Publications
| [1] | A. Joshi, M. Cho, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, and C. Hegde. In Proc. AAAI Conf. Artificial Intelligence, Feb. 2020. |
| [2] | V. Shah and C. Hegde. Signal reconstruction from modulo observations. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2019. |
| [3] | M. Cho and C. Hegde. Reducing the search space for hyperparameter optimization using group sparsity. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2019. |
| [4] | R. Hyder, V. Shah, C. Hegde, and S. Asif. Alternating phase projected gradient descent with generative priors for solving compressive phase retrieval. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2019. |
| [5] | A. Mukherjee, A. Joshi, S. Sarkar, and C. Hegde. Attribute-controlled traffic data augmentation using conditional generative models. In CVPR Workshop on Vision for All Seasons, Jun. 2019. |
| [6] | A. Joshi, A. Mukherjee, S. Sarkar, and C. Hegde. Semantic adversarial attacks: Parametric transformations that fool deep classifiers. In Inter. Conf. on Computer Vision (ICCV), Jul. 2019. |
| [7] | V. Shah, A. Joshi, S. Ghosal, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, and C. Hegde. Encoding invariances in deep generative models. Preprint, Jun. 2019. |
| [8] | G. Jagatap and C. Hegde. Algorithmic guarantees for inverse imaging with untrained network priors. In NeurIPS, Sep. 2019. |
| [9] | Z. Jiang, A. Balu, C. Hegde, and S. Sarkar. Incremental consensus-based collaborative deep learning. In Proc. ICML Workshop on Nonconvex Optimization for Machine Learning, July 2018. |
| [10] | G. Jagatap and C. Hegde. Towards sample-optimal methods for solving random quadratic equations with structure. In Proc. IEEE Int. Symp. Inform. Theory (ISIT), June 2018. |
| [11] | M. Soltani and C. Hegde. Fast low-rank matrix estimation for ill-conditioned matrices. In Proc. IEEE Int. Symp. Inform. Theory (ISIT), June 2018. |
| [12] | Z. Jiang, A. Balu, C. Hegde, and S. Sarkar. Decentralized stochastic momentum gradient descent for multi-agent learning. Preprint, June 2018. |
| [13] | G. Jagatap and C. Hegde. Learning ReLU networks using alternating minimization. Preprint, June 2018. |
| [14] | T. Nguyen, R. Wong, and C. Hegde. Autoencoders learn generative linear models. Preprint, June 2018. |
| [15] | T. Nguyen, R. Wong, and C. Hegde. Autoencoders learn generative linear models. In Proc. ICML Workshop on Theory and Applications of Deep Generative Modeling (TADGM), June 2018. |
| [16] | H. Rajan, E. Weber, C. Hegde, J. Smith, P. Aduri, Z. Zhu, J. Tao, H. Bagheri, I. Kouper, E. Rozier, S. Sarkar, R. Maitra, and B. Plale. Dependable Data Science: Challenges and Opportunities. Preprint, March 2018. |
| [17] | C. Hegde. Algorithmic aspects of inverse problems using generative models. In Proc. Allerton Conf. on Comm., Contr., and Comp., Oct. 2018. |
| [18] | S. Asif and C. Hegde. Phase retrieval for signals in a union of subspaces. In Proc. IEEE Global Conf. Signal and Image Processing (GlobalSIP), Nov. 2018. |
| [19] | P. Chakraborty, C. Hegde, and A. Sharma. Freeway incident detection from cameras: A semi-supervised learning approach. In Proc. IEEE Int. Conf. Intelligent Transportation Systems (ITSC), Nov. 2018. |
| [20] | M. Soltani and C. Hegde. Towards provable learning of polynomial neural networks using low-rank matrix estimation. In Proc. Intl. Conf. Artificial Intelligence and Statistics (AISTATS), Apr. 2018. |
| [21] | T. Nguyen, R. Wong, and C. Hegde. A provable approach for double-sparse coding. In Proc. AAAI Conf. Artificial Intelligence, Feb. 2018. |
| [22] | M. Soltani and C. Hegde. Provable algorithms for learning two-layer polynomial neural networks. Preprint, Jan. 2018. |
| [23] | G. Jagatap, Z. Chen, C. Hegde, and N. Vaswani. Fourier ptychography using structured sparsity. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2018. |
| [24] | Z. Chen, G. Jagatap, S. Nayer, C. Hegde, and N. Vaswani. Low rank fourier ptychography. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2018. |
| [25] | V. Shah and C. Hegde. Solving linear inverse problems using gan priors: An algorithm with provable guarantees. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2018. |
| [26] | T. Nguyen, A. Soni, and C. Hegde. On learning sparsely used dictionaries from incomplete samples. In Proc. Int. Conf. Machine Learning (ICML), Jul. 2018. |
| [27] | G. Jagatap, Z. Chen, C. Hegde, and N. Vaswani. Model corrected low rank ptychography. In Proc. IEEE Conf. Image Proc., Sept. 2018. |
| [28] | P. Chakraborty, C. Hegde, and A. Sharma. Data-driven traffic incident detection using robust summary statistics. Preprint, Sept. 2018. |
| [29] | G. Jagatap, Z. Chen, C. Hegde, and N. Vaswani. Fourier ptychography for structured data. Preprint, Nov. 2018. |
| [30] | R. Singh, V. Shah, B. Pokuri, B. Ganapathysubramanian, S. Sarkar, and C. Hegde. Physics-aware deep generative models for microstructure simulation. In Proc. NIPS Workshop on Machine Learning for Molecules and Materials, Dec. 2018. |
| [31] | V. Shah and C. Hegde. Signal reconstruction from modulo observations. Preprint, Oct. 2018. |
| [32] | C. Hegde and A. Kamal. Theoretical foundations of computer engineering. Monograph available online, June 2017. |
| [33] | C. Hegde. Lecture notes on data analytics. Monograph available online, June 2017. |
| [34] | M. Soltani and C. Hegde. Improved algorithms for matrix recovery from rank-one projections. Preprint, available online at http://arxiv.org/abs/1705.07469, May 2017. |
| [35] | M. Soltani and C. Hegde. Fast algorithms for demixing signals from nonlinear observations. IEEE Trans. Sig. Proc., 65(16):4209--4222, Aug. 2017. |
| [36] | C. Hubbard and C. Hegde. Parallel computing heuristics for matrix completion. In Proc. IEEE Global Conf. Signal and Image Processing (GlobalSIP), Nov. 2017. |
| [37] | B. Wang, C. Gan, J. Yang, C. Hegde, and J. Wu. Graph-based multiple-line outage identification in power transmission systems. In IEEE Power and Engineering Systems General Meeting (PES), Jul. 2017. |
| [38] | M. Soltani and C. Hegde. Stable recovery of sparse vectors from random sinusoidal feature maps. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Mar. 2017. |
| [39] | D. Sahoo, C. Hegde, and M. Bhattacharya. A programmed digital image analysis tool for rapid and precise phenotyping for arabidopsis at early stages. Preprint, Jan. 2017. |
| [40] | C. Hegde. Learning graph evolutions from cut sketches: Faster algorithms, fewer samples. In Proc. Asilomar Conf. Sig. Sys. Comput., Nov. 2017. |
| [41] | V. Shah, M. Soltani, and C. Hegde. Reconstruction from periodic nonlinearities, with applications to HDR imaging. In Proc. Asilomar Conf. Sig. Sys. Comput., Nov. 2017. |
| [42] | G. Jagatap and C. Hegde. Sample-efficient algorithms for recovering structured signals from magnitude-only measurements. Available online at http://arxiv.org/abs/1705.06412, Nov. 2017. |
| [43] | Z. Jiang, A. Balu, C. Hegde, and S. Sarkar. Collaborative deep learning over fixed topology networks. In Adv. Neural Inf. Proc. Sys. (NIPS), Dec. 2017. |
| [44] | M. Soltani and C. Hegde. Fast algorithms for learning latent variables in graphical models. In Proc. ACM KDD Workshop on Mining and Learning with Graphs (KDD MLG), Aug. 2017. |
| [45] | G. Jagatap and C. Hegde. Fast sample-efficient algorithms for structured phase retrieval. In Adv. Neural Inf. Proc. Sys. (NIPS), Dec. 2017. |
| [46] | T. Nguyen, R. Wong, and C. Hegde. A provable approach for double-sparse coding. Preprint, available online at https://arxiv.org/abs/1711.03638, Nov. 2017. |
| [47] | M. Cohen, C. Hegde, S. Jegelka, and L. Schmidt. Efficiently optimizing over (non-convex) cones via approximate projections. In Proc. NIPS Workshop on Optimization for Machine Learning (OPT), Dec. 2017. |
| [48] | A. Balu, T. Nguyen, A. Kokate, C. Hegde, and S. Sarkar. A forward-backward approach for visualizing information flow in deep networks. In Proc. NIPS Symposium on Interpretability for Machine Learning, Dec. 2017. |
| [49] | M. Soltani and C. Hegde. Fast low-rank matrix estimation without the condition number. Preprint, Dec. 2017. |
| [50] | P. Chakraborty, C. Hegde, and A. Sharma. Trend filtering in network time series with applications to traffic incident detection. In Proc. NIPS Time Series Workshop (TSW), Dec. 2017. |
| [51] | M. Soltani and C. Hegde. Demixing structured superposition signals from periodic and aperiodic nonlinearities. In Proc. IEEE Global Conf. Signal and Image Processing (GlobalSIP), Nov. 2017. |
| [52] | C. Hubbard and C. Hegde. GPUFish: A parallel computing framework for matrix completion from a few observations. Technical report, Iowa State University, December 2016. |
| [53] | C. Hegde, P. Indyk, and L. Schmidt. Nearly linear-time algorithms for graph-structured sparsity. In Proc. Intl. Joint Conf. Artificial Intelligence (IJCAI), Best Paper Awards Track, July 2016. |
| [54] | C. Hegde. Bilevel feature selection in nearly-linear time. In Proc. Stat. Sig. Proc. (SSP), June 2016. |
| [55] | C. Hegde. A fast algorithm for demixing signals with structured sparsity. In Proc. Intl. Conf. Sig. Proc. Comm. (SPCOM), June 2016. |
| [56] | M. Soltani and C. Hegde. Demixing sparse signals from nonlinear observations. Technical report, Iowa State University, March 2016. |
| [57] | C. Hegde. Bilevel feature selection in nearly-linear time. Preprint, 2016. |
| [58] | C. Hegde, P. Indyk, and L. Schmidt. Fast recovery from a union of subspaces. In Adv. Neural Inf. Proc. Sys. (NIPS), Dec. 2016. |
| [59] | M. Soltani and C. Hegde. Demixing sparse signals from nonlinear observations. In Proc. Asilomar Conf. Sig. Sys. Comput., Nov. 2016. |
| [60] | M. Soltani and C. Hegde. Iterative thresholding for demixing structured superpositions in high dimensions. In Proc. NIPS Workshop on Learning in High Dimensions with Structure (LHDS), Dec. 2016. |
| [61] | C. Hubbard, J. Bavslik, C. Hegde, and C. Hu. Data-driven prognostics of lithium-ion rechargeable battery using bilinear kernel regression. In Annual Conf. Prognostics and Health Management (PHM), Oct. 2016. |
| [62] | M. Soltani and C. Hegde. A fast iterative algorithm for demixing sparse signals from nonlinear observations. In Proc. IEEE Global Conf. Signal and Image Processing (GlobalSIP), Dec. 2016. |
| [63] | C. Hegde, P. Indyk, and L. Schmidt. A nearly linear-time framework for graph-structured sparsity. In Proc. Int. Conf. Machine Learning (ICML), July 2015. |
| [64] | Y. Li, C. Hegde, A. Sankaranarayanan, R. Baraniuk, and K. Kelly. Compressive image classification via secant projections. J. Optics, 17(6), June 2015. |
| [65] | J. Acharya, I. Diakonikolas, C. Hegde, J. Li, and L. Schmidt. Fast and near-optimal algorithms for approximating distributions by histograms. In Proc. Symp. Principles of Database Systems (PODS), May 2015. |
| [66] | M. Araya-Polo, C. Hegde, P. Indyk, and L. Schmidt. Greedy strategies for data-adaptive shot selection. In Proc. Annual EAGE Meeting, May 2015. |
| [67] | C. Hegde, P. Indyk, and L. Schmidt. Approximation algorithms for model-based compressive sensing. IEEE Trans. Inform. Theory, 61(9):5129--5147, Sept. 2015. |
| [68] | C. Hegde, A. Sankaranarayanan, W. Yin, and R. Baraniuk. NuMax: A convex approach for learning near-isometric linear embeddings. IEEE Trans. Sig. Proc., 63(22):6109--6121, Nov. 2015. |
| [69] | L. Schmidt, C. Hegde, P. Indyk, L. Lu, X. Chi, and D. Hohl. Seismic feature extraction using Steiner tree methods. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Apr. 2015. |
| [70] | C. Hegde, P. Indyk, and L. Schmidt. Fast algorithms for structured sparsity. Bulletin of the EATCS, 1(117):197--228, Oct. 2015. |
| [71] | C. Hegde, P. Indyk, and L. Schmidt. Nearly linear-time model-based compressive sensing. In Proc. Intl. Colloquium on Automata, Languages, and Programming (ICALP), July 2014. |
| [72] | Y. Li, C. Hegde, and K. Kelly. Object tracking via compressive sensing. In Proc. Comput. Optical Sensing and Imaging (COSI), June 2014. |
| [73] | C. Hegde, P. Indyk, and L. Schmidt. A fast approximation algorithm for tree-sparse recovery. In Proc. IEEE Int. Symp. Inform. Theory (ISIT), June 2014. |
| [74] | L. Schmidt, C. Hegde, P. Indyk, J. Kane, L. Lu, and D. Hohl. Automatic fault localization using the Generalized Earth Movers Distance. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), May 2014. |
| [75] | C. Hegde, A. Sankaranarayanan, and R. Baraniuk. Lie operators for compressive sensing. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), May 2014. |
| [76] | S. Nagaraj, C. Hegde, A. Sankaranarayanan, and R. Baraniuk. Optical flow-based transport for image manifolds. Appl. Comput. Harmon. Anal., 36(2):280--301, March 2014. |
| [77] | C. Hegde, P. Indyk, and L. Schmidt. Approximation-tolerant model-based compressive sensing. In Proc. ACM Symp. Discrete Alg. (SODA), Jan. 2014. |
| [78] | C. Hegde, P. Indyk, and L. Schmidt. A fast adaptive variant of the GW algorithm for the Prize-Collecting Steiner Tree problem. DIMACS Workshop, Dec. 2014. |
| [79] | L. Schmidt, C. Hegde, and P. Indyk. The Constrained Earth Movers Distance model, with applications to compressive sensing. In Proc. Sampling Theory and Appl. (SampTA), July 2013. |
| [80] | C. Hegde, A. Sankaranarayanan, and R. Baraniuk. Learning measurement matrices for redundant dictionaries. In Proc. Work. Struc. Parc. Rep. Adap. Signaux (SPARS), July 2013. |
| [81] | Y. Li, C. Hegde, R. Baraniuk, and K. Kelly. Compressive classification via secant projections. In Proc. Comput. Optical Sensing and Imaging (COSI), June 2013. |
| [82] | E. Grant, C. Hegde, and P. Indyk. Nearly optimal linear embeddings into very low dimensions. In Proc. IEEE Global Conf. Signal and Image Processing (GlobalSIP), Dec. 2013. |
| [83] | C. Hegde, P. Indyk, and L. Schmidt. Approximation algorithms for two-dimensional structured sparsity models. Preprint, Dec. 2013. |
| [84] | C. Hegde and L. Schmidt. Model-based recovery of sparse matrices with bounded degree distributions. Preprint, Nov. 2013. |
| [85] | C. Hegde and R. Baraniuk. SPIN: Iterative signal recovery on incoherent manifolds. In Proc. IEEE Int. Symp. Inform. Theory (ISIT), July 2012. |
| [86] | C. Hegde, A. Sankaranarayanan, and R. Baraniuk. Learning manifolds in the wild. Preprint, July 2012. |
| [87] | C. Hegde, O. Tuzel, and F. Porikli. Efficient upsampling of natural images. MERL Technical Report, March 2012. |
| [88] | C. Hegde and R. Baraniuk. Signal recovery on incoherent manifolds. IEEE Trans. Inform. Theory, 58(12):7204--7214, Dec. 2012. |
| [89] | D. Grady, M. Moll, C. Hegde, A. Sankaranarayanan, R. Baraniuk, and L. Kavraki. Multi-objective sensor replanning for a car-like robot. In Proc. IEEE Int. Symp. on Safety, Security, and Rescue Robotics (SSRR), Nov. 2012. |
| [90] | D. Grady, M. Moll, C. Hegde, A. Sankaranarayanan, R. Baraniuk, and L. Kavraki. Multi-robot target verification with reachability constraints. In Proc. IEEE Int. Symp. on Safety, Security, and Rescue Robotics (SSRR), Nov. 2012. |
| [91] | C. Hegde. Nonlinear Signal Models: Geometry, Algorithms, and Analysis. PhD thesis, ECE Department, Rice University, Sept. 2012. |
| [92] | C. Hegde, A. Sankaranarayanan, and R. Baraniuk. Near-isometric linear embeddings of manifolds. In Proc. Stat. Sig. Proc. (SSP), Aug. 2012. |
| [93] | C. Hegde and R. Baraniuk. Sampling and recovery of pulse streams. IEEE Trans. Sig. Proc., 59(4):1505--1517, Apr. 2011. |
| [94] | D. Grady, M. Moll, C. Hegde, A. Sankaranarayanan, R. Baraniuk, and L. Kavraki. Look before you leap: Predictive sensing and opportunistic navigation. In Proc. IROS Workshop on Open Prob. Motion Plan., Sept. 2011. |
| [95] | A. Sankaranarayanan, C. Hegde, S. Nagaraj, and R. Baraniuk. Go with the flow: Optical flow-based transport operators for image manifolds. In Proc. Allerton Conf. on Comm., Contr., and Comp., Sept. 2011. |
| [96] | R. Baraniuk, M. Davenport, M. Duarte, and C. Hegde. An Introduction to Compressive Sensing. Connexions e-textbook, 2011. |
| [97] | S. Schnelle, J. Laska, C. Hegde, M. Duarte, M. Davenport, and R. Baraniuk. Texas hold 'em algorithms for distributed compressive sensing. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), March 2010. |
| [98] | C. Hegde and R. Baraniuk. Compressive sensing of a superposition of pulses. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), March 2010. |
| [99] | M. Davenport, C. Hegde, M. Duarte, and R. Baraniuk. High-dimensional data fusion via joint manifold learning. In Proc. AAAI Fall Symp. on Manifold Learning, Nov. 2010. |
| [100] | R. Baraniuk, V. Cevher, M. Duarte, and C. Hegde. Model-based compressive sensing. IEEE Trans. Inform. Theory, 56(4):1982--2001, Apr. 2010. |
| [101] | M. Davenport, C. Hegde, M. Duarte, and R. Baraniuk. Joint manifolds for data fusion. IEEE Trans. Image Proc., 19(10):2580--2594, Oct. 2010. |
| [102] | V. Cevher, P. Indyk, C. Hegde, and R. Baraniuk. Recovery of clustered sparse signals from compressive measurements. In Proc. Sampling Theory and Appl. (SampTA), May 2009. |
| [103] | M. Duarte, C. Hegde, V. Cevher, and R. Baraniuk. Recovery of compressible signals from unions of subspaces. In Proc. IEEE Conf. Inform. Science and Systems (CISS), March 2009. |
| [104] | C. Hegde, M. Duarte, and V. Cevher. Compressive sensing recovery of spike trains using a structured sparsity model. In Proc. Work. Struc. Parc. Rep. Adap. Signaux (SPARS), Apr. 2009. |
| [105] | M. Davenport, C. Hegde, M. Duarte, and R. Baraniuk. A theoretical analysis of joint manifolds. Technical report, Rice University ECE Department, Jan. 2009. |
| [106] | C. Hegde and R. Baraniuk. Compressive sensing of streams of pulses. In Proc. Allerton Conf. on Comm., Contr., and Comp., Sept. 2009. |
| [107] | V. Cevher, M. Duarte, C. Hegde, and R. Baraniuk. Sparse signal recovery using Markov Random Fields. In Adv. Neural Inf. Proc. Sys. (NIPS), Dec. 2008. |
| [108] | C. Hegde, M. Wakin, and R. Baraniuk. Random projections for manifold learning: Proofs and analysis. Technical Report TREE-0710, Rice Univ., ECE Dept., Dec. 2007. |
| [109] | C. Hegde, M. Davenport, M. Wakin, and R. Baraniuk. Efficient machine learning using random projections. In Proc. NIPS Workshop on Efficient Machine Learning, Dec. 2007. |
| [110] | M. Davenport, C. Hegde, M. Wakin, and R. Baraniuk. Manifold-based approaches for improved classification. In Proc. NIPS Workshop on Topology Learning, Dec. 2007. |
| [111] | C. Hegde, M. Wakin, and R. Baraniuk. Random projections for manifold learning. In Adv. Neural Inf. Proc. Sys. (NIPS), Dec. 2007. |
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