David J. Miller

181 papers A* 5A 2B 7C 3Misc 23Journal 105Unranked 36

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Backdoor Inversion in Neural-Activation Space. MLSP Guangmingmei Yang, Xi Li, Hang Wang, George Kesidis, David J. Miller
2025	J	`jnl`	Correcting the distribution of batch normalization signals for Trojan mitigation. Neurocomputing Xi Li, Zhen Xiang, David J. Miller, George Kesidis
2025	J	`jnl`	Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization. CoRR Guangmingmei Yang, David J. Miller, George Kesidis
2025	J	`jnl`	Inverting Trojans in LLMs. CoRR Zhengxing Li, Guangmingmei Yang, Jayaram Raghuram, David J. Miller, George Kesidis
2025	J	`jnl`	Maximum Margin-Based Activation Clipping for Posttraining Overfitting Mitigation in DNN Classifiers. IEEE Trans. Artif. Intell. Hang Wang, David J. Miller, George Kesidis
2024	J	`jnl`	BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers: A Comprehensive Study. IEEE Trans. Knowl. Data Eng. Xi Li, David J. Miller, Zhen Xiang, George Kesidis
2024	—	`conf`	Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection. MLSP Hang Wang, Zhen Xiang, David J. Miller, George Kesidis
2024	A*	`conf`	MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic. SP Hang Wang, Zhen Xiang, David J. Miller, George Kesidis
2024	J	`jnl`	On Trojans in Refined Language Models. CoRR Jayaram Raghuram, George Kesidis, David J. Miller
2024	J	`jnl`	Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks. CoRR Xi Li, Hang Wang, David J. Miller, George Kesidis
2023	—	`conf`	A BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers. MLSP Xi Li, David J. Miller, Zhen Xiang, George Kesidis
2023	J	`jnl`	Anomaly detection of adversarial examples using class-conditional generative adversarial networks. Comput. Secur. Hang Wang, David J. Miller, George Kesidis
2023	J	`jnl`	Backdoor Mitigation by Correcting the Distribution of Neural Activations. CoRR Xi Li, Zhen Xiang, David J. Miller, George Kesidis
2023	J	`jnl`	Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection. CoRR Hang Wang, Zhen Xiang, David J. Miller, George Kesidis
2023	J	`jnl`	Post-Training Overfitting Mitigation in DNN Classifiers. CoRR Hang Wang, David J. Miller, George Kesidis
2023	Misc	`conf`	Training Set Cleansing of Backdoor Poisoning by Self-Supervised Representation Learning. ICASSP Hang Wang, Sahar Karami, Ousmane Dia, Hippolyt Ritter, Ehsan Emamjomeh-Zadeh, Jiahui Chen, Zhen Xiang, David J. Miller, George Kesidis
2022	J	`jnl`	Deep Learning in Biological Image and Signal Processing [From the Guest Editors]. IEEE Signal Process. Mag. Erik Meijering, Vince D. Calhoun, Gloria Menegaz, David J. Miller, Jong Chul Ye
2022	Misc	`conf`	Detecting Backdoor Attacks against Point Cloud Classifiers. ICASSP Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis
2022	J	`jnl`	Detection of Backdoors in Trained Classifiers Without Access to the Training Set. IEEE Trans. Neural Networks Learn. Syst. Zhen Xiang, David J. Miller, George Kesidis
2022	A*	`conf`	Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. ICLR Zhen Xiang, David J. Miller, George Kesidis
2022	J	`jnl`	Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. CoRR Zhen Xiang, David J. Miller, George Kesidis
2022	Misc	`conf`	Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. ICASSP Xi Li, Zhen Xiang, David J. Miller, George Kesidis
2022	J	`jnl`	Training set cleansing of backdoor poisoning by self-supervised representation learning. CoRR Hang Wang, Sahar Karami, Ousmane Dia, Hippolyt Ritter, Ehsan Emamjomeh-Zadeh, Jiahui Chen, Zhen Xiang, David J. Miller, George Kesidis
2022	J	`jnl`	Universal Post-Training Backdoor Detection. CoRR Hang Wang, Zhen Xiang, David J. Miller, George Kesidis
2021	J	`jnl`	A BIC based Mixture Model Defense against Data Poisoning Attacks on Classifiers. CoRR Xi Li, David J. Miller, Zhen Xiang, George Kesidis
2021	A*	`conf`	A Backdoor Attack against 3D Point Cloud Classifiers. ICCV Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis
2021	J	`jnl`	A Backdoor Attack against 3D Point Cloud Classifiers. CoRR Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis
2021	J	`jnl`	Anomaly Detection of Test-Time Evasion Attacks using Class-conditional Generative Adversarial Networks. CoRR Hang Wang, David J. Miller, George Kesidis
2021	J	`jnl`	Backdoor Attack and Defense for Deep Regression. CoRR Xi Li, George Kesidis, David J. Miller, Vladimir Lucic
2021	J	`jnl`	Detecting Backdoor Attacks Against Point Cloud Classifiers. CoRR Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis
2021	J	`jnl`	Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set. Neural Comput. Zhen Xiang, David J. Miller, Hang Wang, George Kesidis
2021	Misc	`conf`	L-Red: Efficient Post-Training Detection of Imperceptible Backdoor Attacks Without Access to the Training Set. ICASSP Zhen Xiang, David J. Miller, George Kesidis
2021	J	`jnl`	Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing. Comput. Secur. Zhen Xiang, David J. Miller, George Kesidis
2021	J	`jnl`	Robust and Active Learning for Deep Neural Network Regression. CoRR Xi Li, George Kesidis, David J. Miller, Maxime Bergeron, Ryan Ferguson, Vladimir Lucic
2021	J	`jnl`	Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. CoRR Xi Li, Zhen Xiang, David J. Miller, George Kesidis
2020	—	`conf`	A Scalable Mixture Model Based Defense Against Data Poisoning Attacks on Classifiers. DDDAS Xi Li, David J. Miller, Zhen Xiang, George Kesidis
2020	J	`jnl`	Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks. Proc. IEEE David J. Miller, Zhen Xiang, George Kesidis
2020	—	`conf`	Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. CODASPY Haoti Zhong, Cong Liao, Anna Cinzia Squicciarini, Sencun Zhu, David J. Miller
2020	J	`jnl`	Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion. Entropy Hang Wang, David J. Miller
2020	J	`jnl`	L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set. CoRR Zhen Xiang, David J. Miller, George Kesidis
2020	Misc	`conf`	Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification. ICASSP Zhen Xiang, David J. Miller, George Kesidis
2020	—	`conf`	Revealing Perceptible Backdoors in DNNs, Without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. MLSP Zhen Xiang, David J. Miller, Hang Wang, George Kesidis
2020	J	`jnl`	Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing. CoRR Zhen Xiang, David J. Miller, George Kesidis
2020	J	`jnl`	Scanning the Issue. Proc. IEEE Weiqiang Liu, Maximilian John, Andreas Karrenbauer, Adam Allerhand, Fabrizio Lombardi, Michael Shulte, David J. Miller, Zhen Xiang, George Kesidis, Antti Oulasvirta, Niraj Ramesh Dayama, Morteza Shiripour
2019	—	`conf`	A Benchmark Study Of Backdoor Data Poisoning Defenses For Deep Neural Network Classifiers And A Novel Defense. MLSP Zhen Xiang, David J. Miller, George Kesidis
2019	J	`jnl`	Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks. CoRR David J. Miller, Zhen Xiang, George Kesidis
2019	J	`jnl`	Exploiting the value of class labels on high-dimensional feature spaces: topic models for semi-supervised document classification. Pattern Anal. Appl. Hossein Soleimani, David J. Miller
2019	B	`conf`	Learned Neural Iterative Decoding for Lossy Image Compression Systems. DCC Alexander G. Ororbia II, Ankur Mali, Jian Wu, Scott O'Connell, William Dreese, David J. Miller, C. Lee Giles
2019	J	`jnl`	Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers. CoRR George Kesidis, David J. Miller
2019	J	`jnl`	Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification. CoRR Zhen Xiang, David J. Miller, George Kesidis
2019	J	`jnl`	Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. CoRR Zhen Xiang, David J. Miller, George Kesidis
2019	—	`conf`	Toward Image Privacy Classification and Spatial Attribution of Private Content. IEEE BigData Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, David J. Miller
2019	J	`jnl`	When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. Neural Comput. David J. Miller, Yujia Wang, George Kesidis
2019	Misc	`conf`	When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. ICASSP Yujia Wang, David J. Miller, George Kesidis
2018	J	`jnl`	A Locally Optimal Algorithm for Estimating a Generating Partition from an Observed Time Series and Its Application to Anomaly Detection. Neural Comput. Najah F. Ghalyan, David J. Miller, Asok Ray
2018	J	`jnl`	A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters. CoRR David J. Miller, Xinyi Hu, Zhen Xiang, George Kesidis
2018	—	`conf`	Anomaly Detection of Attacks (Ada) on DNN Classifiers at Test Time. MLSP David J. Miller, Yujia Wang, George Kesidis
2018	J	`jnl`	Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. CoRR Cong Liao, Haoti Zhong, Anna Cinzia Squicciarini, Sencun Zhu, David J. Miller
2018	J	`jnl`	Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion. IEEE Trans. Neural Networks Learn. Syst. Chia-Hsiang Lin, Chong-Yung Chi, Lulu Chen, David J. Miller, Yue Wang
2018	—	`conf`	Flexible Inference for Cyberbully Incident Detection. ECML/PKDD (3) Haoti Zhong, David J. Miller, Anna Cinzia Squicciarini
2018	J	`jnl`	Learned Iterative Decoding for Lossy Image Compression Systems. CoRR Alexander G. Ororbia II, Ankur Mali, Jian Wu, Scott O'Connell, David J. Miller, C. Lee Giles
2018	Misc	`conf`	Locally optimal, delay-tolerant predictive source coding. CISS Zhen Xiang, David J. Miller
2018	A	`conf`	Toward Automated Multiparty Privacy Conflict Detection. CIKM Haoti Zhong, Anna Cinzia Squicciarini, David J. Miller
2018	—	`conf`	Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD). MLSP David J. Miller, George Kesidis, Zhicong Qiu
2018	J	`jnl`	When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. CoRR Yujia Wang, David J. Miller, George Kesidis
2017	A*	`conf`	A Group-Based Personalized Model for Image Privacy Classification and Labeling. IJCAI Haoti Zhong, Anna Cinzia Squicciarini, David J. Miller, Cornelia Caragea
2017	J	`jnl`	A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes. IEEE Trans. Neural Networks Learn. Syst. Zhicong Qiu, David J. Miller, George Kesidis
2017	—	`conf`	A locally optimal algorithm for estimating a generating partition from an observed time series. MLSP David J. Miller, Najah F. Ghalyan, Asok Ray
2017	C	`conf`	A simulation framework for uneconomic virtual bidding in day-ahead electricity markets. ACC Yuquan Shan, Chiara Lo Prete, George Kesidis, David J. Miller
2017	J	`jnl`	Adversarial Learning: A Critical Review and Active Learning Study. CoRR David J. Miller, Xinyi Hu, Zhicong Qiu, George Kesidis
2017	—	`conf`	Adversarial learning: A critical review and active learning study. MLSP David J. Miller, Xinyi Hu, Zhicong Qiu, George Kesidis
2017	J	`jnl`	Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data. Frontiers Neuroinformatics Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Congchao Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu
2017	Misc	`conf`	Flow based botnet detection through semi-supervised active learning. ICASSP Zhicong Qiu, David J. Miller, George Kesidis
2017	J	`jnl`	Semisupervised, Multilabel, Multi-Instance Learning for Structured Data. Neural Comput. Hossein Soleimani, David J. Miller
2017	J	`jnl`	When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. CoRR David J. Miller, Yujia Wang, George Kesidis
2016	J	`jnl`	A simulation framework for uneconomic virtual bidding in day-ahead electricity markets: Short talk. SIGMETRICS Perform. Evaluation Rev. Yuquan Shan, Chiara Lo Prete, George Kesidis, David J. Miller
2016	J	`jnl`	ATD: Anomalous Topic Discovery in High Dimensional Discrete Data. IEEE Trans. Knowl. Data Eng. Hossein Soleimani, David J. Miller
2016	A*	`conf`	Content-Driven Detection of Cyberbullying on the Instagram Social Network. IJCAI Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Griffin, David J. Miller, Cornelia Caragea
2016	B	`conf`	Exploiting the value of class labels in topic models for semi-supervised document classification. IJCNN Hossein Soleimani, David J. Miller
2016	—	`conf`	FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data. ISBI Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu
2016	—	`conf`	Graphical Time Warping for Joint Alignment of Multiple Curves. NIPS Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu
2016	—	`conf`	IP-level fast re-routing for robustness to mass failure events using a hybrid bandwidth and reliability cost metric. MILCOM George Kesidis, David J. Miller, Zhicong Qiu
2016	A	`conf`	Semi-supervised Multi-Label Topic Models for Document Classification and Sentence Labeling. CIKM Hossein Soleimani, David J. Miller
2015	J	`jnl`	ATD: Anomalous Topic Discovery in High Dimensional Discrete Data. CoRR Hossein Soleimani, David J. Miller
2015	J	`jnl`	Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data. CoRR Zhicong Qiu, David J. Miller, George Kesidis
2015	—	`conf`	Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data. MLSP Zhicong Qiu, David J. Miller, George Kesidis
2015	J	`jnl`	Generation bidding game with potentially false attestation of flexible demand. EURASIP J. Adv. Signal Process. Yuquan Shan, Jayaram Raghuram, George Kesidis, David J. Miller, Anna Scaglione, Jeff Rowe, Karl N. Levitt
2015	J	`jnl`	Multicategory Crowdsourcing Accounting for Variable Task Difficulty, Worker Skill, and Worker Intention. IEEE Trans. Knowl. Data Eng. Aditya Kurve, David J. Miller, George Kesidis
2015	J	`jnl`	On an Objective Basis for the Maximum Entropy Principle. Entropy David J. Miller, Hossein Soleimani
2015	J	`jnl`	Optimizing cluster formation in super-peer networks via local incentive design. Peer-to-Peer Netw. Appl. Aditya Kurve, Christopher Griffin, David J. Miller, George Kesidis
2015	J	`jnl`	Parsimonious Topic Models with Salient Word Discovery. IEEE Trans. Knowl. Data Eng. Hossein Soleimani, David J. Miller
2014	—	`conf`	A Bidding Game for Generators in the Presence of Flexible Demand. Feedback Computing Jayaram Raghuram, George Kesidis, Christopher Griffin, Karl N. Levitt, David J. Miller, Jeff Rowe, Anna Scaglione
2014	Misc	`conf`	Detecting anomalous latent classes in a batch of network traffic flows. CISS Fatih Kocak, David J. Miller, George Kesidis
2014	J	`jnl`	Generation bidding game with flexible demand. CoRR Yuquan Shan, Jayaram Raghuram, George Kesidis, Christopher Griffin, Karl N. Levitt, David J. Miller, Jeff Rowe, Anna Scaglione
2014	J	`jnl`	Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning. IEEE Trans. Neural Networks Learn. Syst. Jayaram Raghuram, David J. Miller, George Kesidis
2014	J	`jnl`	Parsimonious Topic Models with Salient Word Discovery. CoRR Hossein Soleimani, David J. Miller
2014	—	`conf`	Sparse topic models by parameter sharing. MLSP Hossein Soleimani, David J. Miller
2013	Misc	`conf`	A generative semi-supervised model for multi-view learning when some views are label-free. ICASSP Gaole Jin, Raviv Raich, David J. Miller
2013	—	`conf`	Defeating Tyranny of the Masses in Crowdsourcing: Accounting for Low-Skilled and Adversarial Workers. GameSec Aditya Kurve, David J. Miller, George Kesidis
2013	—	`conf`	Latent Interest-Group Discovery and Management by Peer-to-Peer Online Social Networks. SocialCom Jianping He, David J. Miller, George Kesidis
2013	J	`jnl`	Multicategory Crowdsourcing Accounting for Plurality in Worker Skill and Intention, Task Difficulty, and Task Heterogeneity. CoRR Aditya Kurve, David J. Miller, George Kesidis
2013	J	`jnl`	Schema matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields. ACM Trans. Database Syst. Anuj R. Jaiswal, David J. Miller, Prasenjit Mitra
2013	—	`conf`	The predictive value of young and old links in a social network. DBSocial Hung-Hsuan Chen, David J. Miller, C. Lee Giles
2012	J	`jnl`	An Overview of Population Genetic Data Simulation. J. Comput. Biol. Xiguo Yuan, David J. Miller, Junying Zhang, David M. Herrington, Yue Wang
2012	J	`jnl`	Improved Generative Semisupervised Learning Based on Finely Grained Component-Conditional Class Labeling. Neural Comput. David J. Miller, Jayaram Raghuram, George Kesidis, Christopher M. Collins
2012	J	`jnl`	Nonlinear System Modeling With Random Matrices: Echo State Networks Revisited. IEEE Trans. Neural Networks Learn. Syst. Bai Zhang, David J. Miller, Yue Joseph Wang
2012	Misc	`conf`	Semisupervised domain adaptation for mixture model based classifiers. CISS Jayaram Raghuram, David J. Miller, George Kesidis
2012	—	`conf`	Sequential anomaly detection in a batch with growing number of tests: Application to network intrusion detection. MLSP David J. Miller, Fatih Kocak, George Kesidis
2011	J	`jnl`	A Flow Classifier with Tamper-Resistant Features and an Evaluation of Its Portability to New Domains. IEEE J. Sel. Areas Commun. Guixi Zou, George Kesidis, David J. Miller
2011	—	`conf`	Salting Public Traces with Attack Traffic to Test Flow Classifiers. CSET Z. Berkay Celik, Jayaram Raghuram, George Kesidis, David J. Miller
2011	J	`jnl`	Trends in Machine Learning for Signal Processing [In the Spotlight]. IEEE Signal Process. Mag. Tülay Adali, David J. Miller, Konstantinos I. Diamantaras, Jan Larsen
2010	Misc	`conf`	Feasibility of range estimation using sonar LPI. CISS J. Daniel Park, David J. Miller, John F. Doherty, Stephen C. Thompson
2010	C	`conf`	Improved Fine-Grained Component-Conditional Class Labeling with Active Learning. ICMLA David J. Miller, Chu-Fang Lin, George Kesidis, Christopher M. Collins
2010	J	`jnl`	Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. IEEE J. Sel. Top. Signal Process. Scott C. Markley, David J. Miller
2010	J	`jnl`	Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions. IEEE Trans. Neural Networks Yaman Aksu, David J. Miller, George Kesidis, Qing X. Yang
2010	J	`jnl`	Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases. J. Mach. Learn. Res. Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Joseph Wang
2010	J	`jnl`	Uninterpreted Schema Matching with Embedded Value Mapping under Opaque Column Names and Data Values. IEEE Trans. Knowl. Data Eng. Anuj R. Jaiswal, David J. Miller, Prasenjit Mitra
2009	Misc	`conf`	A study on the feasibility of low probability of intercept sonar. CISS J. Daniel Park, David J. Miller, John F. Doherty, Stephen C. Thompson
2009	J	`jnl`	An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Bioinform. David J. Miller, Yanxin Zhang, Guoqiang Yu, Yongmei Liu, Li Chen, Carl D. Langefeld, David M. Herrington, Yue Joseph Wang
2009	J	`jnl`	STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer. Comput. Methods Programs Biomed. Don C. Bigler, Yaman Aksu, David J. Miller, Qing X. Yang
2008	Misc	`conf`	A transductive extension of maximum entropy/iterative scaling for decision aggregation in distributed classification. ICASSP David J. Miller, Yanxin Zhang, George Kesidis
2008	J	`jnl`	Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling. EURASIP J. Adv. Signal Process. David J. Miller, Yanxin Zhang, George Kesidis
2008	J	`jnl`	Extensions of transductive learning for distributed ensemble classification and application to biometric authentication. Neurocomputing David J. Miller, Siddharth Pal, Yue Joseph Wang
2008	J	`jnl`	caBIG BMC Bioinform. Yitan Zhu, Huai Li, David J. Miller, Zuyi Wang, Jianhua Xuan, Robert Clarke, Eric P. Hoffman, Yue Joseph Wang
2007	J	`jnl`	An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification. J. VLSI Signal Process. Siddharth Pal, David J. Miller
2007	J	`jnl`	Guest Editorial for Special Issue on the 2005 IEEE Workshop on Machine Learning for Signal Processing. J. VLSI Signal Process. David J. Miller, Deniz Erdogmus
2007	J	`jnl`	Transductive Methods for the Distributed Ensemble Classification Problem. Neural Comput. David J. Miller, Siddharth Pal
2006	J	`jnl`	Efficient Mining of the Multidimensional Traffic Cluster Hierarchy for Digesting, Visualization, and Anomaly Identification. IEEE J. Sel. Areas Commun. Jisheng Wang, David J. Miller, George Kesidis
2006	C	`conf`	Learning the Tree of Phenotypes Using Genomic Data and VISDA. BIBE Yuanjian Feng, Zuyi Wang, Yitan Zhu, Jianhua Xuan, David J. Miller
2006	—	`conf`	Phenotypic-Specific Gene Module Discovery using a Diagnostic Tree and caBIGTM VISDA. EMBC Yitan Zhu, Zuyi Wang, Yuanjian Feng, Jianhua Xuan, David J. Miller, Eric P. Hoffman, Yue Wang
2006	—	`conf`	Polymorphic worm detection and defense: system design, experimental methodology, and data resources. LSAD@SIGCOMM Jisheng Wang, Ihab Hamadeh, George Kesidis, David J. Miller
2006	Misc	`conf`	Transductive Methods for Distributed Ensemble Classification. CISS David J. Miller, Siddharth Pal
2006	J	`jnl`	Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection. IEEE Trans. Signal Process. Michael W. Graham, David J. Miller
2005	B	`conf`	Hierarchical shaped deficit round-robin scheduling. GLOBECOM Soranun Jiwasurat, George Kesidis, David J. Miller
2005	J	`jnl`	Mixture Modeling with Pairwise, Instance-Level Class Constraints. Neural Comput. Qi Zhao, David J. Miller
2005	—	`conf`	Semisupervised learning of mixture models with class constraints. ICASSP (5) Qi Zhao, David J. Miller
2004	J	`jnl`	A Maximum Entropy Approach for Collaborative Filtering. J. VLSI Signal Process. John Browning, David J. Miller
2004	—	`conf`	A deterministic, annealing-based approach for learning and model selection in finite mixture models. ICASSP (5) Qi Zhao, David J. Miller
2004	J	`jnl`	Cyber defense technology networking and evaluation. Commun. ACM 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, Peng Liu, David J. Miller, Russ Mundy, Clifford Neuman, Ron Ostrenga, Vern Paxson, Phillip A. Porras, Catherine Rosenberg, J. Doug Tygar, Shankar Sastry, Daniel F. Sterne, Shyhtsun Felix Wu
2004	J	`jnl`	Guest Editorial for Special Issue on Machine Learning for Signal Processing. J. VLSI Signal Process. David J. Miller, Tülay Adali, Jan Larsen, Marc M. Van Hulle
2004	J	`jnl`	Joint source-channel decoding of predictively and nonpredictively encoded sources: a two-stage estimation approach. IEEE Trans. Commun. David J. Miller, Elias S. G. Carotti, Yu-Wei Wang, Juan Carlos De Martin
2003	J	`jnl`	A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets. IEEE Trans. Pattern Anal. Mach. Intell. David J. Miller, John Browning
2003	—	`conf`	A mixture model and EM algorithm for robust classification, outlier rejection, and class discovery. ICASSP (2) David J. Miller, John Browning
2003	—	`conf`	A mixture model framework for class discovery and outlier detection in mixed labeled/unlabeled data sets. NNSP David J. Miller, John Browning
2003	J	`jnl`	A sequence-based extension of mean-field annealing using the forward/backward algorithm: application to image segmentation. IEEE Trans. Signal Process. David J. Miller, Qi Zhao
2002	Misc	`conf`	A sequence-based generalization of mean-field annealing using the Forward/Backward algorithm: Application to image segmentation. ICASSP David J. Miller, Piya Bunyaratavej, Qi Zhao
2002	J	`jnl`	An iterative hillclimbing algorithm for discrete optimization on images: application to joint encoding of image transform coefficients. IEEE Signal Process. Lett. Piya Bunyaratavej, David J. Miller
2002	J	`jnl`	Hybrid fractal zerotree wavelet image coding. Signal Process. Image Commun. Taekon Kim, Robert E. Van Dyck, David J. Miller
2001	Misc	`conf`	Locally optimal joint encoding of image transform coefficients. ICASSP Piya Bunyaratavej, David J. Miller
2001	—	`conf`	Mobile multimedia services for third generation communications systems. VTC Fall A. Ravindran, Hang Liu, Izzet Agoren, Alex J. Lackpour, David J. Miller, Mohsen Kavehrad, John F. Doherty
2000	J	`jnl`	Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification. Neural Comput. David J. Miller, Lian Yan
2000	J	`jnl`	General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle. IEEE Trans. Neural Networks Learn. Syst. Lian Yan, David J. Miller
2000	J	`jnl`	Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation. IEEE Trans. Commun. Moonseo Park, David J. Miller
1999	J	`jnl`	A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models. IEEE Trans. Pattern Anal. Mach. Intell. Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho
1999	B	`conf`	Approximate maximum entropy joint feature inference for discrete space classification. IJCNN David J. Miller, Lian Yan
1999	J	`jnl`	Critic-driven ensemble classification. IEEE Trans. Signal Process. David J. Miller, Lian Yan
1999	Misc	`conf`	Ensemble classification by critic-driven combining. ICASSP David J. Miller, Lian Yan
1999	B	`conf`	Improved Joint Source-Channel Decoding for Variable-Length Encoded Data Using Soft Decisions and MMSE Estimation. Data Compression Conference Moonseo Park, David J. Miller
1999	J	`jnl`	Improved image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh. IEEE Trans. Image Process. Moonseo Park, David J. Miller
1999	Misc	`conf`	Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation. ICASSP Moonseo Park, David J. Miller
1999	Misc	`conf`	Time series prediction via neural network inversion. ICASSP Lian Yan, David J. Miller
1999	J	`jnl`	Transport of wireless video using separate, concatenated, and joint source-channel coding. Proc. IEEE Robert E. Van Dyck, David J. Miller
1998	—	`conf`	A New Set Partitioning Method for Wavelet-based Image Coding. ICIP (1) Jeongjin Roh, David J. Miller
1998	J	`jnl`	A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models. IEEE Trans. Commun. David J. Miller, Moonseo Park
1998	J	`jnl`	Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier. Neural Comput. David J. Miller, Hasan S. Uyar
1997	Misc	`conf`	Deterministically annealed mixture of experts models for statistical regression. ICASSP Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho
1997	—	`conf`	Image Decoding Over Noisy Channels Using Minimum Mean-Squared Estimation and a Markov Mesh. ICIP (3) Moonseo Park, David J. Miller
1997	J	`jnl`	Low-delay optimal MAP state estimation in HMM's with application to symbol decoding. IEEE Signal Process. Lett. Moonseo Park, David J. Miller
1997	J	`jnl`	Mixture of experts regression modeling by deterministic annealing. IEEE Trans. Signal Process. Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho
1996	—	`conf`	A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data. NIPS David J. Miller, Hasan S. Uyar
1996	Misc	`conf`	A generalized VQ method for combined compression and estimation. ICASSP Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho
1996	J	`jnl`	A global optimization technique for statistical classifier design. IEEE Trans. Signal Process. David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho
1996	J	`jnl`	Entropy-constrained tree-structured vector quantizer design. IEEE Trans. Image Process. Kenneth Rose, David J. Miller, Allen Gersho
1996	J	`jnl`	Hierarchical, Unsupervised Learning with Growing via Phase Transitions. Neural Comput. David J. Miller, Kenneth Rose
1995	—	`conf`	An Information-theoretic Learning Algorithm for Neural Network Classification. NIPS David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho
1994	J	`jnl`	A non-greedy approach to tree-structured clustering. Pattern Recognit. Lett. David J. Miller, Kenneth Rose
1994	J	`jnl`	Combined source-channel vector quantization using deterministic annealing. IEEE Trans. Commun. David J. Miller, Kenneth Rose
1994	—	`conf`	Deterministic annealing for trellis quantizer and HMM design using Baum-Welch re-estimation. ICASSP (5) David J. Miller, Kenneth Rose, Philip A. Chou
1994	B	`conf`	Entropy-Constrained Tree-Structured Vector Quantizer Design by the Minimum Cross Entropy Principle. Data Compression Conference Kenneth Rose, David J. Miller, Allen Gersho
1993	B	`conf`	An Improved Sequential Search Multistage Vector Quantizer. Data Compression Conference David J. Miller, Kenneth Rose
1992	Misc	`conf`	Joint source-channel vector quantization using deterministic annealing. ICASSP David J. Miller, Kenneth Rose