014. \hrefhttp://arxiv.org/abs/1206.0338arXiv:1206:0338
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Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection. Willett, R. M.; Duarte, M. F.; Davenport, M. A.; and Baraniuk, R. G. IEEE Signal Processing Magazine, 31(1): 116-126. 2014. \hrefhttp://dx.doi.org/10.1109/MSP.2013.227950710.1109/MSP.2013.2279507
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Online Markov decision processes with Kullback-Leibler control cost. Guan, P.; Raginsky, M.; and Willett, R. IEEE Transactions on Automatic Control, 59(6): 1423-1438. 2014. \hrefhttp://arxiv.org/abs/1401.3198arXiv:1401:3198
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To $e$ or not to $e$ in Poisson image reconstruction. Oh, A. K.; Harmany, Z. T.; and Willett, R. M. In Proceedings of the IEEE International Conference on Image Processing (ICIP), 2014. ``Top 10% Paper''
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From minimax value to low-regret algorithms for online Markov decision processes. Guan, P.; Raginsky, M.; and Willett, R. In American Control Conference, 2014.
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The Dark Side of Image Reconstruction: Emerging Methods for Photon-limited Imaging. Willett, R. SIAM News. 2014. \hrefhttps://sinews.siam.org/DetailsPage/tabid/607/ArticleID/220/The-Dark-Side-of-Image-Reconstruction.aspxOnline version here
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  2013 (10)
Supernova Ejecta in the Youngest Galactic Supernova Remnant G1.9+0.3. Borkowski, K. J.; Reynolds, S. P.; Green, D. A.; Hwang, U.; Petre, R.; Krishnamurthy, K.; and Willett, R. The Astrophysical Journal Letters, 771(1). 2013. \hrefhttp://arxiv.org/abs/1305.7399arXiv:1305.7399
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Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Application to the detection of residual disease in tumor margins. Mueller, J. L.; Harmany, Z. T.; Mito, J. K.; Kennedy, S. A.; Kim, Y.; Dodd, L.; Geradts, J.; Kirsch, D. G.; Willett, R. M.; Brown, J. Q.; and Ramanujam, N. PLoS ONE. 2013. \hrefhttp://dx.plos.org/10.1371/journal.pone.0066198DOI:10.1371/journal.pone.0066198
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Level set estimation from projection measurements: Performance guarantees and fast computation. Krishnamurthy, K.; Bajwa, W. U.; and Willett, R. SIAM J. Imaging Sciences, 6(4): 2047-2074. 2013. \hrefhttp://arxiv.org/abs/1209.3990arxiv:1209.3990
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A recursive procedure for density estimation on the binary hypercube. Raginsky, M.; Silva, J.; Lazebnik, S.; and Willett, R. Electronic Journal of Statistics, 7: 820-858. 2013. \hrefhttp://arxiv.org/abs/1112:1450arXiv:1112.1450
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Logarithmic total variation regularization for cross-validation in photon-limited imaging. Oh, A.; Harmany, Z.; and Willett, R. In IEEE International Conference on Image Processing (ICIP), 2013.
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Online Optimization in Parametric Dynamic Environments. Hall, E.; and Willett, R. In Proc. Allerton Conference on Communication, Control and Computing, 2013.
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Foreground and background reconstruction in Poisson video. Hall, E.; and Willett, R. In IEEE International Conference on Image Processing (ICIP), 2013.
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Online Logistic Regression on Manifolds. Xie, Y.; and Willett, R. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.
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Dynamical Models and Tracking Regret in Online Convex Programming. Hall, E.; and Willett, R. In Proc. International Conference on Machine Learning (ICML), 2013. \hrefhttp://arxiv.org/abs/1301.1254arXiv.org:1301.1254
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Relax but stay in control: from value to algorithms for online Markov decision processes. Guan, P.; Raginsky, M.; and Willett, R. 2013. \hrefhttp://arxiv.org/abs/1310.7300arXiv:1310.7300
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  2012 (12)
Changepoint detection for high-dimensional time series with missing data. Xie, Y.; Huang, J.; and Willett, R. IEEE Journal of Selected Topics in Signal Processing, 7(1): 12-27. 2012. \hrefhttp://arxiv.org/abs/1208.5062arXiv:1208.5062
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Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods. Arias-Castro, E.; Salmon, J.; and Willett, R. SIAM Journal on Imaging Sciences, 5(3): 944-992. 2012. \hrefhttp://dx.doi.org/10.1137/110859403doi:10.1137/110859403
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Target Detection Performance Bounds in Compressive Imaging. Krishnamurthy, K.; Willett, R.; and Raginsky, M. EURASIP Journal on Advances in Signal Processing. 2012. \hrefhttp://asp.eurasipjournals.com/content/2012/1/205doi:10.1186/1687-6180-2012-205
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Sequential anomaly detection in the presence of noise and limited feedback. Raginsky, M.; Willett, R.; Horn, C.; Silva, J.; and Marcia, R. IEEE Transactions on Information Theory, 58(8): 5544-5562. 2012. \hrefhttp://dx.doi.org/10.1109/TIT.2012.2201375doi:10.1109/TIT.2012.2201375
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This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms � Theory and Practice. Harmany, Z.; Marcia, R.; and Willett, R. IEEE Transactions on Image Processing, 21(3). 2012. \hrefhttp://dx.doi.org/10.1109/TIP.2011.2168410doi:10.1109/TIP.2011.2168410
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Coded-aperture Raman imaging for standoff explosive detection. McCain, S. T.; Guenther, B. D.; Brady, D. J.; and Willett, K. K. R. In Proc. SPIE 8358, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIII, 2012.
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The Value of Multispectral Observations in Photon-Limited Quantitative Tissue Analysis. Harmany, Z. T.; Jiang, X.; and Willett, R. In Proc. Statistical Signal Processing Workshop, 2012.
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A Two-Stage Denoising Filter: The Preprocessed Yaroslavsky Filter. Salmon, J. A.; Willett, R.; and Arias-Castro, E. In Proc. Statistical Signal Processing Workshop, 2012. \hrefhttp://arxiv.org/abs/1208.6516arXiv:1208:6516
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Multiscale Online Tracking of Manifolds. Xin, Y.; Huang, J.; and Willett, R. In Proc. Statistical Signal Processing Workshop, 2012.
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Online Markov decision processes with Kullback-Leibler control cost. Guan, P.; Raginsky, M.; and Willett, R. In Proc. American Control Conference, 2012.
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Poisson Noise Reduction with Non-Local PCA. Salmon, J.; Deledalle, C.; Willett, R.; and Harmany, Z. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
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A two-stage denoising filter: the preprocessed Yaroslavsky filter. Salmon, J.; Arias-Castro, E.; and Willett, R. 2012. \hrefhttp://arxiv.org/abs/1208.6516arXiv:1208.6516
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  2011 (14)
Performance bounds for expander-based compressed sensing in Poisson noise. Raginsky, M.; Jafarpour, S.; Harmany, Z.; Marcia, R.; Willett, R.; and Calderbank, R. IEEE Transactions on Signal Processing, 59(9). 2011. \hrefhttp://arxiv.org/abs/1007.2377arXiv:1007.2377
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Integrated Sensing and Information Processing Theme-Based Redesign of the Undergraduate Electrical and Computer Engineering Curriculum at Duke University. Ybarra, G. A.; Collins, L. M.; Huettel, L. G.; Coonley, K. D.; Massoud, H. Z.; Board, J. A.; Cummer, S. A.; Choudhury, R. R.; Gustafson, M. R.; Jokerst, N. M.; Brooke, M. A.; Willett, R. M.; Kim, J.; and Absher, M. S. Advances in Engineering Education, 2(4). 2011.
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Fast level set estimation from projection measurements. Krishnamurthy, K.; Bajwa, W. U.; Willett, R.; and Calderbank, R. In Proc. Statistical Signal Processing Workshop, 2011.
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Decentralized online convex programming with local information. Raginsky, M.; Kiarashi, N.; and Willett, R. In Proc. American Control Conference, 2011.
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Errata: Sampling Trajectories for Sparse Image Recovery. Willett, R. 2011. Errata pertains to: ``Short and Smooth Sampling Trajectories for Compressed Sensing'' (\em ICASSP 2011), ``Smooth Sampling Trajectories for Sparse Recovery in MRI'' (\em ISBI 2011), and ``Sampling Trajectories for Sparse Image Recovery'' (\em SAMPTA 2011)
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Online anomaly detection with expert system feedback in social networks. Horn, C.; and Willett, R. In Proc. International Conference on Acoustics, Speech, and Signal Processing, 2011.
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Time-Evolving Modeling of Social Networks. Wang, E.; Silva, J.; Willett, R.; and Carin, L. In Proc. International Conference on Acoustics, Speech, and Signal Processing, 2011.
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Tissue Quantification in Photon-Limited Microendoscopy. Harmany, Z. T.; Mueller, J.; Brown, Q.; Ramanujam, N.; and Willett, R. In Proc. SPIE Optics and Photonics, 2011.
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Motion-adaptive compressive coded apertures. Harmany, Z. T.; Oh, A.; Marcia, R.; and Willett, R. In Proc. SPIE Optics and Photonics, 2011.
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Poisson Compressed Sensing. Willett, R.; and Raginsky, M. In Proc. of Defense Applications of Signal Processing, 2011.
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Compressed sensing for practical optical systems: a tutorial. Willett, R.; Marcia, R.; and Nichols, J. Optical Engineering, 50(7): 072601 1-13. 2011. ``There were actually ten Optical Engineering papers from prior years that exhibited higher download rates than any 2016 paper, indicating that the journal continues to serve as an archival resource for the optical engineering community. One of these is a tutorial on compressed optical sensing,'' \hrefhttp://opticalengineering.spiedigitallibrary.org/article.aspx?articleid=2604685Annual highlights
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Compressive Optical Imaging: Architectures and Algorithms. Marcia, R.; Willett, R.; and Harmany, Z. T. In Cristobal, G.; Schelkens, P.; and Thienpont, H., editor(s), Optical and Digital Image Processing Fundamentals and Applications. Wiley, 2011.
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Spatio-temporal Compressed Sensing with Coded Apertures and Keyed Exposures. Harmany, Z.; Marcia, R.; and Willett, R. 2011. Based on 2008 ICASSP paper ``Compressive coded aperture superresolution image reconstruction.'' \hrefhttp://arxiv.org/abs/1111.7247arXiv:1111.7247
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  2010 (9)
Radioactive Scandium in the Youngest Galactic Supernova Remnant G1.9+0.3. Borkowski, K. J.; Reynolds, S. P.; Green, D. A.; Hwang, U.; Petre, R.; Krishnamurthy, K.; and Willett, R. The Astrophysical Journal Letters, 724(2). 2010. \hrefhttp://adsabs.harvard.edu/cgi-bin/nph-abs_connect?fforward=http://dx.doi.org/10.1088/2041-8205/724/2/L161doi:10.1088/2041-8205/724/2/L161
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Multiscale photon-limited spectral image reconstruction. Krishnamurthy, K.; Raginsky, M.; and Willett, R. SIAM Journal on Imaging Sciences, 3(3): 619 - 645. 2010. \hrefhttp://epubs.siam.org/doi/abs/10.1137/090756259doi:10.1137/090756259
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Compressed sensing performance bounds under Poisson noise. Raginsky, M.; Willett, R.; Harmany, Z.; and Marcia, R. IEEE Transactions on Signal Processing, 58(8): 3990-4002. 2010. \hrefhttp://arxiv.org/abs/0910.5146arXiv:0910.5146
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Poisson image reconstruction with total variation regularization. Willett, R.; Harmany, Z.; and Marcia, R. In Proc. IEEE International Conference on Image Processing, 2010.
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Hyperspectral target detection from incoherent projections: Nonequiprobable targets and inhomogeneous SNR. Krishnamurthy, K.; Raginsky, M.; and Willett, R. In Proc. IEEE International Conference on Image Processing, 2010.
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Fishing in Poisson streams: focusing on the whales, ignoring the minnows. Raginsky, M.; Jafarpour, S.; Willett, R.; and Calderbank, R. In Proc. Forty-Fourth Conference on Information Sciences and Systems, 2010. \hrefhttp://arxiv.org/abs/1003:2836arXiv:1003.2836
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Gradient projection for linearly constrained convex optimization in sparse signal recovery. Harmany, Z.; Thompson, D.; Willett, R.; and Marcia, R. In Proc. IEEE International Conference on Image Processing, 2010.
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Hyperspectral target detection from incoherent projections. Krishnamurthy, K.; Raginsky, M.; and Willett, R. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010.
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SPIRAL out of convexity: Sparsity-regularized algorithms for photon-limited imaging. Harmany, Z. T.; Marcia, R. F.; and Willett, R. M. In Proc. SPIE Computational Imaging, 2010.
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  2009 (9)
The False Discovery Rate for Statistical Pattern Recognition. Scott, C.; Bellala, G.; and Willett, R. Electronic Journal of Statistics, 3: 651 - 677. 2009. \hrefhttp://arxiv.org/abs/0901.4184arXiv:0901.4184
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Hypergraph-based anomaly detection in very large networks. Silva, J.; and Willett, R. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3): 563-569. 2009. \hrefhttp://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.232doi:10.1109/TPAMI.2008.232
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Performance Bounds for Expander-Based Compressed Sensing in the Presence of Poisson Noise. Jafarpour, S.; Willett, R.; Raginsky, M.; and Calderbank, R. In Proc. Asilomar Conference on Signals, Systems and Computers, 2009. Winner of best student paper award. \hrefhttp://arxiv.org/abs/0911.1368arXiv:0911.1368
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Sparse Poisson Intensity Reconstruction Algorithms. Harmany, Z. T.; Marcia, R.; and Willett, R. In IEEE Workshop on Statistical Signal Processing, 2009. \hrefhttp://arxiv.org/abs/0905.0483arXiv:0905.0483
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Image Reconstruction of Multiphoton Microscopy Data. Doot, H. J.; Eliceiri, K.; Nowak, R.; and Willett, R. In IEEE International Symposium on Biomedical Imaging, 2009.
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Performance bounds for compressed sensing with Poisson noise. Willett, R.; and Raginsky, M. In Proc. of IEEE Int. Symp. on Inf. Theory, 2009.
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Sequential Probability Assignment Via Online Convex Programming Using Exponential Families. Raginsky, M.; Marcia, R.; Silva, J.; and Willett, R. In Proc. of IEEE International Symposium on Information Theory, 2009.
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Integrating sensing and processing in an electrical and computer engineering curriculum. Ybarra, G.; Collins, L. M.; Huettel, L. G.; Massoud, H. Z.; Board, J.; Brooke, M.; Jokerst, N.; Choudhury, R. R.; Gustafson, M. R.; Willett, R. M.; and Coonley, K. In Proc. Frontiers in Education Conference, 2009.
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Compressive Coded Aperture Imaging. Marcia, R.; Harmany, Z.; and Willett, R. In SPIE Electronic Imaging, 2009.
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  2008 (10)
Multi-excitation Raman spectroscopy technique for fluorescence rejection. McCain, S.; Willett, R.; and Brady, D. Optics Express, 16(15): 10975-10991. 2008.
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Superimposed video disambiguation for increased field of view. Marcia, R.; Kim, C.; Kim, J.; Brady, D.; and Willett, R. Optics Express, 16(31): 16352-16363. 2008.
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Thin infrared imaging systems through multi-channel sampling. Shankar, M.; Willett, R.; Pitsianis, N.; Schulz, T.; Gibbons, R.; Kolste, R. T.; Carriere, J.; Chen, C.; Prather, D.; and Brady, D. Applied Optics, 47(10): B1-B10. 2008.
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Single disperser design for coded aperture snapshot spectral imaging. Wagadarikar, A.; John, R.; Willett, R.; and Brady, D. Applied Optics, 47(10): B44-B51. 2008.
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Near-Minimax Recursive Density Estimation on the Binary Hypercube. Raginsky, M.; Lazebnik, S.; Willett, R.; and Silva, J. In Proc. Neural Information Processing Systems, 2008.
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Controlling the error in fMRI: Hypothesis testing or set estimation?. Harmany, Z.; Willett, R.; Singh, A.; and Nowak, R. In Proc. IEEE International Symposium on Biomedical Imaging � ISBI, 2008.
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Compressive Coded Aperture Video Reconstruction. Marcia, R.; and Willett, R. In Proc. European Signal Processing Conference � EUSIPCO, 2008.
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Fast disambiguation of superimposed images for increased field of view. Marcia, R. F.; Kim, C.; Kim, J.; Brady, D. J.; and Willett, R. M. In Proceedings of the IEEE International Conference on Image Processing (ICIP), pages 2620–2623, San Diego, CA, October 2008.
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Detection of anomalous meetings in a social network. Silva, J.; and Willett, R. In Proc. Conference on Information Sciences and Systems (CISS), 2008.
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Compressive Coded Aperture Superresolution Image Reconstruction. Marcia, R.; and Willett, R. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), 2008.
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  2007 (12)
Single-shot compressive spectral imaging with a dual-disperser architecture. Gehm, M.; John, R.; Brady, D.; Willett, R.; and Schultz, T. Optics Express, 15(21): 14013-14027. 2007. \hrefhttp://dx.doi.org/10.1364/OE.15.014013doi:10.1364/OE.15.014013
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Minimax optimal level set estimation. Willett, R.; and Nowak, R. IEEE Transactions on Image Processing, 16(12): 2965-2979. 2007.
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Multiscale Poisson intensity and density estimation. Willett, R.; and Nowak, R. IEEE Transactions on Information Theory, 53(9): 3171-3187. 2007. \hrefhttp://dx.doi.org/10.1109/TIT.2007.903139doi:10.1109/TIT.2007.903139
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Multiscale-Analysis of Photon-Limited Astronomical Images. Willett, R. In Statistical Challenges in Modern Astronomy IV, volume 371, of Astronomical Society of the Pacific Conference Series, 2007.
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Single disperser design for compressive, single-snapshot spectral imaging. Wagadarikar, A.; John, R.; Willett, R.; and Brady, D. In Proceedings of SPIE Optics and Photonics, 2007.
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Generalization error analysis for FDR controlled classification. Scott, C.; Bellala, G.; and Willett, R. In Proceedings of IEEE Statistical Signal Processing Workshop (SSP), 2007.
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Multiscale reconstruction for photon-limited hyperspectral data. Krishnamurthy, K.; and Willett, R. In Proceedings of IEEE Statistical Signal Processing Workshop (SSP), 2007.
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Multiscale intensity estimation for multi-photon microscopy. Willett, R. In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 2007.
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Multiscale reconstruction for photon-limited shifted excitation Raman spectroscopy. Willett, R. In Proceedings of IEEE Int. Conf. Acoust., Speech, Signal Processing (Proc. International Conference on Acoustics, Speech, and Signal Processing), 2007.
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Multiscale intensity estimation for marked Poisson processes. Willett, R. In Proceedings IEEE Int. Conf. Acoust., Speech, Signal Processing (Proc. International Conference on Acoustics, Speech, and Signal Processing), 2007.
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Multiscale reconstruction for computational spectral imaging. R. Willett, M. G.; and Brady, D. In Proceedings of SPIE Electronic Imaging, Computational Imaging V, 2007.
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Multiscale-Analysis of Photon-Limited Astronomical Images. Willett, R. In Statistical Challenges in Modern Astronomy IV, volume 371, of Astronomical Society of the Pacific Conference Series, 2007.
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  2006 (3)
Multiscale Analysis of Photon-Limited Astronomical Images. Willett, R. In Proceedings of Statistical Challenges in Modern Astronomy (SCMA) IV, 2006.
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Toward a Model for Sources of Internet Background Radiation. Barford, P.; R. Nowak, R. W.; and Yegneswaran, V. In Proceedings of the Passive and Active Measurement Conference (PAM '06), 2006.
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Ultra-thin Multiple-channel LWIR Imaging Systems. Shankar, M.; Willett, R.; Pitsianis, N.; Kolste, R. T.; Chen, C.; Gibbons, R.; and Brady, D. In Proceedings of SPIE Optics and Photonics, 2006.
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  2005 (4)
Level Set Estimation via Trees. Willett, R.; and Nowak, R. In Proceedings of IEEE Int. Conf. Acoust., Speech, Signal Processing (Proc. International Conference on Acoustics, Speech, and Signal Processing), 2005.
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Level Set Estimation in Medical Imaging. Willett, R.; and Nowak, R. In Proceedings of IEEE Statistical Signal Processing Workshop, 2005.
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Faster Rates in Regression via Active Learning. Castro, R.; Willett, R.; and Nowak, R. In Neural Information Processing Systems, 2005.
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Minimax Optimal Level Set Estimation. Willett, R.; and Nowak, R. In Proceedings of Wavelets XI at the SPIE Annual Meeting, 2005.
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  2004 (7)
Estimating Inhomogeneous Fields Using Wireless Sensor Networks. Nowak, R.; Mitra, U.; and Willett, R. IEEE Journal on Selected Areas in Communications, 22(6): 999-1006. 2004.
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Fast, Near-Optimal, Multiresolution Estimation of Poisson Signals and Images. Willett, R.; and Nowak, R. In Proc. Twelfth European Signal Processing Conference — EUSIPCO~'04, 6-10 Sept., Vienna, Austria, 2004.
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Complexity-Regularized Multiresolution Density Estimation. Willett, R.; and Nowak, R. In Proc. IEEE Int. Sym. Information Theory — ISIT~'04, 27 June - 2 July, Chicago, IL, USA, 2004.
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Adaptive Sampling for Wireless Sensor Networks. Willett, R.; Martin, A.; and Nowak, R. In Proc. IEEE Int. Sym. Information Theory — ISIT~'04, 27 June - 2 July, Chicago, IL, USA, 2004.
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Fast Multiresolution Photon-Limited Image Reconstruction. Willett, R.; and Nowak, R. In Proc. IEEE Int. Sym. Biomedical Imaging — ISBI~'04, 15-18 April, Arlington, VA, USA, 2004.
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Coarse-to-Fine Manifold Learning . Castro, R.; Willett, R.; and Nowak, R. In Proc. International Conference on Acoustics, Speech, and Signal Processing~'04, 17-21 May, Montreal, CA, 2004.
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Backcasting: Adaptive Sampling for Sensor Networks. Willett, R.; Martin, A.; and Nowak, R. In Proc. Information Processing in Sensor Networks, 26-27 April, Berkeley, CA, USA, 2004.
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  2003 (4)
Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging. Willett, R.; and Nowak, R. IEEE Transactions on Medical Imaging, 22(3): 332-350. 2003. r̆lhttp://dx.doi.org/10.1109/TMI.2003.809622doi:10.1109/TMI.2003.809622
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Wavelet-Based Superresolution in Astronomy . Willett, R.; Jermyn, I.; Nowak, R.; and Zerubia, J. In Proc. Astronomical Data Analysis Software and Systems XIII , 12-15 October, Strasbourg, France, 2003.
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CORT: Classification \em Or Regression Trees. Scott, C.; Willett, R.; and Nowak, R. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing — Proc. International Conference on Acoustics, Speech, and Signal Processing~'03, 6-10 April, Hong Kong, 2003.
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Multiscale Likelihood Analysis and Image Reconstruction . Willett, R.; and Nowak, R. In Proc. SPIE Vol. 5207, Wavelets X, 4-8 August, San Diego, CA, USA, 2003.
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  2002 (3)
Platelets for Multiscale Analysis in Medical Imaging. Willett, R.; and Nowak, R. In Proc. of the 2nd Joint Meeting of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society — EMBS-BMES~'02, 23-26 Oct., Houston, TX, USA, 2002.
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Platelets for Multiscale Analysis in Photon-Limited Imaging. Willett, R.; and Nowak, R. In Proc. IEEE Int. Conf. Image Processing — ICIP~'02, 22-25 Sept., Rochester, NY, USA, 2002.
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Multiresolution Nonparametric Intensity and Density Estimation. Willett, R.; and Nowak, R. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing — Proc. International Conference on Acoustics, Speech, and Signal Processing~'02, 13-17 May, Orlando, FL, USA, 2002.
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