Joint source-channel vector quantization using deterministic annealing. - David J. Miller 001; Kenneth Rose (ICASSP, 1992)

An Improved Sequential Search Multistage Vector Quantizer. - David J. Miller 001; Kenneth Rose (Data Compression Conference, 1993)

Entropy-Constrained Tree-Structured Vector Quantizer Design by the Minimum Cross Entropy Principle. - Kenneth Rose; David J. Miller 001; Allen Gersho (Data Compression Conference, 1994)

Deterministic annealing for trellis quantizer and HMM design using Baum-Welch re-estimation. - David J. Miller 001; Kenneth Rose; Philip A. Chou (ICASSP (5), 1994)

A non-greedy approach to tree-structured clustering. - David J. Miller 001; Kenneth Rose (Pattern Recognit. Lett., 1994)

Combined source-channel vector quantization using deterministic annealing. - David J. Miller 001; Kenneth Rose (IEEE Trans. Commun., 1994)

An Information-theoretic Learning Algorithm for Neural Network Classification. - David J. Miller 001; Ajit V. Rao; Kenneth Rose; Allen Gersho (NIPS, 1995)

A generalized VQ method for combined compression and estimation. - Ajit V. Rao; David J. Miller 001; Kenneth Rose; Allen Gersho (ICASSP, 1996)

A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data. - David J. Miller 001; Hasan S. Uyar (NIPS, 1996)

Hierarchical, Unsupervised Learning with Growing via Phase Transitions. - David J. Miller 001; Kenneth Rose (Neural Comput., 1996)

Entropy-constrained tree-structured vector quantizer design. - Kenneth Rose; David J. Miller 001; Allen Gersho (IEEE Trans. Image Process., 1996)

A global optimization technique for statistical classifier design. - David J. Miller 001; Ajit V. Rao; Kenneth Rose; Allen Gersho (IEEE Trans. Signal Process., 1996)

Deterministically annealed mixture of experts models for statistical regression. - Ajit V. Rao; David J. Miller 001; Kenneth Rose; Allen Gersho (ICASSP, 1997)

Image Decoding Over Noisy Channels Using Minimum Mean-Squared Estimation and a Markov Mesh. - MoonSeo Park; David J. Miller 001 (ICIP (3), 1997)

Low-delay optimal MAP state estimation in HMM's with application to symbol decoding. - MoonSeo Park; David J. Miller 001 (IEEE Signal Process. Lett., 1997)

Mixture of experts regression modeling by deterministic annealing. - Ajit V. Rao; David J. Miller 001; Kenneth Rose; Allen Gersho (IEEE Trans. Signal Process., 1997)

A New Set Partitioning Method for Wavelet-based Image Coding. - Jeongjin Roh; David J. Miller 001 (ICIP (1), 1998)

Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier. - David J. Miller 001; Hasan S. Uyar (Neural Comput., 1998)

A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models. - David J. Miller 001; MoonSeo Park (IEEE Trans. Commun., 1998)

Improved Joint Source-Channel Decoding for Variable-Length Encoded Data Using Soft Decisions and MMSE Estimation. - MoonSeo Park; David J. Miller 001 (Data Compression Conference, 1999)

Ensemble classification by critic-driven combining. - David J. Miller 001; Lian Yan (ICASSP, 1999)

Time series prediction via neural network inversion. - Lian Yan; David J. Miller 001 (ICASSP, 1999)

Approximate maximum entropy joint feature inference for discrete space classification. - David J. Miller 001; Lian Yan (IJCNN, 1999)

A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models. - Ajit V. Rao; David J. Miller 001; Kenneth Rose; Allen Gersho (IEEE Trans. Pattern Anal. Mach. Intell., 1999)

Transport of wireless video using separate, concatenated, and joint source-channel coding. - Robert E. Van Dyck; David J. Miller 001 (Proc. IEEE, 1999)

Improved image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh. - MoonSeo Park; David J. Miller 001 (IEEE Trans. Image Process., 1999)

Critic-driven ensemble classification. - David J. Miller 001; Lian Yan (IEEE Trans. Signal Process., 1999)

Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification. - David J. Miller 001; Lian Yan (Neural Comput., 2000)

Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation. - MoonSeo Park; David J. Miller 001 (IEEE Trans. Commun., 2000)

General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle. - Lian Yan; David J. Miller 001 (IEEE Trans. Neural Networks Learn. Syst., 2000)

Locally optimal joint encoding of image transform coefficients. - Piya Bunyaratavej; David J. Miller 001 (ICASSP, 2001)

Mobile multimedia services for third generation communications systems. - A. Ravindran; Hang Liu; Izzet Agoren; Alex J. Lackpour; David J. Miller 001; Mohsen Kavehrad; John F. Doherty (VTC Fall, 2001)

A sequence-based generalization of mean-field annealing using the Forward/Backward algorithm: Application to image segmentation. - David J. Miller 001; Piya Bunyaratavej; Qi Zhao (ICASSP, 2002)

Hybrid fractal zerotree wavelet image coding. - Taekon Kim; Robert E. Van Dyck; David J. Miller 001 (Signal Process. Image Commun., 2002)

An iterative hillclimbing algorithm for discrete optimization on images: application to joint encoding of image transform coefficients. - Piya Bunyaratavej; David J. Miller 001 (IEEE Signal Process. Lett., 2002)

A mixture model and EM algorithm for robust classification, outlier rejection, and class discovery. - David J. Miller 001; John Browning (ICASSP (2), 2003)

A mixture model framework for class discovery and outlier detection in mixed labeled/unlabeled data sets. - David J. Miller 001; John Browning (NNSP, 2003)

A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets. - David J. Miller 001; John Browning (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

A sequence-based extension of mean-field annealing using the forward/backward algorithm: application to image segmentation. - David J. Miller 001; Qi Zhao (IEEE Trans. Signal Process., 2003)

A deterministic, annealing-based approach for learning and model selection in finite mixture models. - Qi Zhao; David J. Miller 001 (ICASSP (5), 2004)

Cyber defense technology networking and evaluation. - Ruzena Bajcsy; Terry Benzel; Matt Bishop; Robert Braden; Carla E. Brodley; Sonia Fahmy; Sally Floyd; Wes Hardaker; Anthony D. Joseph; George Kesidis; Karl N. Levitt; Robert Lindell; et al. (Commun. ACM, 2004)

Joint source-channel decoding of predictively and nonpredictively encoded sources: a two-stage estimation approach. - David J. Miller 001; Elias S. G. Carotti; Yu-Wei Wang; Juan Carlos De Martin (IEEE Trans. Commun., 2004)

A Maximum Entropy Approach for Collaborative Filtering. - John Browning; David J. Miller 001 (J. VLSI Signal Process., 2004)

Guest Editorial for Special Issue on Machine Learning for Signal Processing. - David J. Miller 001; Tülay Adali; Jan Larsen; Marc M. Van Hulle (J. VLSI Signal Process., 2004)

Hierarchical shaped deficit round-robin scheduling. - Soranun Jiwasurat; George Kesidis; David J. Miller 001 (GLOBECOM, 2005)

Semisupervised learning of mixture models with class constraints. - Qi Zhao; David J. Miller 001 (ICASSP (5), 2005)

Mixture Modeling with Pairwise, Instance-Level Class Constraints. - Qi Zhao; David J. Miller 001 (Neural Comput., 2005)

Learning the Tree of Phenotypes Using Genomic Data and VISDA. - Yuanjian Feng; Zuyi Wang; Yitan Zhu; Jianhua Xuan; David J. Miller 001 (BIBE, 2006)

Transductive Methods for Distributed Ensemble Classification. - David J. Miller 001; Siddharth Pal (CISS, 2006)

Phenotypic-Specific Gene Module Discovery using a Diagnostic Tree and caBIGTM VISDA. - Yitan Zhu; Zuyi Wang; Yuanjian Feng; Jianhua Xuan; David J. Miller 001; Eric P. Hoffman; Yue Wang 021 (EMBC, 2006)

Polymorphic worm detection and defense: system design, experimental methodology, and data resources. - Jisheng Wang; Ihab Hamadeh; George Kesidis; David J. Miller 001 (LSAD@SIGCOMM, 2006)

Efficient Mining of the Multidimensional Traffic Cluster Hierarchy for Digesting, Visualization, and Anomaly Identification. - Jisheng Wang; David J. Miller 001; George Kesidis (IEEE J. Sel. Areas Commun., 2006)

Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection. - Michael W. Graham; David J. Miller 001 (IEEE Trans. Signal Process., 2006)

Transductive Methods for the Distributed Ensemble Classification Problem. - David J. Miller 001; Siddharth Pal (Neural Comput., 2007)

Guest Editorial for Special Issue on the 2005 IEEE Workshop on Machine Learning for Signal Processing. - David J. Miller 001; Deniz Erdogmus (J. VLSI Signal Process., 2007)

An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification. - Siddharth Pal; David J. Miller 001 (J. VLSI Signal Process., 2007)

A transductive extension of maximum entropy/iterative scaling for decision aggregation in distributed classification. - David J. Miller 001; Yanxin Zhang; George Kesidis (ICASSP, 2008)

caBIGTM VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data. - Yitan Zhu; Huai Li; David J. Miller 001; Zuyi Wang; Jianhua Xuan; Robert Clarke; Eric P. Hoffman; Yue Joseph Wang (BMC Bioinform., 2008)

Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling. - David J. Miller 001; Yanxin Zhang; George Kesidis (EURASIP J. Adv. Signal Process., 2008)

Extensions of transductive learning for distributed ensemble classification and application to biometric authentication. - David J. Miller 001; Siddharth Pal; Yue Joseph Wang (Neurocomputing, 2008)

A study on the feasibility of low probability of intercept sonar. - J. Daniel Park; David J. Miller 001; John F. Doherty; Stephen C. Thompson (CISS, 2009)

An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. - David J. Miller 001; Yanxin Zhang; Guoqiang Yu; Yongmei Liu 003; Li Chen 018; Carl D. Langefeld; David M. Herrington; Yue Joseph Wang (Bioinform., 2009)

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer. - Don C. Bigler; Yaman Aksu; David J. Miller 001; Qing X. Yang (Comput. Methods Programs Biomed., 2009)

Feasibility of range estimation using sonar LPI. - J. Daniel Park; David J. Miller 001; John F. Doherty; Stephen C. Thompson (CISS, 2010)

Improved Fine-Grained Component-Conditional Class Labeling with Active Learning. - David J. Miller 001; Chu-Fang Lin; George Kesidis; Christopher M. Collins 002 (ICMLA, 2010)

Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases. - Guoqiang Yu; Yuanjian Feng; David J. Miller 001; Jianhua Xuan; Eric P. Hoffman; Robert Clarke; Ben Davidson; Ie-Ming Shih; Yue Joseph Wang (J. Mach. Learn. Res., 2010)

Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. - Scott C. Markley; David J. Miller 001 (IEEE J. Sel. Top. Signal Process., 2010)

Uninterpreted Schema Matching with Embedded Value Mapping under Opaque Column Names and Data Values. - Anuj R. Jaiswal; David J. Miller 001; Prasenjit Mitra (IEEE Trans. Knowl. Data Eng., 2010)

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions. - Yaman Aksu; David J. Miller 001; George Kesidis; Qing X. Yang (IEEE Trans. Neural Networks, 2010)

Salting Public Traces with Attack Traffic to Test Flow Classifiers. - Z. Berkay Celik; Jayaram Raghuram; George Kesidis; David J. Miller 001 (CSET, 2011)

A Flow Classifier with Tamper-Resistant Features and an Evaluation of Its Portability to New Domains. - Guixi Zou; George Kesidis; David J. Miller 001 (IEEE J. Sel. Areas Commun., 2011)

Trends in Machine Learning for Signal Processing [In the Spotlight]. - Tülay Adali; David J. Miller 001; Konstantinos I. Diamantaras; Jan Larsen (IEEE Signal Process. Mag., 2011)

Semisupervised domain adaptation for mixture model based classifiers. - Jayaram Raghuram; David J. Miller 001; George Kesidis (CISS, 2012)

Sequential anomaly detection in a batch with growing number of tests: Application to network intrusion detection. - David J. Miller 001; Fatih Kocak; George Kesidis (MLSP, 2012)

An Overview of Population Genetic Data Simulation. - Xiguo Yuan; David J. Miller 001; Junying Zhang; David M. Herrington; Yue Wang 021 (J. Comput. Biol., 2012)

Improved Generative Semisupervised Learning Based on Finely Grained Component-Conditional Class Labeling. - David J. Miller 001; Jayaram Raghuram; George Kesidis; Christopher M. Collins 002 (Neural Comput., 2012)

Nonlinear System Modeling With Random Matrices: Echo State Networks Revisited. - Bai Zhang; David J. Miller 001; Yue Joseph Wang (IEEE Trans. Neural Networks Learn. Syst., 2012)

The predictive value of young and old links in a social network. - Hung-Hsuan Chen; David J. Miller 001; C. Lee Giles (DBSocial, 2013)

Defeating Tyranny of the Masses in Crowdsourcing: Accounting for Low-Skilled and Adversarial Workers. - Aditya Kurve; David J. Miller 001; George Kesidis (GameSec, 2013)

A generative semi-supervised model for multi-view learning when some views are label-free. - Gaole Jin; Raviv Raich; David J. Miller 001 (ICASSP, 2013)

Latent Interest-Group Discovery and Management by Peer-to-Peer Online Social Networks. - Jianping He 004; David J. Miller 001; George Kesidis (SocialCom, 2013)

Multicategory Crowdsourcing Accounting for Plurality in Worker Skill and Intention, Task Difficulty, and Task Heterogeneity. - Aditya Kurve; David J. Miller 001; George Kesidis (CoRR, 2013)

Schema matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields. - Anuj R. Jaiswal; David J. Miller 001; Prasenjit Mitra (ACM Trans. Database Syst., 2013)

Detecting anomalous latent classes in a batch of network traffic flows. - Fatih Kocak; David J. Miller 001; George Kesidis (CISS, 2014)

A Bidding Game for Generators in the Presence of Flexible Demand. - Jayaram Raghuram; George Kesidis; Christopher Griffin 001; Karl N. Levitt; David J. Miller 001; Jeff Rowe; Anna Scaglione (Feedback Computing, 2014)

Sparse topic models by parameter sharing. - Hossein Soleimani; David J. Miller 001 (MLSP, 2014)

Generation bidding game with flexible demand. - Yuquan Shan; Jayaram Raghuram; George Kesidis; Christopher Griffin 001; Karl N. Levitt; David J. Miller 001; Jeff Rowe; Anna Scaglione (CoRR, 2014)

Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning. - Jayaram Raghuram; David J. Miller 001; George Kesidis (IEEE Trans. Neural Networks Learn. Syst., 2014)

Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data. - Zhicong Qiu; David J. Miller 001; George Kesidis (MLSP, 2015)

Generation bidding game with potentially false attestation of flexible demand. - Yuquan Shan; Jayaram Raghuram; George Kesidis; David J. Miller 001; Anna Scaglione; Jeff Rowe; Karl N. Levitt (EURASIP J. Adv. Signal Process., 2015)

On an Objective Basis for the Maximum Entropy Principle. - David J. Miller 001; Hossein Soleimani (Entropy, 2015)

Optimizing cluster formation in super-peer networks via local incentive design. - Aditya Kurve; Christopher Griffin 001; David J. Miller 001; George Kesidis (Peer-to-Peer Netw. Appl., 2015)

Multicategory Crowdsourcing Accounting for Variable Task Difficulty, Worker Skill, and Worker Intention. - Aditya Kurve; David J. Miller 001; George Kesidis (IEEE Trans. Knowl. Data Eng., 2015)

Parsimonious Topic Models with Salient Word Discovery. - Hossein Soleimani; David J. Miller 001 (IEEE Trans. Knowl. Data Eng., 2015)

Semi-supervised Multi-Label Topic Models for Document Classification and Sentence Labeling. - Hossein Soleimani; David J. Miller 001 (CIKM, 2016)

Content-Driven Detection of Cyberbullying on the Instagram Social Network. - Haoti Zhong; Hao Li; Anna Cinzia Squicciarini; Sarah Michele Rajtmajer; Christopher Griffin 001; David J. Miller 001; Cornelia Caragea (IJCAI, 2016)

Exploiting the value of class labels in topic models for semi-supervised document classification. - Hossein Soleimani; David J. Miller 001 (IJCNN, 2016)

FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data. - Yinxue Wang; Guilai Shi; David J. Miller 001; Yizhi Wang; Gerard Broussard; Yue Wang 021; Lin Tian; Guoqiang Yu (ISBI, 2016)

IP-level fast re-routing for robustness to mass failure events using a hybrid bandwidth and reliability cost metric. - George Kesidis; David J. Miller 001; Zhicong Qiu (MILCOM, 2016)

Graphical Time Warping for Joint Alignment of Multiple Curves. - Yizhi Wang; David J. Miller 001; Kira Poskanzer; Yue Wang 021; Lin Tian; Guoqiang Yu (NIPS, 2016)

A simulation framework for uneconomic virtual bidding in day-ahead electricity markets: Short talk. - Yuquan Shan; Chiara Lo Prete; George Kesidis; David J. Miller 001 (SIGMETRICS Perform. Evaluation Rev., 2016)

ATD: Anomalous Topic Discovery in High Dimensional Discrete Data. - Hossein Soleimani; David J. Miller 001 (IEEE Trans. Knowl. Data Eng., 2016)

A simulation framework for uneconomic virtual bidding in day-ahead electricity markets. - Yuquan Shan; Chiara Lo Prete; George Kesidis; David J. Miller 001 (ACC, 2017)

Flow based botnet detection through semi-supervised active learning. - Zhicong Qiu; David J. Miller 001; George Kesidis (ICASSP, 2017)

A Group-Based Personalized Model for Image Privacy Classification and Labeling. - Haoti Zhong; Anna Cinzia Squicciarini; David J. Miller 001; Cornelia Caragea (IJCAI, 2017)

A locally optimal algorithm for estimating a generating partition from an observed time series. - David J. Miller 001; Najah F. Ghalyan; Asok Ray (MLSP, 2017)

Adversarial learning: A critical review and active learning study. - David J. Miller 001; Xinyi Hu; Zhicong Qiu; George Kesidis (MLSP, 2017)

Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data. - Yinxue Wang; Guilai Shi; David J. Miller 001; Yizhi Wang; Congchao Wang; Gerard Broussard; Yue Wang 021; Lin Tian; Guoqiang Yu (Frontiers Neuroinformatics, 2017)

Semisupervised, Multilabel, Multi-Instance Learning for Structured Data. - Hossein Soleimani; David J. Miller 001 (Neural Comput., 2017)

A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes. - Zhicong Qiu; David J. Miller 001; George Kesidis (IEEE Trans. Neural Networks Learn. Syst., 2017)

Toward Automated Multiparty Privacy Conflict Detection. - Haoti Zhong; Anna Cinzia Squicciarini; David J. Miller 001 (CIKM, 2018)

Locally optimal, delay-tolerant predictive source coding. - Zhen Xiang; David J. Miller 001 (CISS, 2018)

Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD). - David J. Miller 001; George Kesidis; Zhicong Qiu (MLSP, 2018)

Anomaly Detection of Attacks (Ada) on DNN Classifiers at Test Time. - David J. Miller 001; Yujia Wang; George Kesidis (MLSP, 2018)

Flexible Inference for Cyberbully Incident Detection. - Haoti Zhong; David J. Miller 001; Anna Cinzia Squicciarini (ECML/PKDD (3), 2018)

Learned Iterative Decoding for Lossy Image Compression Systems. - Alexander G. Ororbia II; Ankur Arjun Mali; Jian Wu 006; Scott O'Connell; David J. Miller 001; C. Lee Giles (CoRR, 2018)

A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters. - David J. Miller 001; Xinyi Hu; Zhen Xiang; George Kesidis (CoRR, 2018)

A Locally Optimal Algorithm for Estimating a Generating Partition from an Observed Time Series and Its Application to Anomaly Detection. - Najah F. Ghalyan; David J. Miller 001; Asok Ray (Neural Comput., 2018)

Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion. - Chia-Hsiang Lin; Chong-Yung Chi; Lulu Chen; David J. Miller 001; Yue Wang 021 (IEEE Trans. Neural Networks Learn. Syst., 2018)

Toward Image Privacy Classification and Spatial Attribution of Private Content. - Haoti Zhong; Hao Li; Anna Cinzia Squicciarini; Sarah Michele Rajtmajer; David J. Miller 001 (IEEE BigData, 2019)

Learned Neural Iterative Decoding for Lossy Image Compression Systems. - Alexander G. Ororbia II; Ankur Arjun Mali; Jian Wu 006; Scott O'Connell; William Dreese; David J. Miller 001; C. Lee Giles (DCC, 2019)

When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. - Yujia Wang; David J. Miller 001; George Kesidis (ICASSP, 2019)

A Benchmark Study Of Backdoor Data Poisoning Defenses For Deep Neural Network Classifiers And A Novel Defense. - Zhen Xiang; David J. Miller 001; George Kesidis (MLSP, 2019)

Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks. - David J. Miller 001; Zhen Xiang; George Kesidis (CoRR, 2019)

Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers. - George Kesidis; David J. Miller 001 (CoRR, 2019)

Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. - Zhen Xiang; David J. Miller 001; George Kesidis (CoRR, 2019)

When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. - David J. Miller 001; Yujia Wang; George Kesidis (Neural Comput., 2019)

Exploiting the value of class labels on high-dimensional feature spaces: topic models for semi-supervised document classification. - Hossein Soleimani; David J. Miller 001 (Pattern Anal. Appl., 2019)

Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. - Haoti Zhong; Cong Liao; Anna Cinzia Squicciarini; Sencun Zhu; David J. Miller 001 (CODASPY, 2020)

A Scalable Mixture Model Based Defense Against Data Poisoning Attacks on Classifiers. - Xi Li 015; David J. Miller 001; Zhen Xiang; George Kesidis (DDDAS, 2020)

Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification. - Zhen Xiang; David J. Miller 001; George Kesidis (ICASSP, 2020)

Revealing Perceptible Backdoors in DNNs, Without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. - Zhen Xiang; David J. Miller 001; Hang Wang; George Kesidis (MLSP, 2020)

Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion. - Hang Wang; David J. Miller 001 (Entropy, 2020)

Scanning the Issue. - Weiqiang Liu 001; Maximilian John; Andreas Karrenbauer; Adam Allerhand; Fabrizio Lombardi; Michael Shulte; David J. Miller 001; Zhen Xiang; George Kesidis; Antti Oulasvirta; Niraj Ramesh Dayama; Morteza Shiripour (Proc. IEEE, 2020)

Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks. - David J. Miller 001; Zhen Xiang; George Kesidis (Proc. IEEE, 2020)

L-Red: Efficient Post-Training Detection of Imperceptible Backdoor Attacks Without Access to the Training Set. - Zhen Xiang; David J. Miller 001; George Kesidis (ICASSP, 2021)

A Backdoor Attack against 3D Point Cloud Classifiers. - Zhen Xiang; David J. Miller 001; Siheng Chen; Xi Li 015; George Kesidis (ICCV, 2021)

Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing. - Zhen Xiang; David J. Miller 001; George Kesidis (Comput. Secur., 2021)

Anomaly Detection of Test-Time Evasion Attacks using Class-conditional Generative Adversarial Networks. - Hang Wang; David J. Miller 001; George Kesidis (CoRR, 2021)

Robust and Active Learning for Deep Neural Network Regression. - Xi Li 015; George Kesidis; David J. Miller 001; Maxime Bergeron; Ryan Ferguson; Vladimir Lucic (CoRR, 2021)

Backdoor Attack and Defense for Deep Regression. - Xi Li 015; George Kesidis; David J. Miller 001; Vladimir Lucic (CoRR, 2021)

Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set. - Zhen Xiang; David J. Miller 001; Hang Wang; George Kesidis (Neural Comput., 2021)

Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. - Xi Li 015; Zhen Xiang; David J. Miller 001; George Kesidis (ICASSP, 2022)

Detecting Backdoor Attacks against Point Cloud Classifiers. - Zhen Xiang; David J. Miller 001; Siheng Chen; Xi Li 015; George Kesidis (ICASSP, 2022)

Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. - Zhen Xiang; David J. Miller 001; George Kesidis (ICLR, 2022)

Universal Post-Training Backdoor Detection. - Hang Wang; Zhen Xiang; David J. Miller 001; George Kesidis (CoRR, 2022)

Deep Learning in Biological Image and Signal Processing [From the Guest Editors]. - Erik Meijering; Vince D. Calhoun; Gloria Menegaz; David J. Miller 001; Jong Chul Ye (IEEE Signal Process. Mag., 2022)

Detection of Backdoors in Trained Classifiers Without Access to the Training Set. - Zhen Xiang; David J. Miller 001; George Kesidis (IEEE Trans. Neural Networks Learn. Syst., 2022)

Training Set Cleansing of Backdoor Poisoning by Self-Supervised Representation Learning. - Hang Wang; Sahar Karami; Ousmane Dia; Hippolyt Ritter; Ehsan Emamjomeh-Zadeh; Jiahui Chen; Zhen Xiang; David J. Miller 001; George Kesidis (ICASSP, 2023)

A BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers. - Xi Li 015; David J. Miller 001; Zhen Xiang; George Kesidis (MLSP, 2023)

Anomaly detection of adversarial examples using class-conditional generative adversarial networks. - Hang Wang; David J. Miller 001; George Kesidis (Comput. Secur., 2023)

Backdoor Mitigation by Correcting the Distribution of Neural Activations. - Xi Li 015; Zhen Xiang; David J. Miller 001; George Kesidis (CoRR, 2023)

Post-Training Overfitting Mitigation in DNN Classifiers. - Hang Wang; David J. Miller 001; George Kesidis (CoRR, 2023)

Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection. - Hang Wang; Zhen Xiang; David J. Miller 001; George Kesidis (MLSP, 2024)

MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic. - Hang Wang; Zhen Xiang; David J. Miller 001; George Kesidis (SP, 2024)

Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks. - Xi Li 015; Hang Wang; David J. Miller 001; George Kesidis (CoRR, 2024)

On Trojans in Refined Language Models. - Jayaram Raghuram; George Kesidis; David J. Miller 001 (CoRR, 2024)

BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers: A Comprehensive Study. - Xi Li 015; David J. Miller 001; Zhen Xiang; George Kesidis (IEEE Trans. Knowl. Data Eng., 2024)

Correcting the distribution of batch normalization signals for Trojan mitigation. - Xi Li 015; Zhen Xiang; David J. Miller 001; George Kesidis (Neurocomputing, 2025)