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ORCIDDavid J. Miller 0001
Person information
- affiliation: Pennsylvania State University, University Park, PA, USA
- affiliation (PhD 1995): University of California, Santa Barbara, CA, USA
Other persons with the same name
- David J. Miller — disambiguation page
- David J. Miller 0002 — University of Nebraska, Lincoln
- David J. Miller 0003 — Sandia National Laboratories
- David J. Miller 0004 — IBM
- David J. Miller 0005 — University of Cambridge, UK
- David J. Miller 0006 — The College of Wooster, Department of Physics
- David J. Miller 0007 — University of Sydney
- David J. Miller 0008
![[0000-0003-0291-9531]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-0291-9531")
— James Cook University, Townsville, QLD, Australia
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Journal Articles
- 2023
[j68]Hang Wang, David J. Miller, George Kesidis:
Anomaly detection of adversarial examples using class-conditional generative adversarial networks. Comput. Secur. 124: 102956 (2023)- 2022
- [j67]Erik Meijering, Vince D. Calhoun, Gloria Menegaz, David J. Miller, Jong Chul Ye:
Deep Learning in Biological Image and Signal Processing [From the Guest Editors]. IEEE Signal Process. Mag. 39(2): 24-26 (2022) - [j66]Zhen Xiang, David J. Miller, George Kesidis:
Detection of Backdoors in Trained Classifiers Without Access to the Training Set. IEEE Trans. Neural Networks Learn. Syst. 33(3): 1177-1191 (2022) - 2021
- [j65]Zhen Xiang, David J. Miller, George Kesidis:
Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing. Comput. Secur. 106: 102280 (2021) - [j64]Zhen Xiang, David J. Miller, Hang Wang, George Kesidis:
Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set. Neural Comput. 33(5): 1329-1371 (2021) - 2020
- [j63]Hang Wang, David J. Miller:
Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion. Entropy 22(3): 326 (2020) - [j62]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:
Scanning the Issue. Proc. IEEE 108(3): 400-401 (2020) - [j61]David J. Miller, Zhen Xiang, George Kesidis:
Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks. Proc. IEEE 108(3): 402-433 (2020) - 2019
- [j60]David J. Miller, Yujia Wang, George Kesidis:
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. Neural Comput. 31(8): 1624-1670 (2019) - [j59]Hossein Soleimani, David J. Miller:
Exploiting the value of class labels on high-dimensional feature spaces: topic models for semi-supervised document classification. Pattern Anal. Appl. 22(2): 299-309 (2019) - 2018
- [j58]Najah F. Ghalyan, David J. Miller, Asok Ray:
A Locally Optimal Algorithm for Estimating a Generating Partition from an Observed Time Series and Its Application to Anomaly Detection. Neural Comput. 30(9) (2018) - [j57]Chia-Hsiang Lin, Chong-Yung Chi, Lulu Chen, David J. Miller, Yue Wang:
Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion. IEEE Trans. Neural Networks Learn. Syst. 29(9): 4022-4037 (2018) - 2017
- [j56]Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Congchao Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu:
Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data. Frontiers Neuroinformatics 11: 48 (2017) - [j55]Hossein Soleimani, David J. Miller:
Semisupervised, Multilabel, Multi-Instance Learning for Structured Data. Neural Comput. 29(4): 1053-1102 (2017) - [j54]Zhicong Qiu, David J. Miller, George Kesidis:
A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes. IEEE Trans. Neural Networks Learn. Syst. 28(4): 917-933 (2017) - 2016
- [j53]Yuquan Shan, Chiara Lo Prete, George Kesidis, David J. Miller:
A simulation framework for uneconomic virtual bidding in day-ahead electricity markets: Short talk. SIGMETRICS Perform. Evaluation Rev. 44(3): 30 (2016) - [j52]Hossein Soleimani, David J. Miller:
ATD: Anomalous Topic Discovery in High Dimensional Discrete Data. IEEE Trans. Knowl. Data Eng. 28(9): 2267-2280 (2016) - 2015
- [j51]Yuquan Shan, Jayaram Raghuram, George Kesidis, David J. Miller, Anna Scaglione, Jeff Rowe, Karl N. Levitt:
Generation bidding game with potentially false attestation of flexible demand. EURASIP J. Adv. Signal Process. 2015: 29 (2015) - [j50]David J. Miller, Hossein Soleimani:
On an Objective Basis for the Maximum Entropy Principle. Entropy 17(1): 401-406 (2015) - [j49]Aditya Kurve, Christopher Griffin, David J. Miller, George Kesidis:
Optimizing cluster formation in super-peer networks via local incentive design. Peer-to-Peer Netw. Appl. 8(1): 1-21 (2015) - [j48]Aditya Kurve, David J. Miller, George Kesidis:
Multicategory Crowdsourcing Accounting for Variable Task Difficulty, Worker Skill, and Worker Intention. IEEE Trans. Knowl. Data Eng. 27(3): 794-809 (2015) - [j47]Hossein Soleimani, David J. Miller:
Parsimonious Topic Models with Salient Word Discovery. IEEE Trans. Knowl. Data Eng. 27(3): 824-837 (2015) - 2014
- [j46]Jayaram Raghuram, David J. Miller, George Kesidis:
Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning. IEEE Trans. Neural Networks Learn. Syst. 25(8): 1520-1537 (2014) - 2013
- [j45]Anuj R. Jaiswal, David J. Miller, Prasenjit Mitra:
Schema matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields. ACM Trans. Database Syst. 38(1): 2 (2013) - 2012
- [j44]Xiguo Yuan, David J. Miller, Junying Zhang, David M. Herrington, Yue Wang:
An Overview of Population Genetic Data Simulation. J. Comput. Biol. 19(1): 42-54 (2012) - [j43]David J. Miller, Jayaram Raghuram, George Kesidis, Christopher M. Collins:
Improved Generative Semisupervised Learning Based on Finely Grained Component-Conditional Class Labeling. Neural Comput. 24(7): 1926-1966 (2012) - [j42]Bai Zhang, David J. Miller, Yue Joseph Wang:
Nonlinear System Modeling With Random Matrices: Echo State Networks Revisited. IEEE Trans. Neural Networks Learn. Syst. 23(1): 175-182 (2012) - 2011
- [j41]Guixi Zou, George Kesidis, David J. Miller:
A Flow Classifier with Tamper-Resistant Features and an Evaluation of Its Portability to New Domains. IEEE J. Sel. Areas Commun. 29(7): 1449-1460 (2011) - [j40]Tülay Adali, David J. Miller, Konstantinos I. Diamantaras, Jan Larsen:
Trends in Machine Learning for Signal Processing [In the Spotlight]. IEEE Signal Process. Mag. 28(6): 193-196 (2011) - 2010
- [j39]Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Joseph Wang:
Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases. J. Mach. Learn. Res. 11: 2141-2167 (2010) - [j38]Scott C. Markley, David J. Miller:
Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. IEEE J. Sel. Top. Signal Process. 4(3): 548-559 (2010) - [j37]Anuj R. Jaiswal, David J. Miller, Prasenjit Mitra:
Uninterpreted Schema Matching with Embedded Value Mapping under Opaque Column Names and Data Values. IEEE Trans. Knowl. Data Eng. 22(2): 291-304 (2010) - [j36]Yaman Aksu, David J. Miller, George Kesidis, Qing X. Yang:
Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions. IEEE Trans. Neural Networks 21(5): 701-717 (2010) - 2009
- [j35]David J. Miller, Yanxin Zhang, Guoqiang Yu, Yongmei Liu, Li Chen, Carl D. Langefeld, David M. Herrington, Yue Joseph Wang:
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Bioinform. 25(19): 2478-2485 (2009) - [j34]Don C. Bigler, Yaman Aksu, David J. Miller, Qing X. Yang:
STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer. Comput. Methods Programs Biomed. 95(2): 146-157 (2009) - 2008
- [j33]Yitan Zhu, Huai Li, David J. Miller, Zuyi Wang, Jianhua Xuan, Robert Clarke, Eric P. Hoffman, Yue Joseph Wang:
caBIGTM VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data. BMC Bioinform. 9 (2008) - [j32]David J. Miller, Yanxin Zhang, George Kesidis:
Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling. EURASIP J. Adv. Signal Process. 2008 (2008) - [j31]David J. Miller, Siddharth Pal, Yue Joseph Wang:
Extensions of transductive learning for distributed ensemble classification and application to biometric authentication. Neurocomputing 72(1-3): 119-125 (2008) - 2007
- [j30]David J. Miller, Siddharth Pal:
Transductive Methods for the Distributed Ensemble Classification Problem. Neural Comput. 19(3): 856-884 (2007) - [j29]David J. Miller, Deniz Erdogmus:
Guest Editorial for Special Issue on the 2005 IEEE Workshop on Machine Learning for Signal Processing. J. VLSI Signal Process. 48(1-2): 1-3 (2007) - [j28]Siddharth Pal, David J. Miller:
An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification. J. VLSI Signal Process. 48(1-2): 21-37 (2007) - 2006
- [j27]Jisheng Wang, David J. Miller, George Kesidis:
Efficient Mining of the Multidimensional Traffic Cluster Hierarchy for Digesting, Visualization, and Anomaly Identification. IEEE J. Sel. Areas Commun. 24(10): 1929-1941 (2006) - [j26]Michael W. Graham, David J. Miller:
Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection. IEEE Trans. Signal Process. 54(4): 1289-1303 (2006) - 2005
- [j25]Qi Zhao, David J. Miller:
Mixture Modeling with Pairwise, Instance-Level Class Constraints. Neural Comput. 17(11): 2482-2507 (2005) - 2004
- [j24]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:
Cyber defense technology networking and evaluation. Commun. ACM 47(3): 58-61 (2004) - [j23]David J. Miller, Elias S. G. Carotti, Yu-Wei Wang, Juan Carlos De Martin:
Joint source-channel decoding of predictively and nonpredictively encoded sources: a two-stage estimation approach. IEEE Trans. Commun. 52(9): 1575-1584 (2004) - [j22]David J. Miller, Tülay Adali, Jan Larsen, Marc M. Van Hulle:
Guest Editorial for Special Issue on Machine Learning for Signal Processing. J. VLSI Signal Process. 37(2-3): 171-175 (2004) - [j21]John Browning, David J. Miller:
A Maximum Entropy Approach for Collaborative Filtering. J. VLSI Signal Process. 37(2-3): 199-209 (2004) - 2003
- [j20]David J. Miller, John Browning:
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. 25(11): 1468-1483 (2003) - [j19]David J. Miller, Qi Zhao:
A sequence-based extension of mean-field annealing using the forward/backward algorithm: application to image segmentation. IEEE Trans. Signal Process. 51(10): 2692-2705 (2003) - 2002
- [j18]Taekon Kim, Robert E. Van Dyck, David J. Miller:
Hybrid fractal zerotree wavelet image coding. Signal Process. Image Commun. 17(4): 347-360 (2002) - [j17]Piya Bunyaratavej, David J. Miller:
An iterative hillclimbing algorithm for discrete optimization on images: application to joint encoding of image transform coefficients. IEEE Signal Process. Lett. 9(2): 46-50 (2002) - 2000
- [j16]David J. Miller, Lian Yan:
Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification. Neural Comput. 12(9): 2175-2207 (2000) - [j15]MoonSeo Park, David J. Miller:
Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation. IEEE Trans. Commun. 48(1): 1-6 (2000) - [j14]Lian Yan, David J. Miller:
General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle. IEEE Trans. Neural Networks Learn. Syst. 11(3): 558-573 (2000) - 1999
- [j13]Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho:
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models. IEEE Trans. Pattern Anal. Mach. Intell. 21(2): 159-173 (1999) - [j12]Robert E. Van Dyck, David J. Miller:
Transport of wireless video using separate, concatenated, and joint source-channel coding. Proc. IEEE 87(10): 1734-1750 (1999) - [j11]MoonSeo Park, David J. Miller:
Improved image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh. IEEE Trans. Image Process. 8(6): 863-867 (1999) - [j10]David J. Miller, Lian Yan:
Critic-driven ensemble classification. IEEE Trans. Signal Process. 47(10): 2833-2844 (1999) - 1998
- [j9]David J. Miller, Hasan S. Uyar:
Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier. Neural Comput. 10(2): 281-293 (1998) - [j8]David J. Miller, MoonSeo Park:
A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models. IEEE Trans. Commun. 46(2): 222-231 (1998) - 1997
- [j7]MoonSeo Park, David J. Miller:
Low-delay optimal MAP state estimation in HMM's with application to symbol decoding. IEEE Signal Process. Lett. 4(10): 289-292 (1997) - [j6]Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho:
Mixture of experts regression modeling by deterministic annealing. IEEE Trans. Signal Process. 45(11): 2811-2820 (1997) - 1996
- [j5]David J. Miller, Kenneth Rose:
Hierarchical, Unsupervised Learning with Growing via Phase Transitions. Neural Comput. 8(2): 425-450 (1996) - [j4]Kenneth Rose, David J. Miller, Allen Gersho:
Entropy-constrained tree-structured vector quantizer design. IEEE Trans. Image Process. 5(2): 393-398 (1996) - [j3]David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho:
A global optimization technique for statistical classifier design. IEEE Trans. Signal Process. 44(12): 3108-3122 (1996) - 1994
- [j2]David J. Miller, Kenneth Rose:
A non-greedy approach to tree-structured clustering. Pattern Recognit. Lett. 15(7): 683-690 (1994) - [j1]David J. Miller, Kenneth Rose:
Combined source-channel vector quantization using deterministic annealing. IEEE Trans. Commun. 42(234): 347-356 (1994)
Conference and Workshop Papers
- 2023
- [c73]Hang Wang, Sahar Karami, Ousmane Dia, Hippolyt Ritter, Ehsan Emamjomeh-Zadeh, Jiahui Chen, Zhen Xiang, David J. Miller, George Kesidis:
Training Set Cleansing of Backdoor Poisoning by Self-Supervised Representation Learning. ICASSP 2023: 1-5 - [c72]Xi Li, David J. Miller, Zhen Xiang, George Kesidis:
A BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers. MLSP 2023: 1-6 - 2022
- [c71]Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis:
Detecting Backdoor Attacks against Point Cloud Classifiers. ICASSP 2022: 3159-3163 - [c70]Xi Li, Zhen Xiang, David J. Miller, George Kesidis:
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. ICASSP 2022: 3333-3337 - [c69]Zhen Xiang, David J. Miller, George Kesidis:
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. ICLR 2022 - 2021
- [c68]Zhen Xiang, David J. Miller, George Kesidis:
L-Red: Efficient Post-Training Detection of Imperceptible Backdoor Attacks Without Access to the Training Set. ICASSP 2021: 3745-3749 - [c67]Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis:
A Backdoor Attack against 3D Point Cloud Classifiers. ICCV 2021: 7577-7587 - 2020
- [c66]Haoti Zhong, Cong Liao, Anna Cinzia Squicciarini, Sencun Zhu, David J. Miller:
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. CODASPY 2020: 97-108 - [c65]Xi Li, David J. Miller, Zhen Xiang, George Kesidis:
A Scalable Mixture Model Based Defense Against Data Poisoning Attacks on Classifiers. DDDAS 2020: 262-273 - [c64]Zhen Xiang, David J. Miller, George Kesidis:
Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification. ICASSP 2020: 3827-3831 - [c63]Zhen Xiang, David J. Miller, Hang Wang, George Kesidis:
Revealing Perceptible Backdoors in DNNs, Without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. MLSP 2020: 1-6 - 2019
- [c62]Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, David J. Miller:
Toward Image Privacy Classification and Spatial Attribution of Private Content. IEEE BigData 2019: 1351-1360 - [c61]Alexander G. Ororbia II, Ankur Arjun Mali, Jian Wu, Scott O'Connell, William Dreese, David J. Miller, C. Lee Giles:
Learned Neural Iterative Decoding for Lossy Image Compression Systems. DCC 2019: 3-12 - [c60]Yujia Wang, David J. Miller, George Kesidis:
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. ICASSP 2019: 8063-8066 - [c59]Zhen Xiang, David J. Miller, George Kesidis:
A Benchmark Study Of Backdoor Data Poisoning Defenses For Deep Neural Network Classifiers And A Novel Defense. MLSP 2019: 1-6 - 2018
- [c58]Haoti Zhong, Anna Cinzia Squicciarini, David J. Miller:
Toward Automated Multiparty Privacy Conflict Detection. CIKM 2018: 1811-1814 - [c57]Zhen Xiang, David J. Miller:
Locally optimal, delay-tolerant predictive source coding. CISS 2018: 1-6 - [c56]David J. Miller, George Kesidis, Zhicong Qiu:
Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD). MLSP 2018: 1-6 - [c55]David J. Miller, Yujia Wang, George Kesidis:
Anomaly Detection of Attacks (Ada) on DNN Classifiers at Test Time. MLSP 2018: 1-6 - [c54]Haoti Zhong, David J. Miller, Anna Cinzia Squicciarini:
Flexible Inference for Cyberbully Incident Detection. ECML/PKDD (3) 2018: 356-371 - 2017
- [c53]Yuquan Shan, Chiara Lo Prete, George Kesidis, David J. Miller:
A simulation framework for uneconomic virtual bidding in day-ahead electricity markets. ACC 2017: 2705-2712 - [c52]Zhicong Qiu, David J. Miller, George Kesidis:
Flow based botnet detection through semi-supervised active learning. ICASSP 2017: 2387-2391 - [c51]Haoti Zhong, Anna Cinzia Squicciarini, David J. Miller, Cornelia Caragea:
A Group-Based Personalized Model for Image Privacy Classification and Labeling. IJCAI 2017: 3952-3958 - [c50]David J. Miller, Najah F. Ghalyan, Asok Ray:
A locally optimal algorithm for estimating a generating partition from an observed time series. MLSP 2017: 1-6 - [c49]David J. Miller, Xinyi Hu, Zhicong Qiu, George Kesidis:
Adversarial learning: A critical review and active learning study. MLSP 2017: 1-6 - 2016
- [c48]Hossein Soleimani, David J. Miller:
Semi-supervised Multi-Label Topic Models for Document Classification and Sentence Labeling. CIKM 2016: 105-114 - [c47]Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Griffin, David J. Miller, Cornelia Caragea:
Content-Driven Detection of Cyberbullying on the Instagram Social Network. IJCAI 2016: 3952-3958 - [c46]Hossein Soleimani, David J. Miller:
Exploiting the value of class labels in topic models for semi-supervised document classification. IJCNN 2016: 4025-4031 - [c45]Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu:
FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data. ISBI 2016: 351-354 - [c44]George Kesidis, David J. Miller, Zhicong Qiu:
IP-level fast re-routing for robustness to mass failure events using a hybrid bandwidth and reliability cost metric. MILCOM 2016: 812-816 - [c43]Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu:
Graphical Time Warping for Joint Alignment of Multiple Curves. NIPS 2016: 3648-3656 - 2015
- [c42]Zhicong Qiu, David J. Miller, George Kesidis:
Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data. MLSP 2015: 1-6 - 2014
- [c41]Fatih Kocak, David J. Miller, George Kesidis:
Detecting anomalous latent classes in a batch of network traffic flows. CISS 2014: 1-6 - [c40]Jayaram Raghuram, George Kesidis, Christopher Griffin, Karl N. Levitt, David J. Miller, Jeff Rowe, Anna Scaglione:
A Bidding Game for Generators in the Presence of Flexible Demand. Feedback Computing 2014 - [c39]Hossein Soleimani, David J. Miller:
Sparse topic models by parameter sharing. MLSP 2014: 1-6 - 2013
- [c38]Hung-Hsuan Chen, David J. Miller, C. Lee Giles:
The predictive value of young and old links in a social network. DBSocial 2013: 43-48 - [c37]Aditya Kurve, David J. Miller, George Kesidis:
Defeating Tyranny of the Masses in Crowdsourcing: Accounting for Low-Skilled and Adversarial Workers. GameSec 2013: 140-153 - [c36]Gaole Jin, Raviv Raich, David J. Miller:
A generative semi-supervised model for multi-view learning when some views are label-free. ICASSP 2013: 3302-3306 - [c35]Jianping He, David J. Miller, George Kesidis:
Latent Interest-Group Discovery and Management by Peer-to-Peer Online Social Networks. SocialCom 2013: 162-167 - 2012
- [c34]Jayaram Raghuram, David J. Miller, George Kesidis:
Semisupervised domain adaptation for mixture model based classifiers. CISS 2012: 1-6 - [c33]David J. Miller, Fatih Kocak, George Kesidis:
Sequential anomaly detection in a batch with growing number of tests: Application to network intrusion detection. MLSP 2012: 1-6 - 2011
- [c32]Z. Berkay Celik, Jayaram Raghuram, George Kesidis, David J. Miller:
Salting Public Traces with Attack Traffic to Test Flow Classifiers. CSET 2011 - 2010
- [c31]J. Daniel Park, David J. Miller, John F. Doherty, Stephen C. Thompson:
Feasibility of range estimation using sonar LPI. CISS 2010: 1-6 - [c30]David J. Miller, Chu-Fang Lin, George Kesidis, Christopher M. Collins:
Improved Fine-Grained Component-Conditional Class Labeling with Active Learning. ICMLA 2010: 3-8 - 2009
- [c29]J. Daniel Park, David J. Miller, John F. Doherty, Stephen C. Thompson:
A study on the feasibility of low probability of intercept sonar. CISS 2009: 284-289 - 2008
- [c28]David J. Miller, Yanxin Zhang, George Kesidis:
A transductive extension of maximum entropy/iterative scaling for decision aggregation in distributed classification. ICASSP 2008: 1865-1868 - 2006
- [c27]Yuanjian Feng, Zuyi Wang, Yitan Zhu, Jianhua Xuan, David J. Miller:
Learning the Tree of Phenotypes Using Genomic Data and VISDA. BIBE 2006: 165-170 - [c26]David J. Miller, Siddharth Pal:
Transductive Methods for Distributed Ensemble Classification. CISS 2006: 1605-1610 - [c25]Yitan Zhu, Zuyi Wang, Yuanjian Feng, Jianhua Xuan, David J. Miller, Eric P. Hoffman, Yue Wang:
Phenotypic-Specific Gene Module Discovery using a Diagnostic Tree and caBIGTM VISDA. EMBC 2006: 5767-5770 - [c24]Jisheng Wang, Ihab Hamadeh, George Kesidis, David J. Miller:
Polymorphic worm detection and defense: system design, experimental methodology, and data resources. LSAD@SIGCOMM 2006: 169-176 - 2005
- [c23]Soranun Jiwasurat, George Kesidis, David J. Miller:
Hierarchical shaped deficit round-robin scheduling. GLOBECOM 2005: 6 - [c22]Qi Zhao, David J. Miller:
Semisupervised learning of mixture models with class constraints. ICASSP (5) 2005: 185-188 - 2004
- [c21]Qi Zhao, David J. Miller:
A deterministic, annealing-based approach for learning and model selection in finite mixture models. ICASSP (5) 2004: 457-460 - 2003
- [c20]David J. Miller, John Browning:
A mixture model and EM algorithm for robust classification, outlier rejection, and class discovery. ICASSP (2) 2003: 809-812 - [c19]David J. Miller, John Browning:
A mixture model framework for class discovery and outlier detection in mixed labeled/unlabeled data sets. NNSP 2003: 489-498 - 2002
- [c18]David J. Miller, Piya Bunyaratavej, Qi Zhao:
A sequence-based generalization of mean-field annealing using the Forward/Backward algorithm: Application to image segmentation. ICASSP 2002: 969-972 - 2001
- [c17]Piya Bunyaratavej, David J. Miller:
Locally optimal joint encoding of image transform coefficients. ICASSP 2001: 2573-2576 - [c16]A. Ravindran, Hang Liu, Izzet Agoren, Alex J. Lackpour, David J. Miller, Mohsen Kavehrad, John F. Doherty:
Mobile multimedia services for third generation communications systems. VTC Fall 2001: 2589-2593 - 1999
- [c15]MoonSeo Park, David J. Miller:
Improved Joint Source-Channel Decoding for Variable-Length Encoded Data Using Soft Decisions and MMSE Estimation. Data Compression Conference 1999: 544 - [c14]David J. Miller, Lian Yan:
Ensemble classification by critic-driven combining. ICASSP 1999: 1029-1032 - [c13]Lian Yan, David J. Miller:
Time series prediction via neural network inversion. ICASSP 1999: 1049-1052 - [c12]MoonSeo Park, David J. Miller:
Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation. ICASSP 1999: 2451-2454 - [c11]David J. Miller, Lian Yan:
Approximate maximum entropy joint feature inference for discrete space classification. IJCNN 1999: 1419-1424 - 1998
- [c10]Jeongjin Roh, David J. Miller:
A New Set Partitioning Method for Wavelet-based Image Coding. ICIP (1) 1998: 102-106 - 1997
- [c9]Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho:
Deterministically annealed mixture of experts models for statistical regression. ICASSP 1997: 3201-3204 - [c8]MoonSeo Park, David J. Miller:
Image Decoding Over Noisy Channels Using Minimum Mean-Squared Estimation and a Markov Mesh. ICIP (3) 1997: 594-597 - 1996
- [c7]Ajit V. Rao, David J. Miller, Kenneth Rose, Allen Gersho:
A generalized VQ method for combined compression and estimation. ICASSP 1996: 2032-2035 - [c6]David J. Miller, Hasan S. Uyar:
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data. NIPS 1996: 571-577 - 1995
- [c5]David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho:
An Information-theoretic Learning Algorithm for Neural Network Classification. NIPS 1995: 591-597 - 1994
- [c4]Kenneth Rose, David J. Miller, Allen Gersho:
Entropy-Constrained Tree-Structured Vector Quantizer Design by the Minimum Cross Entropy Principle. Data Compression Conference 1994: 12-21 - [c3]David J. Miller, Kenneth Rose, Philip A. Chou:
Deterministic annealing for trellis quantizer and HMM design using Baum-Welch re-estimation. ICASSP (5) 1994: 261-264 - 1993
- [c2]David J. Miller, Kenneth Rose:
An Improved Sequential Search Multistage Vector Quantizer. Data Compression Conference 1993: 12-21 - 1992
- [c1]David J. Miller, Kenneth Rose:
Joint source-channel vector quantization using deterministic annealing. ICASSP 1992: 377-380
Informal and Other Publications
- 2024
- [i31]Xi Li, Hang Wang, David J. Miller, George Kesidis:
Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks. CoRR abs/2402.02034 (2024) - 2023
- [i30]Hang Wang, Zhen Xiang, David J. Miller, George Kesidis:
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection. CoRR abs/2308.04617 (2023) - [i29]Xi Li, Zhen Xiang, David J. Miller, George Kesidis:
Backdoor Mitigation by Correcting the Distribution of Neural Activations. CoRR abs/2308.09850 (2023) - [i28]Hang Wang, David J. Miller, George Kesidis:
Post-Training Overfitting Mitigation in DNN Classifiers. CoRR abs/2309.16827 (2023) - 2022
- [i27]Zhen Xiang, David J. Miller, George Kesidis:
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. CoRR abs/2201.08474 (2022) - [i26]Hang Wang, Zhen Xiang, David J. Miller, George Kesidis:
Universal Post-Training Backdoor Detection. CoRR abs/2205.06900 (2022) - [i25]Hang Wang, Sahar Karami, Ousmane Dia, H. Ritter, Ehsan Emamjomeh-Zadeh, Jiahui Chen, Zhen Xiang, David J. Miller, George Kesidis:
Training set cleansing of backdoor poisoning by self-supervised representation learning. CoRR abs/2210.10272 (2022) - 2021
- [i24]Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis:
A Backdoor Attack against 3D Point Cloud Classifiers. CoRR abs/2104.05808 (2021) - [i23]Hang Wang, David J. Miller, George Kesidis:
Anomaly Detection of Test-Time Evasion Attacks using Class-conditional Generative Adversarial Networks. CoRR abs/2105.10101 (2021) - [i22]Xi Li, David J. Miller, Zhen Xiang, George Kesidis:
A BIC based Mixture Model Defense against Data Poisoning Attacks on Classifiers. CoRR abs/2105.13530 (2021) - [i21]Xi Li, George Kesidis, David J. Miller, Maxime Bergeron, Ryan Ferguson, Vladimir Lucic:
Robust and Active Learning for Deep Neural Network Regression. CoRR abs/2107.13124 (2021) - [i20]Xi Li, George Kesidis, David J. Miller, Vladimir Lucic:
Backdoor Attack and Defense for Deep Regression. CoRR abs/2109.02381 (2021) - [i19]Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, George Kesidis:
Detecting Backdoor Attacks Against Point Cloud Classifiers. CoRR abs/2110.10354 (2021) - [i18]Xi Li, Zhen Xiang, David J. Miller, George Kesidis:
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. CoRR abs/2112.03350 (2021) - 2020
- [i17]Zhen Xiang, David J. Miller, George Kesidis:
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing. CoRR abs/2010.07489 (2020) - [i16]Zhen Xiang, David J. Miller, George Kesidis:
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set. CoRR abs/2010.09987 (2020) - 2019
- [i15]David J. Miller, Zhen Xiang, George Kesidis:
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks. CoRR abs/1904.06292 (2019) - [i14]Zhen Xiang, David J. Miller, George Kesidis:
Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification. CoRR abs/1908.10498 (2019) - [i13]George Kesidis, David J. Miller:
Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers. CoRR abs/1910.08032 (2019) - [i12]Zhen Xiang, David J. Miller, George Kesidis:
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic. CoRR abs/1911.07970 (2019) - 2018
- [i11]Alexander G. Ororbia II, Ankur Arjun Mali, Jian Wu, Scott O'Connell, David J. Miller, C. Lee Giles:
Learned Iterative Decoding for Lossy Image Compression Systems. CoRR abs/1803.05863 (2018) - [i10]Cong Liao, Haoti Zhong, Anna Cinzia Squicciarini, Sencun Zhu, David J. Miller:
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. CoRR abs/1808.10307 (2018) - [i9]David J. Miller, Xinyi Hu, Zhen Xiang, George Kesidis:
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters. CoRR abs/1811.00121 (2018) - [i8]Yujia Wang, David J. Miller, George Kesidis:
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. CoRR abs/1811.02658 (2018) - 2017
- [i7]David J. Miller, Xinyi Hu, Zhicong Qiu, George Kesidis:
Adversarial Learning: A Critical Review and Active Learning Study. CoRR abs/1705.09823 (2017) - [i6]David J. Miller, Yujia Wang, George Kesidis:
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. CoRR abs/1712.06646 (2017) - 2015
- [i5]Zhicong Qiu, David J. Miller, George Kesidis:
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data. CoRR abs/1511.01047 (2015) - [i4]Hossein Soleimani, David J. Miller:
ATD: Anomalous Topic Discovery in High Dimensional Discrete Data. CoRR abs/1512.06452 (2015) - 2014
- [i3]Hossein Soleimani, David J. Miller:
Parsimonious Topic Models with Salient Word Discovery. CoRR abs/1401.6169 (2014) - [i2]Yuquan Shan, Jayaram Raghuram, George Kesidis, Christopher Griffin, Karl N. Levitt, David J. Miller, Jeff Rowe, Anna Scaglione:
Generation bidding game with flexible demand. CoRR abs/1408.6689 (2014) - 2013
- [i1]Aditya Kurve, David J. Miller, George Kesidis:
Multicategory Crowdsourcing Accounting for Plurality in Worker Skill and Intention, Task Difficulty, and Task Heterogeneity. CoRR abs/1307.7332 (2013)
Coauthor Index
[j68] [j66] [j65] [j64] [j62] [j61] [j60] [j54] [j53] [j51] [j49] [j48] [j46] [j43] [j41] [j36] [j32] [j27] [j24] [c73] [c72] [c71] [c70] [c69] [c68] [c67] [c65] [c64] [c63] [c60] [c59] [c56] [c55] [c53] [c52] [c49] [c44] [c42] [c41] [c40] [c37] [c35] [c34] [c33] [c32] [c30] [c28] [c24] [c23] [i31] [i30] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [i12] [i9] [i8] [i7] [i6] [i5] [i2] [i1]
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