Alexander Cloninger

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Department of Mathematics,
Halıcıoğlu Data Science Institute
University of California at San Diego
Office: AP&M 5747
Email: acloninger (at) ucsd (dot) edu

Research Areas

Note: a number of these papers cross multiple areas listed below, but are only listed in primary location.

Mathematical Analysis of Deep Learning

  • “Nonclosedness of the Set of Neural Networks in Sobolev Space.” Neural Networks, 2021. (with Scott Mahan, Emily J. King)

  • “A deep network construction that adapts to intrinsic dimensionality beyond the domain.” Neural Networks, 2021. (with Timo Klock)

  • “Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks.” International Conference on Machine Learning (ICML), 2020. (with Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Hadi Esmaeilzadeh)

  • “Variational Random Walk Autoencoders.” The Eurpoean Conference on Computer Vision (ECCV), 2020. (with Henry Li, Ofir Lindenbaum, Xiuyuan Cheng)

  • “PT-MMD: A novel statistical framework for the evaluation of generative systems.” Asilomar Conference on Signals, Systems, and Computers, 2019. (with Alexander Potapov, Ian Colbert, Dhiman Sengupta, Ken Kreutz-Delgado, Srinjoy Das)

  • “Provable approximation properties for deep neural networks.” Applied and Computational Harmonic Analysis, 2017. (with Uri Shaham, Raphy Coifman)

  • “Diffusion Nets.” Applied and Computational Harmonic Analysis, 2017. (with Gal Mishne, Uri Shaham, Israel Cohen)

  • “Bigeometric organization of deep nets.” Applied and Computational Harmonic Analysis, 2017. (with Nicholas Downing, Harlan Krumholz, Raphy Coifman)

Distances and Learning on Distributions

  • “Supervised learning of sheared distributions using linearized optimal transport.” Arxiv: 2201.10590, 2022. (with Varun Khurana, Harish Kannan, Caroline Moosmueller)

  • “Kernel distance measures for time series, random fields and other structured data.” Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 2021. (with Srinjoy Das, Hrushikesh Mhaskar)

  • “Cautious Active Clustering.” Applied and Computational Harmonic Analysis, 2021. (with Hrushikesh Mhaskar)

  • “Linear Optimal Transport Embedding: Provable fast Wasserstein distance computation and classification for nonlinear problems.” Arxiv: 2008.09165, 2020. (with Caroline Moosmueller)

  • “Classification Logit Two-sample Testing by Neural Networks.” Arxiv: 1909.11298, 2020. (with Xiuyuan Cheng)

  • “A witness function based construction of discriminative models using Hermite polynomials.” Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 2020. (with Hrushikesh Mhaskar, Xiuyuan Cheng)

  • “Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy.” Arxiv: 1812.04594, 2018.

  • “Two-sample statistics based on anisotropic kernels.” Information and Inference, 2019. (with Xiuyuan Cheng, Raphy Coifman)

  • “People mover's distance: Class level geometry using fast pairwise data adaptive transportation costs.” Applied and Computational Harmonic Analysis, 2018. (with Brita Roy, Carley Riley, Harlan Krumholz)

Geometric Data Analysis

  • “Structure from Voltage.” Arxiv: 2203.00063, 2022. (with Robi Bhattacharjee, Yoav Freund)

  • “StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm.” ArXiv: 2108.10411, 2021. (with Andreas Oslandsbotn, Zeljko Kereta, Valeriya Naumova, Yoav Freund)

  • “Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features.” Arxiv: 2106.02614, 2021. (with Jinjie Zhang, Rayan Saab)

  • “LDLE: Low Distortion Local Eigenmaps.” Journal of Machine Learning Research, 2021. (with Dhruv Kohli, Gal Mishne)

  • “A low discrepancy sequence on graphs.” Journal of Fourier Analysis and Applications, 2021. (with Hrushikesh Mhaskar)

  • “Natural Graph Wavelet Packet Dictionaries.” Journal of Fourier Analysis and Applications, 2020. (with Haotian Li, Naoki Saito)

  • “Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples.” Proceedings of Uncertainty in Artificial Intelligence (UAI), 2020. (with Saeed Vahidian, Baharan Mirzasoleiman)

  • “On the Dual Geometry of Laplacian Eigenfunctions.” Experimental Mathematics, 2019. (with Stefan Steinerberger)

  • “The pre-image problem for Laplacian Eigenmaps utilizing L 1 regularization with applications to data fusion.” Inverse Problems, 2017. (with Tim Doster, Wojciech Czaja)

  • “Prediction models for graph-linked data with localized regression.” SPIE, 2017.

  • “A note on Markov normalized magnetic eigenmaps.” Applied and Computational Harmonic Analysis, 2017.

  • “Spectral echolocation via the wave embedding.” Applied and Computational Harmonic Analysis, 2017. (with Stefan Steinerberger)

  • “Function Driven Diffusion for Personalized Counterfactual Inference.” Arxiv: 1610.10025, 2017.

  • “Eigenvector localization on data-dependent graphs.” International Conference on Sampling Theory and Applications (SampTA), 2015. (with Wojciech Czaja)

  • “Multimodal Data Fusion via Graph-and Operator-based Techniques.” Hyperspectral Imaging and Sounding of the Environment, 2015. (with Tim Doster, Wojciech Czaja)

  • “Operator analysis and diffusion based embeddings for heterogeneous data fusion.” IEEE Geoscience and Remote Sensing Symposium, 2014. (with Tim Doster, Wojciech Czaja)

  • “Operator based integration of information in multimodal radiological search mission with applications to anomaly detection.” SPIE, 2014. (with John Benedetto, Wojciech Czaja, Tim Doster, Kevin Kochersberger, Ben Manning, Tom McCullough, Lance McLane)

  • “LIDAR image recovery by incorporating heterogeneous imaging modalities.” SPIE, 2014. (with Wojciech Czaja)

  • “A case study on data fusion with overlapping segments.” IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2013. (with Tim Doster, Wojciech Czaja)


  • “Using Neural Networks to Predict Micro-Spatial Economic Growth.” National Bureau of Economic Research, 2021. (with Arman Khachiyan, Anthony Thomas, Huye Zhou, Gordon H Hanson, Tajana Rosing, Amit Khandelwal)

  • “A Manifold Learning Based Video Prediction Approach for Deep Motion Transfer.” International Conference on Computer Vision (ICCV), 2021. (with Yuliang Cai, Sumit Mohan, Adithya Niranjan, Nilesh Jain, Srinjoy Das)

  • “DeepSurv: Personalized treatment recommender system using A Cox proportional hazards deep neural network.” Biomedical Research Methodology, 2018. (with Jared Katzman, Uri Shaham, Jonathan Bates, Tingting Jiang, Yuval Kluger)

  • “Outcome Based Matching.” Arxiv: 1712.05063, 2018. (with Jonathan Bates)

  • “Describing the performance of US hospitals by applying big data analytics.” PloS One, 2017. (with Nicholas Downing, Arjun Venkatesh, Angela Hsieh, Elizabeth Drye, Raphy Coifman, Harlan Krumholz)

  • “2D sparse sampling algorithm for ND Fredholm equations with applications to NMR relaxometry.” International Conference on Sampling Theory and Applications (SampTA), 2015. (with Ariel Hafftka, Hasan Celik, Wojciech Czaja, Richard Spencer)

  • “Efficient 2D MRI relaxometry using compressed sensing.” Journal of Magnetic Resonance, 2015. (with Ruiliang Bai, Wojciech Czaja, Peter Basser)

  • “Hildreth’s algorithm with applications to soft constraints for user interface layout.” Journal of Computational and Applied Mathematics, 2015. (with Noreen Jamil, Xuemei Chen)

  • “Solving 2D Fredholm integral from incomplete measurements using compressive sensing.” SIAM Journal on Imaging Sciences, 2014. (with Ruiliang Bai, Peter Basser, Wojciech Czaja)

  • “A general formula for reactant conversion over a single catalyst particle in TAP pulse experiments.” Chemical Engineering Science, 2009. (with Renato Feres, Grigoriy Yablonsky, John Gleaves)


  • “Magnetic resonance 2D relaxometry reconstruction using partial data.” US Patent No. 10802098, 2020. (with Peter J Basser, Ruiliang Bai, Wojciech Czaja)

  • “On suprema of autoconvolutions with an application to Sidon sets.” Proceedings of the American Mathematical Society, 2017. (with Stefan Steinerberger)