Publications

From ARO MURI
Publications
Jump to: navigation, search

Publications reporting on research supported by the ARO MURI on Adaptive Exploitation of Non-Commutative Multimodal Information Structure

publications by year

2016

  1. V. Kostina and S. Verdu, "Nonasymptotic noisy lossy source coding," IEEE Transactions on Information Theory, vol. 62, no. 11, pp. 6111-6123, 2016.
  2. I. Sason and S. Verdu, "f-divergence inequalities," IEEE Transactions on Information Theory, vol. 62, no. 11, pp. 5973{6006, 2016.
  1. P.-Y. Chen, B. Zhang, M. Al Hasan, "Incremental Method for Spectral Clustering of Increasing Orders," 12th International Workshop on Mining and Learning with Graphs, San Francisco Aug. 2016.
  2. P.-Y. Chen, C.-C, Tu, P.-S. Ting, Y.-Y. Lo, D. Koutra and A.O. Hero, "Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach," arXiv:1609.05378, Sep 2016.
  3. T.P. Coleman, J. Tantiongloc, A. Allegra, D. Mesa, D. Kang, and M. Mendoza, "Diffeomorphism learning via relative entropy constrained optimal transport," in Signal and Information Processing (GlobalSIP), 2016 IEEE Global Conference on. IEEE, 2016, pp. 1330-1334.
  4. K. Greenewald, S. Kelley, A.Hero, "Dynamic metric learning from pairwise comparisons," Allerton Conference on Communication, Control and Computing 2016.
  5. A. Jain, S.R. Kulkarni, and S. Verdu, "Energy efficiency of wireless cooperation," in Communication, Control, and Computing (Allerton), 2016 54th Annual Allerton Conference on. IEEE, 2016, pp. 664-671.
  6. A. Jain, A.R. Zamir, S. Savarese, and A. Saxena, "Structuralrnn: Deep learning on spatio-temporal graphs," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  7. W. Lu, V. Tarokh and A.O. Hero, "Analysis of a privacy-preserving PCA algorithm via random matrix theory," IEEE GlobalSIP Conference, Dec 2016.
  8. W. Lu, A.O. Hero and V. Tarokh, "Scaling laws and phase transitions for target detection in MIMO radar," IEEE Information Theory Workshop, Cambridge UK, 2016.
  9. S. Ravishankar, B.E. Moore, R.R. Nadakuditi, and J.A. Fessler, "Efficient learning of dictionaries with low-rank atoms," in Signal and Information Processing (GlobalSIP), 2016 IEEE Global Conference on. IEEE, 2016, pp. 222-226.
  10. S. Ravishankar, R.R. Nadakuditi, and J.A. Fessler, "Sum of outer products dictionary learning for inverse problems," in Signal and Information Processing (GlobalSIP), 2016 IEEE Global Conference on. IEEE, 2016, pp. 1142-1146.
  11. I. Sason and S. Verdu, "f-divergence inequalities via functional domination," in Science of Electrical Engineering (ICSEE), IEEE International Conference on the. IEEE, 2016, pp. 1-5.
  12. G. Vazquez-Vilar, A. Guill{\'e}n i F{\`a}bregas, and Sergio Verdu, "From the metaconverse bound to perfect codes," in International Conference on the Science of Electrical Engineering (ICSEE), 2016.
  13. M. Wilmanski, C. Kreucher, A.O. Hero, "Complex input convolutional neural networks for wide angle SAR ATR," IEEE GlobalSIP Conference, Dec 2016.
  14. H. Wu and R.R. Nadakuditi, "Free component analysis," in Signals, Systems and Computers, 2016 50th Asilomar Conference on. IEEE, 2016, pp. 85-89.

2017

  1. Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G.S. Buller, S. McLaughlin, and AO Hero, "Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields," IEEE Transactions on Computational Imaging, 3(4), pp.658-670, 2017.
  2. P.-Y. Chen and A.O. Hero, "Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms," IEEE Tran. in Signal and Information Processing over Networks, vol. 3, no. 3 pp. 553-567, 2017. Arxiv:1609.07200
  3. H. Firouzi, B. Rajaratnam, A.O. Hero, "Two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)," IEEE Transactions on Information Theory, vol. 63, no. 1, pp. 698 - 714, Jan. 2017. https://arxiv.org/abs/1502:06189.
  4. K. Greenewald, S. Kelley, B. Oselio, A.O. Hero, "Similarity Function Tracking using Pairwise Comparisons," IEEE Transactions on Signal Processing, vol. 65, no. 21, pp. 5635-5648, 2017. arxiv:1610.03090.
  5. V. Kostina, Y. Polyanskiy, and S. Verdu, "Joint source-channel coding with feedback," IEEE Transactions on Information Theory, vol. 63, no. 6, pp. 3502-3515, 2017.
  6. J. Liu, P. Cuff, and S. Verdu, "E_$\gamma$-resolvability," IEEE Transactions on Information Theory, vol. 63, no. 5, pp. 2629-2658, 2017.
  7. S. Ravishankar, RR Nadakuditi, and JFessler, "Efficient sum of outer products dictionary learning (soup-dil) and its application to inverse problems," IEEE Transactions on Computational Imaging, 2017.
  8. S. Ravishankar, BE Moore, RR Nadakuditi, and JA Fessler, "Lowrank and adaptive sparse signal (lassi) models for highly accelerated dynamic imaging," IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1116-1128, 2017.
  9. I. Soloveychik, Y. Xiang, and V. Tarokh, "Pseudo-Wigner matrices," accepted in IEEE Transactions on Information Theory, arXiv:1701.05544, 2017.
  10. Justin Tantiongloc, Diego A Mesa, Rui Ma, Sanggyun Kim, Cristian H Alzate, Jaime J Camacho, Vidya Manian, and Todd P Coleman, "An information and control framework for optimizing user-compliant human-computer interfaces," Proceedings of the IEEE, vol. 105, no. 2, pp. 273-285, 2017.
  11. Anh Truong, S Rasoul Etesami, Jalal Etesami, and Negar Kiyavash, "Optimal attack strategies against predictors-learning from expert advice," IEEE Transactions on Information Forensics and Security, 2017.
  12. Wisler, A., Berisha, V., Spanias, A. and Hero, A.O., "Direct estimation of density functionals using a polynomial basis," IEEE Transactions on Signal Processing, 66(3), pp.558-572. 2017.
  1. AR Asadi, E. Abbe, and S. Verdu, "Compressing data on graphs with clusters," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, pp. 1583-1587,2017.
  2. A Bhargava, R Ganti, and R Nowak, "Active positive semidefinite matrix completion: Algorithms, theory and applications," in Artificial Intelligence and Statistics (AISTATS), pp. 1349-1357, 2017.
  3. S.P. Chepuri, S. Liu, G. Leus, and A.O. Hero, "Learning sparse graphs under smoothness prior," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  4. P.-Y. Chen and A.O. Hero, "AMOS: an automated model order selection algorithm for spectral graph clustering," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  5. TP Coleman, "Dynamical systems, ergodicity, and posterior matching," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017, pp. 2678-2682.
  6. A. Ghassami and N. Kiyavash, "Interaction information for causal inference: The case of directed triangle," Proceedings of ISIT, June, 25-30, 2017, Aachen, Germany.
  7. Y. Kim and S. Verdu, "Fixed-length-parsing universal compression with side information," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017, pp. 2563-2567
  8. J. Liu, R. van Handel, and S. Verdu, "Beyond the blowing-up lemma: Sharp converses via reverse hypercontractivity," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017, pp. 943-947.
  9. S. Liu, S. P. Chepuri, G. Leus and A.O. Hero, "Distributed sensor selection for field estimation," Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  10. S. Liu, P.-Y. Chen and A.O. Hero, "Distributed optimization for evolving networks of growing connectivity," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  11. K. Moon, S. Sekeh, M. Noshad and A.O. Hero, "Information theoretic structure learning with confidence," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017. arXiv preprint arXiv:1609.03912.
  12. K. Moon, K. Sricharan, A.Hero, "Ensemble estimation of mutual information," Proceedings of the IEEE Intl Symp. on Information Theory (ISIT), Aachen, June 2017. arXiv preprint arXiv:1701.08083.
  13. K. Moon, K. Sricharan, A. Hero, "Ensemble Estimation of Distributional Functionals via k-Nearest Neighbors," Allerton Conference, 2017
  14. M. Noshad, S. Sekeh, K. Moon and A. Hero, "Direct Estimation of Information Divergence Using Nearest Neighbor Ratios," Proceedings of the IEEE Intl Symp. on Information Theory (ISIT), Aachen, June 2017. arXiv preprint arXiv:1702.05222.
  15. B. Oselio and A.O. Hero, "Dynamic reconstruction of influence graphs with adaptive directed information," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  16. D. Pimentel-Alarcon and R. Nowak, "Random consensus robust pca," in Artificial Intelligence and Statistics, 2017, pp. 344-352.
  17. S. Ravishankar and JA Fessler, "Data-driven models and approaches for imaging," in Mathematics in Imaging. Optical Society of America, 2017, pp. MW2C4.
  18. S. Salehkaleybar, J. Etesami, and N. Kiyavash, "Identifying nonlinear 1-step causal in uences in the presence of latent variables," Proceedings of ISIT, June, 25-30, 2017, Aachen, Germany.
  19. I. Sason and S. Verdu, "Arimoto-Renyi conditional entropy and bayesian hypothesis testing," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017, pp. 2965-2969.
  20. Y. Shkel, M. Raginsky, and S. Verdu, "Universal lossy compression under logarithmic loss," in IEEE International Symposium on Information Theory, 2017.
  21. I. Soloveychik, Y. Xiang, and V. Tarokh, "Explicit symmetric pseudo-random matrices," Proceedings of ITW, November, 6-10, 2017, Kaohsiung, Taiwan.
  22. I. Soloveychik, Y. Xiang, and V. Tarokh, "On the spectral norms of pseudo-Wigner and related matrices," Proceedings of Allerton Conference, October, 4-6, 2017, University of Illinois at Urbana-Champaign.
  23. I. Soloveychik, Y. Xiang, and V. Tarokh, "Pseudo-Wigner matrices from dual bch codes," Proceedings of ISIT, June, 25-30, 2017, Aachen, Germany.
  24. S. Yagli, Y. Altug, and S. Verdu, "Minimax Renyi redundancy," Proceedings of ISIT, June, 25-30, 2017, Aachen, Germany, 2017.
  25. MH Yassaee, J. Liu, and S. Verdu, "One-shot multivariate covering lemmas via weighted sum and concentration inequalities," in Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017, pp. 669-673.
  26. X. Wang, C. Jung, A. O Hero, "Part-Level Fully Convolutional Networks for Pedestrian Detection," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, Mar 2017.
  1. K. Greenewald, S. Zhou, A.O. Hero, "The Tensor Graphical Lasso (TeraLasso)," 2017 (Appeared in 2019 in JRSS-B). arxiv1705.03983.

2018

  1. Y. Altmann, S. McLaughlin, M.J. Padgett, V.K. Goyal, A.O. Hero, and D.Faccio, "Quantum-inspired computational imaging," Science, Vol. 361, Issue 6403, 17 Aug 2018. http://science.sciencemag.org/content/361/6403/eaat2298
  2. P.-Y. Chen and A. Hero, "Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering," IEEE Trans on Signal Processing, Vol. 66 No. 13, pp. 3407 3420, July 2018.
  3. H.W. Chung, B.M> Sadler, L. Zheng and A.O. Hero, "Unequal error protection querying policies for the noisy 20 questions problem," IEEE Transactions on Information Theory, 64(2), pp.1105-1131, 2018.
  4. S. Liu, P.-Y. Chen and A. Hero, "Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity," IEEE Trans on Signal Processing. Vol. 66, No. 7, pp.1845-1859, July 2018.
  5. K.R. Moon, K. Sricharan, K. Greenewald, and A.O. Hero, "Ensemble Estimation of Information Divergence," Entropy (Special Issue on Information Theory for Machine Learning), vol. 20, no. 8, p. 560, July 2018. doi: 10.3390/e20080560.
  6. I. Soloveychik, Y. Xiang, and V. Tarokh, "Pseudo-Wigner matrices," IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 31703178, 2018.
  7. I. Soloveychik, Y. Xiang, and V. Tarokh, "Symmetric pseudo-random matrices," IEEE Transactions on Information The- ory, vol. 64, no. 4, pp. 31793196, 2018.
  8. Sadeghian, A., Voisin, M., Legros, F., Vesel, R., Alahi, A., and Savarese, S., "Car-net: Clairvoyant attentive recurrent network," In Proceedings of the European Conference on Computer Vision (ECCV), pp. 151-167, 2018.
  9. Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., and Savarese, S., "Taskonomy: Disentangling Task Transfer Learning," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3712-3722, 2018. Best Paper Award.
  1. T. Banerjee, G. Whipps, P. Gurram, V. Tarokh, "Sequential Event Detection Using Multimodal Data in Nonstationary Environments," in Proc. of the 21st International Conference on Information Fusion, July 2018.
  2. T. Banerjee, G. Whipps, P. Gurram, and V. Tarokh, "Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data," Accepted to IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018. https://arxiv.org/abs/1807.06945.
  3. H.-W. Chung, B. Sadler, L. Zheng, A. Hero, "Unequal error protection querying policies for the noisy 20 questions problem," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), Calgary, April 2018.
  4. H-.W. Chung, J-.O. Lee, D. Kim, A.O. Hero, "Trade-offs Between Query Difficulty and Sample Complexity in Crowdsourced Data Acquisition," 2018 56th Allerton Conference on Communication, Control, and Computing, Oct 2018.
  5. A. Ghassami, N. Kiyavash, B. Huang, K. Zhang, "Multi-domain causal structure learning in linear systems," In Advances in neural information processing systems, 2018.
  6. S. Liu, P.-Y. Chen, I. Rajapakse, A. Hero, "First-order bifurcation detection for dynamic complex networks," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), Calgary, April 2018.
  7. S. Liu, P.-Y. Chen, J. Chen, and A. Hero, "Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications," AISTATS 2018.
  8. M. Noshad and A. Hero, "Rate-optimal meta learning of classification error," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), Calgary, April 2018.
  9. M. Noshad and A. Hero, "Scalable Hash-Based Estimation of Divergence Measures," AISTATS 2018.
  10. A Prasadan and RR Nadakuditi, "The finite sample performance of dynamic mode decomposition," To appear in IEEE GlobalSIP. pp. 286-290, IEEE, 2018.
  11. G. Schamberg and T.P. Coleman, "Quantifying Context-Dependent Causal Influences", NeurIPS Workshop on Causal Learning, December 2018.
  12. S. Sekeh, B. Oselio, A. Hero, "A Dimension-Independent discriminant between distributions," IEEE Conf on Acoustics, Speech and Signal Processing (ICASSP), Calgary, April 2018.
  13. S. Sekeh, B. Oselio, A. Hero, "Multi-class Bayes error estimation with a global minimal spanning tree," 2018 56th Allerton Conference on Communication, Control, and Computing, Oct 2018.
  1. I. Soloveychik and V. Tarokh, "Asymptotically pseudo-free matrices," to be submitted to IEEE Transactions on Information Theory, 2018.


2019

  1. P.-Y. Chen, C.-C, Tu, P.-S. Ting, Y.-Y. Lo, D. Koutra and A.O. Hero, "Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach," IEEE Trans on Signal and Information Processing over Networks, vol. 5, no. 1, pp. 139-151, Mar. 2019.
  2. K. Greenewald, S. Zhou, A.O. Hero, “The Tensor Graphical Lasso (TeraLasso),” Journal of the Royal Statistical Society, Series B, vol. 81, no. 5 Nov. 2019.
  3. D. A. Mesa, J. Tantiongloc, M. Mendoza, S. Kim, and T. P. Coleman, "A Distributed Framework for the Construction of Transport Maps," Neural Computation, April 2019 (Volume 31, Issue 4).
  4. D. A. Mesa, R. Ma, S. K. Gorantla and T. P. Coleman, "Construction and Analysis of Posterior Matching in Arbitrary Dimensions via Optimal Transport," IEEE Transactions on Information Theory, under review (minor revision), 2019.
  5. A. Prasadan, R. Nadakuditi, "Time Series Source Separation using Dynamic Mode Decomposition", SIAM Journal on Dynamical Systems, 2019. arXiv preprint arXiv:1903.01310,
  6. A. Prasadan, Arvind, Raj Rao Nadakuditi, and Debashis Paul. "Sparse equisigned PCA: Algorithms and performance bounds in the noisy rank-1 setting." Electronic Journal of Statistics 14.1 (2020): 345-385.
  7. A. Jung, A.O. Hero, A. Mara, S. Jahromi, A. Heimowitz, Y.C. Eldar, “Semi-supervised Learning in Network-Structured Data via Total Variation Minimization,” IEEE Trans. on Signal Processing, vol 67, no. 24 6256-6269, Dec. 2019.
  8. Mardia, J., Jiao, J., Tanczos, E., Nowak, R. D., and Weissman, T. “Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types,” Information and Inference: A Journal of the IMA, pp. 1-38, 2019.
  9. B. Moore, S. Ravishankar, R. R. Nadakuditi, and J. Fessler, "Online adaptive image reconstruction (OnAIR) using dictionary models," IEEE Transactions on Computational Imaging, 2019.
  10. B. Moore, C. Gao, R. R. Nadakuditi, "Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video," IEEE Transactions on Computational Imaging, vol. 5(2), pp. 195-211, 2019.
  11. M. Newman, X. Zhang and R. R. Nadakuditi, "Spectra of random networks with arbitrary degrees," Physical Review E, vol. 99(4), 042309, 2019.
  12. U. Oswal, A. Bhargava, and R. Nowak, "Linear Bandits with Feature Feedback," arXiv preprint arXiv:1903.03705 (2019).
  13. A. Prasadan and RR Nadakuditi, "Time Series Source Separation Using Dynamic Mode Decomposition." SIAM Journal on Applied Dynamical Systems 19.2 (2020): 1160-1199.
  14. G. Schamberg and TP Coleman, "Measuring Sample Path Causal Influences with Relative Entropy," IEEE Transactions on Information Theory, under review (minor revision), 2018.
  15. S. Sekeh and A. Hero, “Geometric estimation of multivariate dependency,” Entropy, vol. 21, no. 8, p. 787, Aug 2019. DOI 10.3390/e21040410.
  16. S. Sekeh, M. Noshad, A.O. Hero, “Convergence Rates for Empirical Estimation of Binary Classification Bounds,” Entropy, 21(12), 1144, Dec 2019
  17. Hao Wu and R. R. Nadakuditi, "Free component analysis," Jounral of Machine Learning Research, arXiv preprint arXiv:1905.017132019. Under review.
  18. L. Zhou and A. Hero, "Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information," Entropy, vol. 21, no. 4, p. 410, April 2019.
  1. A. Ghassami, S. Salehkaleybar, N. Kiyavash, K. Zhang, "Counting and sampling from Markov equivalent DAGs using clique trees," In Proceedings of the AAAI Conference on Artificial Intelligence 2019.
  2. B. Jang and A.O. Hero, "Minimum volume topic modeling," AISTATS 2019, Japan. April 2019.
  3. K-S Jun, R Willett, S Wright, and R Nowak, "Bilinear Bandits with Low-rank Structure," In International Conference on Machine Learning, pp. 3163-3172. 2019
  4. V Kosaraju, A Sadeghian, R Martin-Martin, I Reid, H Rezatofighi, S Savarese, "Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks," NeurIPS 2019, http://papers.neurips.cc/paper/8308-social-bigat-multimodal-trajectory-forecasting-using-bicycle-gan-and-graph-attention-networks.pdf.
  5. M. Noshad, Y. Zeng, AO Hero, "Scalable Mutual Information Estimation using Dependence Graphs," IEEE Conf. on Acoustics, Speech and Signal Processing (ICASSP), Brighton 2019.
  6. B. Oselio, A. Sadeghian, S. Savarese, AO Hero, "Time-varying interaction estimation using ensemble methods," IEEE Data Science Workshop, Minneapolis, May 2019.
  7. A. Pokle, R. Martin-Martin, P. Goebel, V. Chow, H. M. Ewald, J. Yang, Z. Wang, A. Sadeghian, D. Sadigh, S. Savarese, and M. Vazquez, "Deep local trajectory replanning and control for robot navigation," in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 58155822.
  8. A Prasadan, A Lodhia and RR Nadakuditi, "Phase transitions in the dynamic mode decomposition algorithm," To appear in IEEE CAMSAP, IEEE, 2019.
  9. A Sadeghian, V Kosaraju, A Sadeghian, N Hirose, H Rezatofighi, and Silvio Savarese, "Sophie: An attentive gan for predicting paths compliant to social and phys- ical constraints," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 13491358.
  10. G. Schamberg and T.P. Coleman, "On the Bias of Directed Information Estimators", IEEE International Symposium on Information Theory (ISIT), July 2019.
  11. S. Sekeh and A.O. Hero, "Feature Selection for multi-labeled variables via Dependency Maximization," IEEE Conf. on Acoustics, Speech and Signal Processing (ICASSP), Brighton 2019.
  12. H. Tiomoko Ali, S. Liu, Y. Yilmaz, AO Hero, R. Couillet, I. Rajapakse, "Latent heterogeneous multilayer community detection," IEEE Conf. on Acoustics, Speech and Signal Processing (ICASSP), Brighton 2019.
  1. R Martin-Martin, H Rezatofighi, A Shenoi, M Patel, J Gwak, N Dass, A Federman, P Goebel, S Savarese, "JRDB: A Dataset and Benchmark for Visual Perception for Navigation in Human Environments," arxiv, 2019, https://arxiv.org/abs/1910.11792.
  2. R. Nowak and E. Tanczos, “Tighter Confidence Intervals for Rating Systems,” arXiv, 2019, arXiv:1912.03528.


2020

  1. G. Schamberg and T.P. Coleman, "Measuring Sample Path Causal Influences with Relative Entropy", IEEE Transactions on Information Theory, Vol. 66, No. 5, May 2020. doi: 10.1109/TIT.2019.2945290
  2. G. Schamberg, W. Chapman, S. P. Xie, and T. P. Coleman, "Direct and Indirect Effects – An Information Theoretic Perspective", Entropy 2020, 22(8), 854; July 2020. doi: 10.3390/e22080854
  3. S. Sekeh, B. Oselio and A. Hero, “Learning to Bound the Multi-class Bayes Error,” IEEE Trans. on Signal Processing, IEEE Trans. on Signal Processing, vol. 68, pp. 3793 – 3807, May 2020. DOI 10.1109/TSP.2020.2994807. https://arxiv.org/abs/1811.06419.
  4. F. Xia, W.B. Shen, C. Li, P. Kasimbeg, M.E. Tchapmi, A. Toshev, R. Martín-Martín, and S. Savarese. "Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments." IEEE Robotics and Automation Letters 5, no. 2 (2020): 713-720.
  5. M. Baranwal, Kunal Garg, Dimitra Panagou, A.O. Hero, “Distributed Fixed-Time Economic Dispatch under Time-Varying Topology and Uncertain Information,” IEEE Control Letters, Vol 5, Issue 4. pp. 1183-1188. Oct 2020. DOI 10.1109/LCSYS.2020.3020248.
  6. J LeBlanc, B Thelen and A Hero, “Testing that a local optimum of the likelihood is globally optimum using reparameterized embeddings,” Journal on Mathematical Imaging and Vision, 2020.
  1. L. Zhou and A. Hero, “Resolution Limits of Non-Adaptive Querying for Noisy 20 Questions Estimation,” IEEE Intl Symposium on Information Theory, Los Angeles, June 21-26, 2020, DOI: 10.1109/ISIT44484.2020.9174277.
  2. N. Charalambides, H. Madhavifar, A. Hero, “Numerically Stable Binary Gradient Coding,” IEEE Intl Symposium on Information Theory (ISIT), Los Angeles, Jan 2020.
  3. A. Magner, M. Baranwal and A. Hero, “The power of graph convolutional networks to distinguish random graph models,” IEEE Intl Symposium on Information Theory, Los Angeles, Jan 2020.
  4. E. Sabeti, P. Song, A. Hero, “Pattern-Based Analysis of Time Series: Estimation,” IEEE Intl Symposium on Information Theory, Los Angeles, Jan 2020.
  5. W. Yu, B. Jang, A. Hero, “The Sylvester Graphical Lasso,” AISTATS, May 2020. arxiv:2002.00288 https://arxiv.org/abs/2002.00288
  6. N. Charalambrides, M. Pilanci, A. Hero, “Weighted gradient coding with leverage score sampling,” IEEE Intl Conference on Acoustics Speech and Signal Processing, May 2020. arxiv:2002.02291 https://arxiv.org/abs/2002.02291.
  7. A. Raj, Y. Bresler, and B. Li, "Improving robustness of deep-learning-based image reconstruction," in 37th International Conference on Machine Learning, ICML, Online Conference, 2020. arXiv preprint arXiv:2002.11821 (2020).
  8. Y. Li and Y. Bresler, “Optimizing Optical Compressed Sensing for Multispectral DNN-Based Image Segmentation,” in Proc. 2020 Asilomar Conf. Signals, Systems, and Computers, Online, Nov. 2020, pp. 636–640. https://doi.org/10.1109/IEEECONF51394.2020.9443293


  1. B. Bordenave, S. Coste, and R.R. Nadakuditi. "Detection thresholds in very sparse matrix completion." arXiv preprint arXiv:2005.06062 (2020).
  2. K. Elkhalil, A. Hasan, J. Ding, S. Farsiu, and V. Tarokh, “Fisher auto-encoders,” arXiv preprint arXiv:2007.06120, 2020.
  3. A Magner, M. Baranwal, A.O. Hero, “Fundamental limitations of deep graph convolutional networks,” arxiv:1910.12954 May 2020.
  4. B. Mason, L. Jain, A. Tripathy, and R. Nowak, “Finding all${\epsilon}$-goodarms in stochastic bandits,”arXiv preprint arXiv:2006.08850, 2020.
  5. M. Malloy, A. Tripathy and R. Nowak, “Optimal Confidence Regions for the Multinomial Parameter,” arXiv, 2020, arXiv:2002.01044.
  6. C. Pérez-D'Arpino, C. Liu, P. Goebel, R. Martín-Martín, S. Savarese, "Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning," arxiv:2010.0860, 2020 https://arxiv.org/abs/2010.08600
  7. M. Soltani, Suya Wu, Jie Ding, and V. Tarokh, “Ped: Pruning with energy distance information,” arXiv preprint arXiv:2007.06121, 2020
  8. B. Tzen and M. Raginsky, ``A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean--Vlasov dynamics," arxiv:2002.01987, 2020.

2021

  1. Wang, Y, C. Hougen, B. Oselio, W. Dempsey, A. Hero, “A geometry-driven longitudinal topic model,” Harvard Data Science Review, April 30 2021.
  2. Robinson, B, R Malinas, Alfred Hero, “Space-Time Adaptive Detection at Low Sample Support,” accepted IEEE Trans, on Signal Processing, April 2021. arxiv.2010.03388.
  3. Noshad, M, J. Choi, Y. Sun, A. Hero, I. Dinov, “A Data Value Metric for Quantifying Information Content and Utility,” accepted Journal of Big Data, Mar. 2021.
  4. Zhou, L, and A. Hero, “Resolution Limits of Noisy 20 Questions Estimation,” IEEE Trans on Information Theory, Vol. 67, Issue 4, April 2021. DOI 10.1109/TIT.2021.3049796.
  5. Yilmaz, Y, M. Aktukmak and A. O. Hero, “Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets Via Generative Models,” in IEEE Transactions on Signal Processing, vol. 69, pp. 5175-5188, 2021.
  6. Moon, K., K. Sricharan, A. Hero, “Ensemble Estimation of Generalized Mutual Information with Applications to Genomics,” IEEE Transactions on Information Theory, vol. 67, no. 9 pp. 5963-5996, 2021. arxiv:1701.08083.
  7. Baranwal, M, K Garg, D Panagou, A.O. Hero, “Distributed Fixed-Time Economic Dispatch under Time-Varying Topology and Uncertain Information,” IEEE Control Letters, vol. 5, no. 4, pp. 1183-1188, Oct. 2021.
  1. Wang, Y, A Hero, “SG-PALM: a Fast Physically Interpretable Tensor Graphical Model,” International Conference on Machine Learning (ICML), July 2021.
  2. Zhou, L, A Hero, “Achievable Resolution Limits for the Noisy Adaptive 20 Questions Problem,” IEEE International Symposium on Information Theory, July 2021.
  3. Zhou, L, A Hero, “Resolution Limits of Non-Adaptive 20 Questions Estimation for Multiple Targets,” IEEE International Symposium on Information Theory, July 2021.
  4. Sabeti, E, PXI Song and AO Hero, “Data discovery using lossless compression-based sparse representation,” IEEE ICASSP 2021. Arxiv:2103.08765 Mar 2021.
  5. Zhou, L and AO Hero, “Resolution Limits of 20 Questions Search Strategies for Moving Targets,” IEEE ICASSP 2021. Arxiv:2103.08097 Mar 2021.
  6. Charalambides, N, M. Palanci, and AO Hero, “Approximate Weighted CR Coded Matrix Multiplication,” IEEE ICASSP 2021. Arxiv:2011.09709 Nov 2020.
  7. Chen, K, J. K. Chen, J. Chuang, M. Vázquez, S. Savarese, “Topological Planning with Transformers for Vision-and-Language Navigation,” IEEE/CVF CVPR 2021. Arxiv:2012.05292 Dec 2020.
  8. Du, SS, W. Hu, Z. Li, R. Shen, Z. Song, J. Wu, “When is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems?.” UAI 2021.
  9. Mohammadreza, S Wu, Suya, Li, Yuerong, Ravier, Robert, Ding, Jie and Tarokh, Vahid, Compressing Deep Networks Using Fisher Score of Feature Maps, 2021 Data Compression Conference (DCC).
  10. Elkhalil, K, A. Hasan, J. Ding, S. Farsiu and V. Tarokh, 2021 International Conference on Artificial Intelligence and Statistics (AISTATS)
  11. Coleman, TP and Maxim Raginsky, "Variational Bayesian inference and conditioned stochastic differential equations," invited paper IEEE Conference on Decision and Control (CDC), 2021.
  12. Zhou, L., Y. Wei, A. Hero, “Second-order asymptotically optimal outlying sequence detection with reject option,” IEEE Information Theory Workshop, Oct 2021.
  13. Hougen, C., L. Kaplan and A. Hero, “Uncertain Bayesian networks: learning from incomplete data,” IEEE Machine Learning for Signal Processing, Gold Coast Australia Oct 2021.
  14. Zhou, Linqi, Yilun Du, and Jiajun Wu. “3D shape generation and completion through point-voxel diffusion." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5826-5835. 2021.
  15. Li, Chengshu, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Elliott Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, and Silvio Savarese. “iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks." In Conference on Robot Learning (CoRL), pp. 455-465. PMLR, 2021.
  16. Srivastava, Sanjana, Chengshu Li, Michael Lingelbach, Roberto Martín-Martín, Fei Xia, Kent Elliott Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, and Li Fei-Fei. “BEHAVIOR: Benchmark for everyday household activities in virtual, interactive, and ecological environments." In Conference on Robot Learning (CoRL), pp. 477-490. PMLR, 2021.
  17. Gao, Ruohan, Yen-Yu Chang, Shivani Mall, Li Fei-Fei, and Jiajun Wu. “ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations." In Conference on Robot Learning (CoRL), pp. 466-476. PMLR, 2021.
  18. Elkhalil, K, A. Hasan, J. Ding, S. Farsiu, and V. Tarokh, “Fisher auto-encoders,” The International Conference on Artificial Intelligence and Statistics, vol. 130, pp. 352–360, 2021.
  19. Soltani, Mohammadreza, Suya Wu, Yuerong Li, Robert Ravier, Jie Ding, and Vahid Tarokh, “Compressing deep networks using fisher score of feature maps,” in 2021 Data Compression Conference (DCC). IEEE, 2021, pp. 371–371.
  1. Le, C.P, M. Soltani, R. Ravier, T. Standley, S. Savarese, V. Tarokh, "Neural Architecture Search From Fr\'echet Task Distance," Arxiv:2103.12827 Mar 2021.
  2. Iyer, Kritika and Najarian, Cyrus P and Fattah, Aya A and Arthurs, Christopher J and Soroushmehr, SM Reza and Subban, Vijayakumar and Sankardas, Mullasari A and Nadakuditi, Raj R and Nallamothu, Brahmajee K and Figueroa, C Alberto, "AngioNet: A Convolutional Neural Network for Vessel Segmentation in X-ray Angiography", https://doi.org/10.1101/2021.01.25.21250488, 2021
  3. Le, C.P., Soltani, Mohammadreza, Ravier, Robert, Standley, Trevor, Savarese, Silvio, Tarokh, Vahid, Neural Architecture Search From Fr$\backslash$'echet Task Distance, submitted.
  4. Tarzanagh, D, L Balzano, A Hero, “Fair structure learning in heterogeneous graphical models,” arxiv:2112.05128, 9 Dec 2021. Submitted
  5. Robinson, R, R. Malinas and A. Hero, “High-Dimensional Covariance Shrinkage for Signal Detection,” arxiv:2103.11830. 3 Dec 2021. Submitted.


2022

  1. Hero, A. B. Rajaratnam and Y. Wei, “A Unified Framework for Correlation Mining in Ultra-High Dimension,” arxiv:2101.04715. To appear in IEEE Trans. on Information Theory 2022.
  2. E Sabeti, S Oh, PX Song, A Hero. “A Pattern Dictionary Method for Anomaly Detection,” Entropy, vol 24, pp. 1095 Aug 2022.
  3. Magner, A, M Baranwal, A. Hero, “Fundamental limitations of deep graph convolutional networks,” the IEEE Trans. on Information Theory, vol 68, no 5, pp. 3218-3283, May 2022.
  4. Zhou, L, Y Wei, A Hero, “Second order asymptotically optimal outler hypothesis testing,” IEEE Trans. on Information Theory, vol. 68, no 6, June 2022.
  5. Wang, Y, Z. Sun, D. Song, A. Hero, “TensorGraphicalModels: A Julia Toolbox for Multiway Covariance Models and Ensemble Kalman Filter,” Software Impacts, May 14, 2022
  6. Le, Cat Phuoc, Mohammadreza Soltani, Juncheng Dong, and Vahid Tarokh, “Fisher task distance and its application in neural architecture search,” IEEE Access, vol. 10, pp. 1–1, 01 2022.
  7. Agrusa, A. S., Kunkel, D. C., Coleman, T., "Robust Regression and Optimal Transport Methods to Predict Gastrointestinal Disease Etiology from High Resolution EGG and Symptom Severity," IEEE Transactions on Biomedical Engineering, 2022
  8. Bordenave, Charles, Simon Coste, Raj Rao Nadakuditi, "Detection thresholds in very sparse matrix completion," Foundations of Computational Mathematics, 2022.


  1. Zhou, L Y Wei, A Hero, “Asymptotics for Outlier Hypothesis Testing ,” IEEE Intl Symposium on Information Theory (ISIT), Aalto Finland, June 2022.
  2. Charalambides, N, H Madhavifar, M Pilanci, A Hero, “Orthonormal Sketches for Secure Coded Regression,” IEEE Intl Symposium on Information Theory (ISIT), Aalto Finland, June 2022.
  3. Hougen, C, L Kaplan, M Ivanovska, F Cerutti, KV Mishra and A Hero, “SOLBP: Second-Order Loopy Belief Propagation for Inferencing in Uncertain Bayesian Networks,” 25th Conference on Information Fusion, Linkoping Sweden, July 2022.
  4. Jang, B, J. Nepper, M. Chevrette, Jo Handelsman and A.O. Hero, “High Dimensional Stochastic Linear Contextual Bandit with Missing Covariates,” IEEE Workshop on Machine Learning for Signal Processing (MLSP) Xian China, Aug. 2022.
  5. Robinson, B, R Malinas, V Latimer, B Morrison, A Hero, “An improvement on the Hotelling T2 test using the Ledoit-Wolf non-linear shrinkage estimator,” 30th European Conference on Signal Processing (EUSIPCO), Belgrade Serbia, to appear Sept. 2022.
  6. Wang, R, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng, and Jiajun Wu. “Translating a Visual LEGO Manual to a Machine-Executable Plan." In European Conference on Computer Vision (ECCV), 2022.
  7. Meng, C, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. “SDEdit: Guided image synthesis and editing with stochastic differential equations." In International Conference on Learning Representations (ICLR). 2022.
  8. Mukherjee, Subhojyoti, Ardhendu S Tripathy, and Robert Nowak, “Chernoff sampling for active testing and extension to active regression,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2022, pp. 7384–7432.
  9. Zhu, Yinglun, and Robert Nowak, “Pareto optimal model selection in linear bandits,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2022, pp. 6793–6813.
  10. Le, Cat Phuoc, Juncheng Dong, Mohammadreza Soltani, and Vahid Tarokh, “Task affinity with maximum bipartite matching in few-shot learning,” in International Conference on Learning Representations, 2022.
  11. Habi, HV, H. Messer, and Y. Bresler, “A Generative Cramér-Rao Bound on Frequency Estimation with Learned Measurement Distribution,” in 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM), Jun. 2022, pp. 176–180. https://doi.org/10.1109/SAM53842.2022.9827830
  12. Coleman T.P. and Maxim Raginsky, “Variational bayesian inference and conditioned

stochastic differential equations,” in Proceedings of the IEEE Conference on Decision and Control, 2021.

Personal tools
Namespaces
Variants
Actions
Navigation
Toolbox
EECS @ UM
Tools