Kejun Huang – Publications

  • J. Hu and K. Huang, “Global Identifiability of ell_1-based Dictionary Learning via Matrix Volume Optimization”, in Advances in Neural Information Processing Systems (NeurIPS), 2023, New Orleans, LA.

  • A. Bumin, K. Huang, and T. Kahveci, “PartialFibers: An efficient method for predicting Drug-Drug Interactions”, in International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2023, Norman, OK.

  • A. Bumin, K. Huang, and T. Kahveci, “Vulture: VULnerabilities in impuTing drUg REsistance”, in ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2023, Houston, TX. [doi]

  • J. Hu and K. Huang, “Identifiable Bounded Component Analysis via Minimum Volume Enclosing Parallelotope”, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), 2023, Rhodes Island, Greece. [doi]

  • Y. Sun and K. Huang, “Volume-regularized Nonnegative Tucker Decomposition with Identifiability Guarantees”, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), 2023, Rhodes Island, Greece. [doi]

  • D. A. Shifman, I. Cohen, K. Huang, X. Xian, G. Singer, “An adaptive machine learning algorithm for the resource-constrained classification problem”, Engineering Applications of Artificial Intelligence, 119:105741, 2023. [doi]

  • A. Bumin and K. Huang, “Stochastic Douglas-Rachford Splitting for Regularized Empirical Risk Minimization: Convergence, Mini-batch, and Implementation”, Transactions on Machine Learning Research, 2022. [doi]

  • A. Bumin, A. Ritz, D. Slonim, T. Kahveci, and K. Huang, “FiT: Fiber-based Tensor Completion for Drug Repurposing”, in ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2022, Chicago, IL. (SIGBio ACM-BCB Best Student Paper Award) [doi]

  • Y. Ren, A. Sarkar, A. Bumin, K. Huang, P. Veltri, A. Dobra, and T. Kahveci, “Identification of Co-existing Embeddings of A Motif in Multilayer Networks”, in ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2022, Chicago, IL. [doi]

  • Y. Sun and K. Huang, “HOQRI: Higher-order QR Iteration for Scalable Tucker Decomposition”, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), 2022, Singapore. [doi]

  • A. Bumin and K. Huang, “Efficient Implementation of Stochastic Proximal Point Algorithm for Matrix and Tensor Completion”, in European Signal Processing Conference (EUSIPCO), 2021, Virtual. [doi]

  • S. Lu, M. Razaviyayn, B. Yang, K. Huang, and M. Hong, “Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems”, in Advances in Neural Information Processing Systems (NeurIPS), 2020, Virtual. [doi]

  • X. Fu, N. Vervliet, L. De Lathauwer, K. Huang, and N. Gillis, “Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective”, IEEE Signal Processing Magazine, 37(5):78–94, 2020. [doi]

  • B. Yang, X. Fu, N. D. Sidiropoulos, and K. Huang, “Learning Nonlinear Mixtures: Identifiability and Algorithm”, IEEE Transactions on Signal Processing, 68:2857-2869, 2020. [doi]

    • Part of the results appears in
      B. Yang, X. Fu, N. D. Sidiropoulos, and K. Huang, “Unsupervised Learning of Nonlinear Mixtures: Identifiability and Algorithm”, in Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2019, Pacific Grove, CA. [doi]

  • X. Fu, S. Ibrahim, H.-T. Wai, C. Gao, and K. Huang, “Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization”, IEEE Transactions on Signal Processing, 60:2170-2185, 2020. [doi]

    • Part of the results appears in
      X. Fu, C. Gao, H.-T. Wai, and K. Huang, “Block-Randomized Stochastic Proximal Gradient for Constrained Low-Rank Tensor Factorization”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019, Brighton, UK. [doi]

  • K. Huang and X. Fu, “Low-complexity Levenberg-Marquardt Algorithm for Tensor Canonical Polyadic Decomposition”, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), 2020, Barcelona, Spain. [doi]

  • B. Yang, K. Huang, and N. D. Sidiropoulos, “Identifying Potential Investors with Data Driven Approaches”, in SIAM International Conference on Data Mining (SDM), 2020, Cincinnati, OH. [doi]

  • G. Zhang, X. Fu, K. Huang, and J. Wang, “Hyperspectral Super-Resolution: A Coupled Nonnegative Block-Term Tensor Decomposition Approach”, in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019, Guadeloupe, West Indies. [doi]

  • S. Ibrahim, X. Fu, N. Kargas, and K. Huang, “Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms”, in Advances in Neural Information Processing Systems (NIPS), 2019, Vancouver, Canada. [doi]

  • K. Huang and X. Fu, “Low-complexity Proximal Gauss-Newton Algorithm for Nonnegative Matrix Factorization”, in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019, Ottawa, Canada. [doi]

  • X. Fu and K. Huang, “Block-Term Tensor Decomposition Via Constrained Matrix Factorization”, in IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2019, Pittsburgh, PA. [doi]

  • K. Huang and X. Fu, “Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm”, in International Conference on Machine Learning (ICML), 2019, Long Beach, CA. [doi]

  • K. Huang, Z. Yang, Z. Wang, and M. Hong, “Learning Partially Observable Markov Decision Processes using Coupled Canonical Polyadic Decomposition”, in IEEE Data Science Workshop (DSW), 2019, Minneapolis, MN. [doi]

  • S. Lu, Z. Zhao, K. Huang, and M. Hong, “Perturbed Projected Gradient Descent Converges to Approximate Second-Order Points for Bound Constrained Nonconvex Problems”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019, Brighton, UK. [doi]

  • X. Fu, K. Huang, E. E. Papalexakis, H. Song, P. P. Talukdar, N. D. Sidiropoulos, C. Faloutsos, and T. Mitchell, “Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data”, IEEE Transactions on Knowledge and Data Engineering, 31(12):2304–2318, Dec. 2019. [doi]

    • Part of the results appears in
      X. Fu, K. Huang, E. E. Papalexakis, H. Song, P. P. Talukdar, N. D. Sidiropoulos, C. Faloutsos, and T. Mitchell, “Efficient and Distributed Algorithms for Large-Scale Generalized Canonical Correlations Analysis”, in IEEE International Conference on Data Mining (ICDM), 2016, Barcelona, Spain. [doi]

  • X. Fu*, K. Huang*, N. D. Sidiropoulos, Q. Shi, and M. Hong, “Anchor-Free Correlated Topic Modeling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5):1056–1071, 2019. [doi]

    • Part of the results appears in
      K. Huang*, X. Fu*, and N. D. Sidiropoulos, “Anchor-free Correlated Topic Modeling: Identifiability and Algorithm”, in Advances in Neural Information Processing Systems (NIPS), 2016, Barcelona, Spain. [doi]

  • X. Fu, K. Huang, N. D. Sidiropoulos, and W.-K. Ma, “Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications”, IEEE Signal Processing Magazine, 36(2):59–80, 2019. [doi]

  • K. Huang, X. Fu, and N. D. Sidiropoulos, “Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling”, in International Conference on Machine Learning (ICML), 2018, Stockholm, Sweden. [doi]

  • S. Smith*, K. Huang*, N. D. Sidiropoulos, and G. Karypis, “Streaming Tensor Factorization for Infinite Data Sources”, in SIAM International Conference on Data Mining (SDM), 2018, San Diego, CA. [doi]

  • X. Fu*, K. Huang*, and N. D. Sidiropoulos, “On Identifiability of Nonnegative Matrix Factorization”, IEEE Signal Processing Letters, 25(3): 328–332, 2018. [doi]

  • K. Huang, X. Fu, and N. D. Sidiropoulos, “On Convergence of Epanechnikov Mean Shift”, in AAAI Conference on Artificial Intelligence (AAAI), 2018, New Orleans, LA. [doi]

  • A. P. Liavas, G. Kostouloas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based Alternating Optimization for Nonnegative Tensor Factorization: Algorithm and Parallel Implementations”, IEEE Transactions on Signal Processing, 66(4): 944–953, 2018 [doi]

    • Part of the results appears in
      A. P. Liavas, G. Kostouloas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based Parallel Algorithm for Large-scale Nonnegative Tensor Factorization”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017, New Orleans, LA. [doi]

  • K. Huang and N. D. Sidiropoulos, “Kullback-Leibler Principal Component for Tensors is not NP-hard”, in Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2017, Pacific Grove, CA. [doi]

  • X. Fu, K. Huang, O. Stretcu, H. Song, E. E. Papalexakis, P. P. Talukdar, T. Mitchell, N. D. Sidiropoulos, C. Faloutsos, and B. Pozcos, “BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals” in SIAM International Conference on Data Mining (SDM), 2017, Houston, TX. [doi]

  • K. Huang and Y. C. Eldar, “Phase Retrieval using a Conjugate Symmetric Reference”, in International Conference on Sampling Theory and Applications (SampTA), 2017, Tallinn, Estonia. [doi]

  • X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A. M. C. So, “Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis”, IEEE Transactions on Signal Processing, 65(16):4150–4165, 2017. [doi]

    • Part of the results appears in
      X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A. M. C. So, “Scalable and Flexible MAX-VAR Generalized Canonical Correlation Analysis via Alternating Optimization”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, New Orleans, LA. [doi]

  • N. D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, and C. Faloutsos, “Tensor Decomposition for Signal Processing and Machine Learning”, IEEE Transactions on Signal Processing, 65(13): 3551–3582, 2017. [doi] (2022 IEEE SPS Donald G. Fink Overview Paper Award)

  • X. Fu, K. Huang, B. Yang, W.-K. Ma, and N. D. Sidiropoulos, “Robust Volume Minimization-based Matrix Factorization for Remote Sensing and Document Clustering”, IEEE Transactions on Signal Processing, 64(23): 6254–6268, 2016. [doi]

    • Part of the results appears in
      X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, “Robust Volume Minimization-based Matrix Factorization via Alternating Optimization”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China. [doi]

  • K. Huang, Y. C. Eldar, and N. D. Sidiropoulos, “Phase Retrieval from 1D Fourier Measurements: Convexity, Uniqueness, and Algorithms”, IEEE Transactions on Signal Processing, 64(23): 6105–6117, 2016. [doi]

    • Part of the results appears in
      K. Huang, Y. C. Eldar, and N. D. Sidiropoulos, “On Convexity and Identifiability in 1-D Fourier Phase Retrieval”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China. [doi]

  • K. Huang and N. D. Sidiropoulos, “Consensus-ADMM for General Quadratically Constrained Quadratic Programming”, IEEE Transactions on Signal Processing, 64(20): 5297–5310, 2016. [doi]

  • C. Qian, N. D. Sidiropoulos, K. Huang, L. Huang, and H.-C. So, “Phase Retrieval Using Feasible Point Pursuit: Algorithms and Cramer-Rao Bound”, IEEE Transactions on Signal Processing, 64(20): 5282–5296, 2016. [doi]

    • Part of the results appears in
      C. Qian, N. D. Sidiropoulos, K. Huang, L. Huang, and H.-C. So, “Least Squares Phase Retrieval Using Feasible Point Pursuit”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China. [doi]

  • K. Huang, N. D. Sidiropoulos, and A. P. Liavas, “A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization”, IEEE Transactions on Signal Processing, 64(19): 5052–5065, 2016. [doi] [code]

    • Part of the results appears in
      K. Huang, N. D. Sidiropoulos, and A. P. Liavas, “Efficient Algorithms for ‘Universally’ Constrained Matrix and Tensor Factorization”, in European Signal Processing Conference (EUSIPCO), 2015, Nice, France. [doi]

  • X. Fu, K. Huang, W.-K. Ma, N. D. Sidiropoulos, and R. Bro, “Joint Tensor Factorization and Outlying Slab Suppression with Applications”, IEEE Transactions on Signal Processing, 63(23): 6315–6328, 2015. [doi]

  • M. Gardner*, K. Huang*, E. E. Papalexakis, X. Fu, P. P. Talukdar, C. Faloutsos, N. D. Sidiropoulos, and T. Mitchell, “Translation Invariant Word Embeddings”, in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015, Lisbon, Portugal. [doi] [code]

  • O. Mehanna, K. Huang, B. Gopalakrishnan, A. Konar, and N. D. Sidiropoulos, “Feasible Point Pursuit and Successive Approximation of Non-convex QCQPs”, IEEE Signal Processing Letters, 22(7): 804–808, 2015. [doi]

  • X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, “Blind Separation of Quasi-stationary Sources: Exploiting Convex Geometry in Covariance Domain”, IEEE Transactions on Signal Processing, 63(9): 2306–2320, 2015. [doi]

  • K. Huang, N. D. Sidiropoulos, E. E. Papalexakis, C. Faloutsos, P. P. Talukdar, and T. Mitchell, “Principled Neuro-Functional Connectivity Discovery”, in SIAM International Conference on Data Mining (SDM), 2015, Vancouver, Canada. [doi]

  • K. Huang and N. D. Sidiropoulos, “Putting NMF to the Test: A Tutorial Derivation of Pertinent Cramer-Rao Bounds and Performance Benchmarking”, IEEE Signal Processing Magazine, 31(3):76–86, 2014. [doi]

  • K. Huang, N. D. Sidiropoulos, and A. Swami, “Nonnegative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition”, IEEE Transactions on Signal Processing, 62(1): 211–224, 2014. [doi]

    • Part of the results appears in
      K. Huang, N. D. Sidiropoulos, and A. Swami, “NMF Revisited: New Uniqueness Results and Algorithms”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013, Vancouver, Canada. [doi]