Publications

Selected Publications from Machine Learning Faculty. For more detailed information, please visit specific faculty Google Scholar sites.

2017

Bhargava, Aniruddha; Ganti, Ravi; Nowak, Rob; “Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications” Artificial Intelligence and Statistics (2017): 1349-1357

Pimentel-Alarcùn, Daniel; Nowak, Robert; “Random Consensus Robust PCA” Artificial Intelligence and Statistics (2017): 344-352

Ongie, Greg; Willett, Rebecca; Nowak, Robert D; Balzano, Laura; “Algebraic Variety Models for High-Rank Matrix Completion” arXiv preprint arXiv:1703.09631 (2017):

Alfeld, Scott; Zhu, Xiaojin; Barford, Paul; “Explicit Defense Actions Against Test-Set Attacks” (2017):

Gutierrez-Barragan, Felipe; Ithapu, Vamsi K; Hinrichs, Chris; Maumet, Camille; Johnson, Sterling C; Nichols, Thomas E; Singh, Vikas; “Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM” arXiv preprint arXiv:1703.01506 (2017):

Garcia, Kevin; Chasman, Deborah; Roy, Sushmita; Ane, Jean-Michel; “Physiological responses and gene co-expression network of mycorrhizal roots under K+ deprivation” Plant Physiology (2017): pp. 01959.2016

Rohe, Karl; Tao, Jun; Han, Xintian; Binkiewicz, Norbert; “A note on quickly sampling a sparse matrix with low rank expectation” arXiv preprint arXiv:1703.02998 (2017):

Le, Thu; Bolt, Daniel; Camburn, Eric; Goff, Peter; Rohe, Karl; “Latent Factors in Student╨Teacher Interaction Factor Analysis” Journal of Educational and Behavioral Statistics (2017): 1.077E+15

2016

Cho, Juhee; Kim, Donggyu; Rohe, Karl; “Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute” arXiv preprint arXiv:1605.02138 (2016):

Shavlik, Jude W; Richard Maclin; “ENRICHING VOCABULARIES BY GENERALIZING EXPLANATIONSTRUCTURES” Machine Learning Proceedings 1989 (2016): 444

Guan, Peng; Raginsky, Maxim; Willett, Rebecca; Zois, Daphney-Stavroula; “Regret minimization algorithms for single-controller zero-sum stochastic games” Decision and Control(CDC), 2016 IEEE 55th Conference on (2016): 7075-7080

Jun, Kwang-Sung; Orabona, Francesco; Willett, Rebecca; Wright, Stephen; “Improved Strongly Adaptive Online Learning using Coin Betting” arXiv preprint arXiv:1610.04578 (2016):

Marais, Willem J; Holz, Robert E; Hu, Yu Hen; Kuehn, Ralph E; Eloranta, Edwin E; Willett, Rebecca M; “Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations” Applied optics 55 29(2016): 8316-8334

Jiang, Xin; Willett, Rebecca; “Online Data Thinning via Multi-Subspace Tracking” arXiv preprint arXiv:1609.03544 (2016):

Chitalia, Rhea; Mueller, Jenna; Fu, Henry L; Whitley, Melodi Javid; Kirsch, David G; Brown, J Quincy; Willett, Rebecca; Ramanujam, Nimmi; “Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes” Biomedical Optics Express 7 9(2016): 3412-3424

Hall, Eric C; Willett, Rebecca M; “Tracking dynamic point processes on networks” IEEE Transactions on Information Theory 62 7(2016): 4327-4346

Zhang, Chenyu; Oh, Albert; Yankovich, Andrew; Slater, Thomas; Haigh, Sarah; Willett, Rebecca; Voyles, Paul M; “Combining Non-Rigid Registration with Non-Local Principle Component Analysis for Atomic Resolution EDS Mapping” Microscopy and Microanalysis 22 S3(2016): 1406-1407

Mueller, Jenna L; Gallagher, Jennifer E; Chitalia, Rhea; Krieger, Marlee; Erkanli, Alaattin; Willett, Rebecca M; Geradts, Joseph; Ramanujam, Nimmi; “Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting” Journal of cancer research and clinical oncology 142 7(2016): 1475-1486

Hall, Eric C; Raskutti, Garvesh; Willett, Rebecca; “Inferring high-dimensional poisson autoregressive models” Statistical Signal Processing Workshop(SSP), 2016 IEEE (2016): 5-Jan

Jiang, Xin; Reynaud-Bouret, Patricia; Rivoirard, Vincent; Sansonnet, Laure; Willett, Rebecca; “Genomic transcription regulatory element location analysis via poisson weighted lasso” Statistical Signal Processing Workshop(SSP), 2016 IEEE (2016): 5-Jan

Marais, Willem J; Holz, Robert E; Hu, Yu Hen; Kuehn, Ralph E; Eloranta, Edwin E; Willett, Rebecca M; “A New Approach to Inverting Backscatter and Extinction from Photon-Limited Lidar Observations” arXiv preprint arXiv:1603.02075 (2016):

Willett, Rebecca; “The dark side of image reconstruction: Emerging methods for photon-limited imaging” (2016):

Yang, Jing; Draper, Stark C; Nowak, Robert; “Learning the interference graph of a wireless network” IEEE Transactions on Signal and Information Processing over Networks (2016):

Jun, Kwang-Sung; Nowak, Robert; “Graph-Based Active Learning: A New Look at Expected Error Minimization” arXiv preprint arXiv:1609.00845 (2016):

Pimentel-Alarcùn, Daniel L; Nowak, Robert D; “A converse to low-rank matrix completion” Information Theory(ISIT), 2016 IEEE International Symposium on (2016): 96-100

Pimentel-Alarcùn, D; Balzano, L; Marcia, R; Nowak, R; Willett, R; “Group-sparse subspace clustering with missing data” Statistical Signal Processing Workshop(SSP), 2016 IEEE (2016): 5-Jan

Jun, Kwang-Sung; Nowak, Robert; “Anytime exploration for multi-armed bandits using confidence information” Proceedings of The 33rd International Conference on Machine Learning (2016): 974-982

Pimentel-Alarcùn, Daniel L; Boston, Nigel; Nowak, Robert D; “A characterization of deterministic sampling patterns for low-rank matrix completion” IEEE Journal of Selected Topics in Signal Processing 10 4(2016): 623-636

Bhargava, Aniruddha; Ganti, Ravi Sastry; Nowak, Robert; “Bandit Approaches to Preference Learning Problems with Multiple Populations” stat 1050(2016): 14

Bhargava, Aniruddha; Ganti, Ravi; Nowak, Robert; “Active Algorithms For Preference Learning Problems with Multiple Populations” arXiv preprint arXiv:1603.04118 (2016):

Rao, Nikhil; Ganti, Ravi; Balzano, Laura; Willett, Rebecca; Nowak, Robert; “On Learning High Dimensional Structured Single Index Models” arXiv preprint arXiv:1603.03980 (2016):

Pimentel-Alarcùn, Daniel; Balzano, Laura; Nowak, Robert; “Necessary and sufficient conditions for sketched subspace clustering” Allerton Conference on Communication, Control, and Computing (2016):

Rau, Martina A; Mason, Blake; Nowak, Robert; “How to model implicit knowledge? Similarity learning methods to assess perceptions of visual representations” Proceedings of the 9th International Conference on Educational Data Mining (2016):

Jain, Lalit; Jamieson, Kevin G; Nowak, Rob; “Finite Sample Prediction and Recovery Bounds for Ordinal Embedding” Advances In Neural Information Processing Systems (2016): 2703-2711

Smith, Brandon M; Wang, Xuan; Hu, Yu Hen; Dyer, Charles R; Chitturi, Madhav V; Lee, John D; “Automatic Driver Face State Estimation in Challenging Naturalistic Driving Videos 2” Transportation Research Board 95th Annual Meeting, Washington, DC (2016):

Tong, Jianfei; Xie, Wei; Hu, Yu-Hen; Bao, Ming; Li, Xiaodong; He, Wei; “Estimation of low-altitude moving target trajectory using single acoustic array” The Journal of the Acoustical Society of America 139 4(2016): 1848-1858

Gitter, Anthony; Huang, Furong; Valluvan, Ragupathyraj; Fraenkel, Ernest; Anandkumar, Animashree; “Unsupervised learning of transcriptional regulatory networks via latent tree graphical models” arXiv preprint arXiv:1609.06335 (2016):

Tuncbag, Nurcan; Gosline, Sara JC; Kedaigle, Amanda; Soltis, Anthony R; Gitter, Anthony; Fraenkel, Ernest; “Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package” PLoS Comput Biol 12 4(2016): e1004879

Viswanathan, Dileep; Soni, Ameet; Shavlik, Jude; Natarajan, Sriraam; “Learning Relational Dependency Networks for Relation Extraction” arXiv preprint arXiv:1607.00424 (2016):

Shavlik, Jude W; “An empirical analysis of ebl approaches for learning plan schemata” Proceedings of the sixth international workshop on Machine learning (2016): 183-187

Geoffrey G Towell; Jude W Shavlik; “Combining explanation-based learning and artificial neural networks” Machine Learning Proceedings 1989 (2016): 90

Meek, Christopher; Simard, Patrice; Zhu, Xiaojin; “Analysis of a design pattern for teaching with features and labels” arXiv preprint arXiv:1611.05950 (2016):

Alfeld, Scott; Zhu, Xiaojin; Barford, Paul; “Machine Teaching as Search” Ninth Annual Symposium on Combinatorial Search (2016):

Huang, Tzu-Kuo; Li, Lihong; Vartanian, Ara; Amershi, Saleema; Zhu, Xiaojin; “Active Learning with Oracle Epiphany” Advances in Neural Information Processing Systems (2016): 2820-2828

Liu, Ji; Zhu, Xiaojin; “The teaching dimension of linear learners” Journal of Machine Learning Research 17 162(2016): 25-Jan

Ghosh, Shalini; Lincoln, Patrick; Tiwari, Ashish; Zhu, Xiaojin;”Trusted Machine Learning for Probabilistic Models” Reliable Machine Learning in the Wild at ICML (2016):

Suh, Jina; Zhu, Xiaojin; Amershi, Saleema; “The label complexity of mixed-initiative classifier training” International Conference on Machine Learning(ICML) (2016):

Jun, Kwang-Sung; Jamieson, Kevin; Nowak, Robert; Zhu, Xiaojin; “Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls” The 19th International Conference on Artificial Intelligence and Statistics(AISTATS) (2016):

Liu, Peng; Sanalkumar, Rajendran; Bresnick, Emery H; Kele_, Sƒndƒz; Dewey, Colin N; “Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq” Genome Research 26 8(2016): 1124-1133

Mukherjee, Lopamudra; Peng, Jiming; Sigmund, Trevor; Singh, Vikas; “Network Flow Formulations for Learning Binary Hashing” European Conference on Computer Vision (2016): 366-381

Kim, Won Hwa; Hwang, Seong Jae; Adluru, Nagesh; Johnson, Sterling C; Singh, Vikas; “Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging” European Conference on Computer Vision (2016): 188-205

Kim, Hyunwoo J; Smith, Brandon M; Adluru, Nagesh; Dyer, Charles R; Johnson, Sterling C; Singh, Vikas; “Abundant Inverse Regression Using Sufficient Reduction and Its Applications” European Conference on Computer Vision (2016): 570-584

Ithapu, Vamsi K; Ravi, Sathya N; Singh, Vikas; “On the interplay of network structure and gradient convergence in deep learning” Communication, Control, and Computing(Allerton), 2016 54th Annual Allerton Conference on (2016): 488-495

Davidson, Drew; Wu, Hao; Jellinek, Robert; Ristenpart, Thomas; Singh, Vikas; “Controlling UAVs with sensor input spoofing attacks” Proceedings of the 10th USENIX Conference on Offensive Technologies (2016): 221-231

Kim, Hyunwoo; Adluru, Nagesh; Johnson, Sterling C; Singh, Vikas; “MANIFOLD-VALUED STATISTICAL MODELS FOR LONGITUDINAL MORPHOMETRIC ANALYSIS IN PRECLINICAL ALZHEIMER’S DISEASE” Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 12 7(2016): P529-P530

Hwang, Seong Jae; Kim, Won Hwa; Bendlin, Barbara B; Adluru, Nagesh; Singh, Vikas; “MULTI-RESOLUTION ANALYSIS OF DTI-DERIVED BRAIN CONNECTIVITY AND THE INFLUENCE OF PET-DERIVED ALZHEIMER’S DISEASE PATHOLOGY IN A PRECLINICAL COHORT” Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 12 7(2016): P205

Zhou, Hao; Ithapu, Vamsi K; Ravi, Sathya Narayanan; Singh, Vikas; Wahba, Grace; Johnson, Sterling C; “Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer’s Disease” Advances in Neural Information Processing Systems (2016): 2496-2504

Wen, Bruce; Campbell, Kirby R; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin W; Campagnola, Paul J; “3D texture analysis for classification of second harmonic generation images of human ovarian cancer” Scientific reports 6(2016):

Jae Hwang, Seong; Adluru, Nagesh; Collins, Maxwell D; Ravi, Sathya N; Bendlin, Barbara B; Johnson, Sterling C; Singh, Vikas; “Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016): 2517-2525

Ravi, Sathya Narayanan; Ithapu, Vamsi; Johnson, Sterling; Singh, Vikas; “Experimental design on a budget for sparse linear models and applications” International Conference on Machine Learning (2016): 583-592

Hwa Kim, Won; Kim, Hyunwoo J; Adluru, Nagesh; Singh, Vikas; “Latent variable graphical model selection using harmonic analysis: applications to the human connectome project(HCP)” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016): 2443-2451

Kim, Hyunwoo J; Adluru, Nagesh; Bendlin, Barbara B; Johnson, Sterling C; Vemuri, Baba C; Singh, Vikas; “Canonical Correlation Analysis on SPD(n) Manifolds” Riemannian Computing in Computer Vision (2016): 69-100

Rao, Nikhil; Nowak, Robert; Cox, Christopher; Rogers, Timothy; “Classification with the sparse group lasso” IEEE Transactions on Signal Processing 64 2(2016): 448-463

Oswal, Urvashi; Cox, Christopher; Lambon-Ralph, Matthew; Rogers, Timothy; Nowak, Robert; “Representational similarity learning with application to brain networks” International Conference on Machine Learning (2016): 1041-1049

Siahpirani, Alireza F; Roy, Sushmita; “A prior-based integrative framework for functional transcriptional regulatory network inference” Nucleic acids research (2016): gkw963

Chasman, Deborah; Walters, Kevin B; Lopes, Tiago JS; Eisfeld, Amie J; Kawaoka, Yoshihiro; Roy, Sushmita; “Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens” PLoS Comput Biol 12 7(2016): e1005013

Chasman, Deborah; Siahpirani, Alireza Fotuhi; Roy, Sushmita; “Network-based approaches for analysis of complex biological systems” Current opinion in biotechnology 39(2016): 157-166

Siahpirani, Alireza Fotuhi; Ay, Ferhat; Roy, Sushmita; “A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions” Genome biology 17 1(2016): 114

Niu, Zhen; Chasman, Deborah; Eisfeld, Amie J; Kawaoka, Yoshihiro; Roy, Sushmita; “Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions” Bioinformatics 32 10(2016): 1509-1517

Knaack, Sara A; Thompson, Dawn A; Roy, Sushmita; “Reconstruction and Analysis of the Evolution of Modular Transcriptional Regulatory Programs Using Arboretum” Yeast Functional Genomics: Methods and Protocols (2016): 375-389

Rohe, Karl; Qin, Tai; Yu, Bin; “Co-clustering directed graphs to discover asymmetries and directional communities” Proceedings of the National Academy of Sciences 113 45(2016): 12679-12684

Khabbazian, Mohammad; Kriebel, Ricardo; Rohe, Karl; Ane, Cecile; “Fast and accurate detection of evolutionary shifts in Ornstein╨Uhlenbeck models” Methods in Ecology and Evolution 7 7(2016): 811-824

Khabbazian, Mohammad; Hanlon, Bret; Russek, Zoe; Rohe, Karl; “Novel Sampling Design for Respondent-driven Sampling” arXiv preprint arXiv:1606.00387 (2016):

Wang, Song; Rohe, Karl; “Discussion of ╥Coauthorship and citation networks for statisticians╙” The Annals of Applied Statistics 10 4(2016): 1820-1826

2015

Ithapu, Vamsi K; Ravi, Sathya; Singh, Vikas; “Convergence of gradient based pre-training in Denoising autoencoders” arXiv preprint arXiv:1502.03537 (2015):

Zhu, Jerry; “Machine Teaching” (2015):

Chi, Hehua; Hu, Yu Hen; “Face de-identification using facial identity preserving features” Signal and Information Processing(GlobalSIP), 2015 IEEE Global Conference on (2015): 586-590

Jiang, Xin; Reynaud-Bouret, Patricia; Rivoirard, Vincent; Sansonnet, Laure; Willett, Rebecca; “A data-dependent weighted LASSO under Poisson noise” arXiv preprint arXiv:1509.08892 (2015):

Hall, Eric C; Willett, Rebecca M; “Online convex optimization in dynamic environments” IEEE Journal of Selected Topics in Signal Processing 9 4(2015): 647-662

Hall, Eric C; Willett, Rebecca M; “Online learning of neural network structure from spike trains” Neural Engineering(NER), 2015 7th International IEEE/EMBS Conference on (2015): 930-933

Ganti, Ravi; Willett, Rebecca M; “Sparse Linear Regression With Missing Data” arXiv preprint arXiv:1503.08348 (2015):

Pimentel-Alarcùn, Daniel L; Nowak, Robert D; “Adaptive strategy for restricted-sampling noisy low-rank matrix completion” Computational Advances in Multi-Sensor Adaptive Processing(CAMSAP), 2015 IEEE 6th International Workshop on (2015): 429-432

Ganti, Ravi; Rao, Nikhil; Willett, Rebecca M; Nowak, Robert; “Learning single index models in high dimensions” arXiv preprint arXiv:1506.08910 (2015):

Pimentel-Alarcùn, Daniel L; Boston, Nigel; Nowak, Robert D; “Deterministic conditions for subspace identifiability from incomplete sampling” Information Theory(ISIT), 2015 IEEE International Symposium on (2015): 2191-2195

Dasarathy, Gautam; Shah, Parikshit; Bhaskar, Badri Narayan; Nowak, Robert D; “Sketching sparse matrices, covariances, and graphs via tensor products” IEEE Transactions on Information Theory 61 3(2015): 1373-1388

Jamieson, Kevin G; Jain, Lalit; Fernandez, Chris; Glattard, Nicholas J; Nowak, Rob; “Next: A system for real-world development, evaluation, and application of active learning” Advances in Neural Information Processing Systems (2015): 2656-2664

Zhou, Shiwei; Hu, Yu Hen; Jiang, Hongrui; “Multiple view image denoising using 3D focus image stacks” Signal and Information Processing(GlobalSIP), 2015 IEEE Global Conference on (2015): 1052-1056

Wang, Gaoang; Hu, Yu Hen; Jiang, Hongrui; “Piecewise planar super-resolution for 3D scene” Signal and Information Processing(GlobalSIP), 2015 IEEE Global Conference on (2015): 333-337

Dai, Biao; Li, Cuiyun; Ji, Hongbing; Hu, Yuhen; “Iterative-Mapping PHD filter for extended targets tracking” Control, Automation and Information Sciences(ICCAIS), 2015 International Conference on (2015): 85-88

Huang, Ru; Chu, Xiaoli; Zhang, Jie; Hu, Yu Hen; “Energy-efficient monitoring in software defined wireless sensor networks using reinforcement learning: A prototype” International Journal of Distributed Sensor Networks (2015):

Kumaraswamy, Raksha; Wazalwar, Anurag; Khot, Tushar; Shavlik, Jude W; Natarajan, Sriraam; “Anomaly Detection in Text: The Value of Domain Knowledge.” FLAIRS Conference (2015): 225-228

Khot, Tushar; Natarajan, Sriraam; Kersting, Kristian; Shavlik, Jude; “Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases” Machine Learning 100 1(2015): 75-100

Kuusisto, Finn; Dutra, InÉs; Elezaby, Mai; Mendonìa, Eneida A; Shavlik, Jude; Burnside, Elizabeth S; “Leveraging expert knowledge to improve machine-learned decision support systems” AMIA Summits on Translational Science Proceedings 2015(2015): 87

Zeng, Xin; Li, Bo; Welch, Rene; Rojo, Constanza; Zheng, Ye; Dewey, Colin N; Kele_, Sƒndƒz; “Perm-seq: mapping protein-DNA interactions in segmental duplication and highly repetitive regions of genomes with prior-enhanced read mapping” PLoS Comput Biol 11 10(2015): e1004491

Leng, Ning; Li, Yuan; McIntosh, Brian E; Nguyen, Bao Kim; Duffin, Bret; Tian, Shulan; Thomson, James A; Dewey, Colin; Stewart, Ron; Kendziorski, Christina; “EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments” Bioinformatics (2015): btv193

Ardalani, Newsha; Lestourgeon, Clint; Sankaralingam, Karthikeyan; Zhu, Xiaojin; “Cross-architecture performance prediction(xapp) using cpu code to predict gpu performance” Proceedings of the 48th International Symposium on Microarchitecture (2015): 725-737

Mei, Shike; Zhu, Xiaojin; “Some Submodular Data-Poisoning Attacks on Machine Learners” (2015):

Zhu, Xiaojin; “Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education.” AAAI (2015): 4083-4087

Mei, Shike; Zhu, Xiaojin; “Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners.” AAAI (2015): 2871-2877

Dasarathy, Gautam; Nowak, Robert; Zhu, Xiaojin; “S2: An efficient graph based active learning algorithm with application to nonparametric classification” Conference on Learning Theory (2015): 503-522

Kim, Won Hwa; Adluru, Nagesh; Chung, Moo K; Okonkwo, Ozioma C; Johnson, Sterling C; Bendlin, Barbara B; Singh, Vikas; “Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer’s disease” NeuroImage 118(2015): 103-117

Kim, Hyunwoo; Xu, Jia; Vemuri, Baba; Singh, Vikas; “Manifold-valued Dirichlet processes” International Conference on Machine Learning (2015): 1199-1208

Ithapu, Vamsi K; Ravi, Sathya; Singh, Vikas; “Convergence rates for pretraining and dropout: Guiding learning parameters using network structure” arXiv preprint arXiv:1506.03412 (2015):

Kim, Won Hwa; Singh, Vikas; Chung, Moo K; Adluru, Nagesh; Bendlin, Barbara B; Johnson, Sterling C; “Multi-resolution statistical analysis on graph structured data in neuroimaging” Biomedical Imaging(ISBI), 2015 IEEE 12th International Symposium on (2015): 1548-1551

Kim, Won Hwa; Adluru, Nagesh; Chung, Moo K; Okonkwo, Ozioma C; Johnson, Sterling C; Bendlin, Barbara B; Singh, Vikas; “A framework for performing multi-resolution statistical analysis of brain connectivity graphs for preclinical Alzheimer╒s disease” Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 11 7(2015): P882

Jae Hwang, Seong; Collins, Maxwell D; Ravi, Sathya N; Ithapu, Vamsi K; Adluru, Nagesh; Johnson, Sterling C; Singh, Vikas; “A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer” Proceedings of the IEEE International Conference on Computer Vision (2015): 1841-1849

Kim, Hyunwoo J; Adluru, Nagesh; Banerjee, Monami; Vemuri, Baba C; Singh, Vikas; “Interpolation on the manifold of K component GMMs” Proceedings of the IEEE International Conference on Computer Vision (2015): 2884-2892

Mukherjee, Lopamudra; Ravi, Sathya N; Ithapu, Vamsi K; Holmes, Tyler; Singh, Vikas; “An NMF perspective on binary hashing” Proceedings of the IEEE International Conference on Computer Vision (2015): 4184-4192

Hwa Kim, Won; Ravi, Sathya N; Johnson, Sterling C; Okonkwo, Ozioma C; Singh, Vikas; “On Statistical Analysis of Neuroimages with Imperfect Registration” Proceedings of the IEEE International Conference on Computer Vision (2015): 666-674

Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas; “SnFFT: A Julia Toolkit for Fourier Analysis of Functions over Permutations” Journal of Machine Learning Research 16(2015): 3469-3473

Hwa Kim, Won; Bendlin, Barbara B; Chung, Moo K; Johnson, Sterling C; Singh, Vikas; “Statistical inference models for image datasets with systematic variations” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015): 4795-4803

Xu, Jia; Mukherjee, Lopamudra; Li, Yin; Warner, Jamieson; Rehg, James M; Singh, Vikas; “Gaze-enabled egocentric video summarization via constrained submodular maximization” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015): 2235-2244

Ithapu, Vamsi K; Singh, Vikas; Johnson, Sterling C; Okonkwo, Ozioma C; “Medical Imaging System Providing Disease Prognosis” (2015):

Ithapu, Vamsi K; Singh, Vikas; Okonkwo, Ozioma C; Chappell, Richard J; Dowling, N Maritza; Johnson, Sterling C; “Imaging based enrichment criteria using deep learning algorithms for efficient clinical trials in MCI” Alzheimer’s & dementia: the journal of the Alzheimer’s Association 11 12(2015): 1489

Kalish, Charles W; Zhu, Xiaojin; Rogers, Timothy T; “Drift in children’s categories: when experienced distributions conflict with prior learning” Developmental science 18 6(2015): 940-956

Alibali, Martha W; Kalish, Chuck; Rogers, Timothy T; Massey, Christine; Kellman, Philip J; Sloutsky, Vladimir M; McClelland, James L; Mickey, Kevin W; “Connecting learning, memory, and representation in math education.” CogSci (2015):

Murphy, April; Rogers, Timothy T; Hubbard, Edward; Brower, Autumn; “Beyond Magnitude: How Math Expertise Guides Number Representation.” CogSci (2015):

Jun, Kwang-Sung; Zhu, Xiaojin; Rogers, Timothy T; Yang, Zhuoran; “Human memory search as initial-visit emitting random walk” Advances in Neural Information Processing Systems (2015): 1072-1080

Roy, Sushmita; Thompson, Dawn; “Evolution of regulatory networks in Candida glabrata: learning to live with the human host” FEMS yeast research 15 8(2015): fov087

Thompson, Dawn; Regev, Aviv; Roy, Sushmita; “Comparative analysis of gene regulatory networks: from network reconstruction to evolution” Annual review of cell and developmental biology 31(2015): 399-428

Roy, Sushmita; Siahpirani, Alireza Fotuhi; Chasman, Deborah; Knaack, Sara; Ay, Ferhat; Stewart, Ron; Wilson, Michael; Sridharan, Rupa; “A predictive modeling approach for cell line-specific long-range regulatory interactions” Nucleic acids research 43 18(2015): 8694-8712

Li, Xiao; Rohe, Karl; “Central limit theorems for network driven sampling” arXiv preprint arXiv:1509.04704 (2015):

Cho, Juhee; Kim, Donggyu; Rohe, Karl; “Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise” arXiv preprint arXiv:1508.05431 (2015):

Rohe, Karl; “Network driven sampling; a critical threshold for design effects” arXiv preprint arXiv:1505.05461 (2015):

Rohe, Karl; “Preconditioning for classical relationships: a note relating ridge regression and OLS p_values to preconditioned sparse penalized regression” Stat 4 1(2015): 157-166

Jia, Jinzhu; Rohe, Karl; “Preconditioning the Lasso for sign consistency” Electronic Journal of Statistics 9 1(2015): 1150-1172

2014

Nichols, Jonathan M; Oh, Albert K; Willett, Rebecca M; “Reducing basis mismatch in harmonic signal recovery via alternating convex search” IEEE Signal Processing Letters 21 8(2014): 1007-1011

Guan, Peng; Raginsky, Maxim; Willett, Rebecca; “From minimax value to low-regret algorithms for online Markov decision processes” American Control Conference(ACC), 2014 (2014): 471-476

Guan, Peng; Raginsky, Maxim; Willett, Rebecca M; “Online Markov decision processes with Kullback╨Leibler control cost” IEEE Transactions on Automatic Control 59 6(2014): 1423-1438

Nichols, JM; Bucholtz, F; McLaughlin, CV; Oh, AK; Willett, RM; “Fixing basis mismatch in compressively sampled photonic link” SPIE Sensing Technology+ Applications (2014): 91180N-91180N-5

Malloy, Matthew L; Nowak, Robert D; “Sequential testing for sparse recovery” IEEE Transactions on Information Theory 60 12(2014): 7862-7873

Vats, Divyanshu; Nowak, Robert D; Baraniuk, Richard G; “Active Learning for Undirected Graphical Model Selection.” AISTATS (2014): 958-967

Li, Bo; Fillmore, Nathanael; Bai, Yongsheng; Collins, Mike; Thomson, James A; Stewart, Ron; Dewey, Colin N; “Evaluation of de novo transcriptome assemblies from RNA-Seq data” Genome biology 15 12(2014): 553

Collins, Maxwell D; Liu, Ji; Xu, Jia; Mukherjee, Lopamudra; Singh, Vikas; “Spectral clustering with a convex regularizer on millions of images” European Conference on Computer Vision (2014): 282-298

Wen, Bruce L; Brewer, Molly A; Nadiarnykh, Oleg; Hocker, James; Singh, Vikas; Mackie, Thomas R; Campagnola, Paul J; “Texture analysis applied to second harmonic generation image data for ovarian cancer classification” Journal of biomedical optics 19 9(2014): 096007-096007

Rogers, Timothy T; McClelland, James L; “Parallel distributed processing at 25: Further explorations in the microstructure of cognition” Cognitive Science 38 6(2014): 1024-1077

Rao, Nikhil; Nowak, Robert; Cox, Christopher; Rogers, Timothy; “Classification with sparse overlapping groups” arXiv preprint arXiv:1402.4512 (2014):

Rao, Nikhil S; Nowak, Robert D; Cox, Christopher R; Rogers, Timothy T; “Logistic regression with structured sparsity” arXiv preprint arXiv:1402.4512 (2014):

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; “High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia” NeuroImage 102(2014): 35-48

Knaack, Sara A; Siahpirani, Alireza Fotuhi; Roy, Sushmita; “A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components” Cancer informatics Suppl. 5(2014): 69

Rohe, Karl; “A note relating ridge regression and OLS p-values to preconditioned sparse penalized regression” arXiv preprint arXiv:1411.7405 (2014):

Binkiewicz, Norbert; Vogelstein, Joshua T; Rohe, Karl; “Covariate-assisted spectral clustering” arXiv preprint arXiv:1411.2158 (2014):

Rohe, Karl; Qin, Tai; Fan, Haoyang; “The highest dimensional stochastic blockmodel with a regularized estimator” Statistica Sinica (2014): 1771-1786

2013

Rohe, Karl; Qin, Tai; “The blessing of transitivity in sparse and stochastic networks” arXiv preprint arXiv:1307.2302 (2013):

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A; Stewart, Ron; Gasch, Audrey P; “Integrated module and gene-specific regulatory inference implicates upstream signaling networks” PLoS Comput Biol 9 10(2013): e1003252

Roy, Sushmita; Wapinski, Ilan; Pfiffner, Jenna; French, Courtney; Socha, Amanda; Konieczka, Jay; Habib, Naomi; Kellis, Manolis; Thompson, Dawn; Regev, Aviv; “Arboretum: reconstruction and analysis of the evolutionary history of condition-specific transcriptional modules” Genome research 23 6(2013): 1039-1050

Vu, Vincent Q; Cho, Juhee; Lei, Jing; Rohe, Karl; “Fantope projection and selection: A near-optimal convex relaxation of sparse PCA” Advances in neural information processing systems (2013): 2670-2678

Qin, Tai; Rohe, Karl; “Regularized spectral clustering under the degree-corrected stochastic blockmodel” Advances in Neural Information Processing Systems (2013): 3120-3128

2012

Adluru, Nagesh, Vikas Singh, and Andrew L Alexander. 2012. “Adaptive Cuts for Extracting Specific White Matter Tracts.” 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 1393–96. doi:10.1109/ISBI.2012.6235828.

Bar-Joseph, Ziv, Anthony Gitter, and Itamar Simon. 2012. “Studying and Modelling Dynamic Biological Processes Using Time-Series Gene Expression Data..” Nature Reviews. Genetics 13 (8). Nature Publishing Group: 552–64. doi:10.1038/nrg3244.

Cai, T Tony, and Ming Yuan. 2012a. “Optimal Estimation of the Mean Function Based on Discretely Sampled Functional Data: Phase Transition.” arXiv.org. Institute of Mathematical Statistics. doi:10.1214/11-AOS898.

Cai, T Tony, and Ming Yuan. 2012b. “Adaptive Covariance Matrix Estimation Through Block Thresholding.” arXiv.org. Institute of Mathematical Statistics. doi:10.1214/12-AOS999.

Cheung, B L P, R Nowak, Hyong Chol Lee, W Drongelen, and B D Veen. 2012. “Cross Validation for Selection of Cortical Interaction Models From Scalp EEG or MEG.” IEEE Transactions on Biomedical Engineering 59 (2). IEEE: 504–14. doi:10.1109/TBME.2011.2174991.

Church, T, J S Ellenberg, and B Farb. 2012. “FI-Modules: a New Approach to Stability for Sn-Representations.” arXiv.org.

Collins, Maxwell D, Jia Xu, Leo Grady, and Vikas Singh. 2012. “Random Walks Based Multi-Image Segmentation: Quasiconvexity Results and GPU-Based Solutions.” 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1656–63. doi:10.1109/CVPR.2012.6247859.

Dewey, Colin N. 2012. “Whole-Genome Alignment.” Methods in Molecular Biology (Clifton, N.J.) 855 (Chapter 8). Totowa, NJ: Humana Press: 237–57. doi:10.1007/978-1-61779-582-4_8.

Ellenberg, Jordan S, Chris Hall, and Emmanuel Kowalski. 2012c. “Expander Graphs, Gonality, and Variation of Galois Representations.” Duke Mathematical Journal 161 (7). Duke University Press: 1233–75. doi:10.1215/00127094-1593272.

Harmany, Zachary T, Roummel F Marcia, and Rebecca M Willett. 2012. “This Is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice.” Image Processing, IEEE Transactions on 21 (3). IEEE: 1084–96. doi:10.1109/TIP.2011.2168410.

Hinrichs, Chris, Vikas Singh, Jiming Peng, and Sterling Johnson. 2012a. “Q-MKL: Matrix-Induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging,” 1421–29.

Hinrichs, Chris, Vikas Singh, Jiming Peng, and Sterling Johnson. 2012b. “Q-MKL: Matrix-Induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging,” 1421–29.

Holby, Edward F, Jeff Greeley, and Dane Morgan. 2012. “Thermodynamics and Hysteresis of Oxide Formation and Removal on Platinum (111) Surfaces.” The Journal of Physical … 116 (18). American Chemical Society: 9942–46. doi:10.1021/jp210805z.

Jacobsen, Heather, Brian Puchala, Thomas F Kuech, and Dane Morgan. 2012. “<I>Ab Initio</I> Study of the Strain Dependent Thermodynamics of Bi Doping in GaAs.” Physical Review B 86 (8). American Physical Society: 085207. doi:10.1103/PhysRevB.86.085207.

Jia, Jinzhu, and Karl Rohe. 2012. “Preconditioning to Comply with the Irrepresentable Condition.” arXiv.org.

Kim, Won H, Deepti Pachauri, Charles Hatt, Moo K Chung, Sterling Johnson, and Vikas Singh. 2012a. “Wavelet Based Multi-Scale Shape Features on Arbitrary Surfaces for Cortical Thickness Discrimination,” 1241–49.

Kim, Won H, Deepti Pachauri, Charles Hatt, Moo K Chung, Sterling Johnson, and Vikas Singh. 2012b. “Wavelet Based Multi-Scale Shape Features on Arbitrary Surfaces for Cortical Thickness Discrimination,” 1241–49.

Koltchinskii, Vladimir, and Ming Yuan. 2012. “Sparsity in Multiple Kernel Learning.” arXiv.org. Institute of Mathematical Statistics. doi:10.1214/10-AOS825.

Mukherjee, Lopamudra, Vikas Singh, Jia Xu, and Maxwell D Collins. 2012. “Analyzing the Subspace Structure of Related Images: Concurrent Segmentation of Image Sets.” In Computer Vision – ECCV 2012, 7575:128–42. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-33765-9_10.

Navlakha, Saket, Anthony Gitter, and Ziv Bar-Joseph. 2012. “A Network-Based Approach for Predicting Missing Pathway Interactions.” Edited by Costas D Maranas. PLoS Computational Biology 8 (8). Public Library of Science: e1002640. doi:10.1371/journal.pcbi.1002640.

Noto, Keith, and Mark Craven. 2012. “Learning Hidden Markov Models for Regression Using Path Aggregation.” arXiv.org.

Pachauri, Deepti, Maxwell Collins, Risi Kondor, and Vikas Singh. 2012. “Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis..” Proceedings of the … International Conference on Machine Learning. International Conference on Machine Learning 2012. NIH Public Access: 1271–78.

Pachauri, Deepti, Maxwell Collins, Vikas Singh, and Risi Kondor. 2012. “Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis.” arXiv.org.

Peng, Jiming, Lopamudra Mukherjee, Vikas Singh, Dale Schuurmans, and Linli Xu. 2012. “An Efficient Algorithm for Maximal Margin Clustering.” Journal of Global Optimization 52 (1). Springer US: 123–37. doi:10.1007/s10898-011-9691-4.

Rao, N S, B Recht, and R D Nowak. 2012. “Universal Measurement Bounds for Structured Sparse Signal Recovery.” International ….

Garvesh Raskutti, Martin J Wainwright, and Bin Yu. 2012. “Minimax-Optimal Rates for Sparse Additive Models Over Kernel Classes via Convex Programming.” The Journal of Machine Learning Research 13 (1). JMLR.org: 389–427.

Rohe, Karl, Tai Qin, and Bin Yu. 2012. “Co-Clustering for Directed Graphs: the Stochastic Co-Blockmodel and Spectral Algorithm Di-Sim.” arXiv.org.

Rohe, Karl, Tai Qin, and Haoyang Fan. 2012. “The Highest Dimensional Stochastic Blockmodel with a Regularized Estimator.” arXiv.org.

Samworth, Richard J, and Ming Yuan. 2012. “Independent Component Analysis via Nonparametric Maximum Likelihood Estimation.” arXiv.org.

Schulz, Marcel H, William E Devanny, Anthony Gitter, Shan Zhong, Jason Ernst, and Ziv Bar-Joseph. 2012. “DREM 2.0: Improved Reconstruction of Dynamic Regulatory Networks From Time-Series Expression Data.” BMC Systems Biology 6 (1). BioMed Central: 104. doi:10.1186/1752-0509-6-104.

Wegkamp, Marten, and Ming Yuan. 2012. “Support Vector Machines with a Reject Option.” arXiv.org. Bernoulli Society for Mathematical Statistics and Probability. doi:10.3150/10-BEJ320.

Werner-Washburne, M, Sushmita Roy, and George S Davidson. 2012. “Aging and the Survival of Quiescent and Non-Quiescent Cells in Yeast Stationary-Phase Cultures.” Sub-Cellular Biochemistry 57 (Chapter 6). Dordrecht: Springer Netherlands: 123–43. doi:10.1007/978-94-007-2561-4_6.

Xu, Jun-Ming, Aniruddha Bhargava, Robert Nowak, and Xiaojin Zhu. 2012. “Robust Spatio-Temporal Signal Recovery From Noisy Counts in Social Media.” arXiv.org.

Yankovich, Andrew B, Brian Puchala, Fei Wang, Jung-Hun Seo, Dane Morgan, Xudong Wang, Zhenqiang Ma, Alex V Kvit, and Paul M Voyles. 2012. “Stable P-Type Conduction From Sb-Decorated Head-to-Head Basal Plane Inversion Domain Boundaries in ZnO Nanowires.” Nano … 12 (3). American Chemical Society: 1311–16. doi:10.1021/nl203848t.

Yuan, Ming. 2012. “Discussion: Latent Variable Graphical Model Selection via Convex Optimization.” arXiv.org. doi:10.1214/12-AOS979.

Yuan, Ming, and T Tony Cai. 2012. “A Reproducing Kernel Hilbert Space Approach to Functional Linear Regression.” arXiv.org. Institute of Mathematical Statistics. doi:10.1214/09-AOS772.

2011

Austin, Benjamin, Erik Kastman, Yin Huang, Guofan Xu, Nayanjyoti Pathak, Vikas Singh, Sean Fain, et al. 2011. “The Relationship of White Matter Hyperintensities with Perfusion and Cognition in Middle-Aged Adults at Risk for Alzheimer’s Disease.” Alzheimer“S & Dementia: the Journal of the Alzheimer”S Association 7 (4). Elsevier: e33–e34. doi:10.1016/j.jalz.2011.09.094.

Dewey, Colin N. 2011. “Positional Orthology: Putting Genomic Evolutionary Relationships Into Context..” Briefings in Bioinformatics 12 (5). Oxford University Press: 401–12. doi:10.1093/bib/bbr040.

Gitter, Anthony, Judith Klein-Seetharaman, Anupam Gupta, and Ziv Bar-Joseph. 2011. “Discovering Pathways by Orienting Edges in Protein Interaction Networks..” Nucleic Acids Research 39 (4). Oxford University Press: e22–e22. doi:10.1093/nar/gkq1207.

Li, Bo, and Colin N Dewey. 2011. “RSEM: Accurate Transcript Quantification From RNA-Seq Data with or Without a Reference Genome.” BMC Bioinformatics 12 (1). BioMed Central: 1. doi:10.1186/1471-2105-12-323.

Mukherjee, Lopamudra, Vikas Singh, and Jiming Peng. 2011. “Scale Invariant Cosegmentation for Image Groups.” 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1881–88. doi:10.1109/CVPR.2011.5995420.

Rao, Nikhil S, Robert D NowakStephen J Wright, and Nick G Kingsbury. 2011. “Convex Approaches to Model Wavelet Sparsity Patterns.” In, 1917–20. IEEE. doi:10.1109/ICIP.2011.6115845.

Roy, Sushmita, Margaret Werner-Washburne, and Terran Lane. 2011. “A Multiple Network Learning Approach to Capture System-Wide Condition-Specific Responses.” Bioinformatics (Oxford, England) 27 (13). Oxford University Press: 1832–38. doi:10.1093/bioinformatics/btr270.

Willett, Rebecca M, Roummel F Marcia, and Jonathan M Nichols. 2011. “Compressed Sensing for Practical Optical Imaging Systems: a Tutorial.” Optical Engineering 50 (7). International Society for Optics and Photonics: 072601–072601–13. doi:10.1117/1.3596602.

2010
Bajwa, Waheed U, Jarvis Haupt, Akbar M Sayeed, and Robert Nowak. 2010. “Compressed Channel Sensing: a New Approach to Estimating Sparse Multipath Channels.” Proceedings of the IEEE 98 (6). IEEE: 1058–76. doi:10.1109/JPROC.2010.2042415.

Bennett, Michael A, Jordan S Ellenberg, and Nathan C Ng. 2010. “THE DIOPHANTINE EQUATION a 4+ 2 δB 2= C N.” International Journal of Number Theory 06 (02): 311–38. doi:10.1142/S1793042110002971.

Ellenberg, Jordan S, Richard Oberlin, and Terence Tao. 2010. “The Kakeya Set and Maximal Conjectures for Algebraic Varieties Over Finite Fields.” Mathematika 56 (01). Cambridge University Press: 1–25. doi:10.1112/S0025579309000400.

Haupt, Jarvis, Waheed U Bajwa, Gil Raz, and Robert Nowak. 2010. “Toeplitz Compressed Sensing Matrices with Applications to Sparse Channel Estimation.” Information Theory, IEEE Transactions on 56 (11). IEEE: 5862–75. doi:10.1109/TIT.2010.2070191.

Mukherjee, Lopamudra, Vikas Singh, Junbiao Peng, and Chris Hinrichs. 2010. “Learning Kernels for Variants of Normalized Cuts: Convex Relaxations and Applications.” 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 3145–52. doi:10.1109/CVPR.2010.5540076

Raskutti, Garvesh, Martin J Wainwright, and Bin Yu. 2010a. “Restricted Eigenvalue Properties for Correlated Gaussian Designs.” The Journal of Machine Learning Research 11 (March). JMLR.org: 2241–59.

Raskutti, Garvesh, Martin J Wainwright, and Bin Yu. 2010b. “Minimax-Optimal Rates for Sparse Additive Models Over Kernel Classes via Convex Programming.” arXiv.org.

Tropp, Joel A, and Stephen J Wright. 2010. “Computational Methods for Sparse Solution of Linear Inverse Problems.” Proceedings of the IEEE 98 (6). IEEE: 948–58. doi:10.1109/JPROC.2010.2044010.

Venkatesh, A, and J S Ellenberg. 2010. “Statistics of Number Fields and Function Fields.” International Congress of Mathematicians, Hyderabad, India 2010.

Yuan, Ming, V Roshan Joseph, and Hui Zou. 2010. “Structured Variable Selection and Estimation.” arXiv.org. doi:10.1214/09-AOAS254.