Publications

"Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO"; B Pezeshki, R Marinescu, A Ihler, R Dechter; Uncertainty in Artificial Intelligence (UAI), Aug. 2023, pp. 1662-1672
C87: [ Abstract ] | [ BibTex ] | [ Link ]

"Scale-Separated Dynamic Mode Decomposition and Ionospheric Forecasting"; Alford-Lago, Curtis, Ihler, Zawdie; Radio Science 58 (8), Aug 2023
J27: [ Abstract ] | [ BibTex ] | [ Link ]

"Design Amortization for Bayesian Optimal Experimental Design"; Kennamer, Walton, Ihler; Conference on Artificial Intelligence (AAAI), June 2023
C86: [ Abstract ] | [ BibTex ] | [ Link ]

"A Deep Q-Learning Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks"; Devaraju, Ihler, Kumar; IEEE Int'l Conf. on Computing, Networking and Communications (ICNC), Feb. 2023
C85: [ Abstract ] | [ BibTex ] | [ Link ]

"A Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks"; Devaraju, Ihler, Kumar; IEEE Consumer Communications & Networking Conference (CCNC), Jan. 2023
C84: [ Abstract ] | [ BibTex ] | [ Link ]

"A cross-layer, mobility and congestion-aware routing protocol for UAV networks"; S Garg, A Ihler, ES Bentley, S Kumar; IEEE Trans. Aerospace & Electronic Systems 59 (4), Dec. 2022
J26: [ Abstract ] | [ BibTex ] | [ Link ]

"NeuroBE: Escalating Neural Network Approximations of Bucket Elimination"; Agarwal, Kask, Ihler, Dechter; Uncertainty in Artificial Intelligence (UAI), Aug. 2022
C83: [ Abstract ] | [ BibTex ]

"AND/OR Branch-and-Bound for Computational Protein Design Optimizing K*"; Pezeshki, Marinescu, Ihler, Dechter; Uncertainty in Artificial Intelligence (UAI), Aug. 2022 (Also presented at the workshop on Tractable Probabilistic Modeling (TPM), at which it received a Best Paper award)
C82: [ Abstract ] | [ BibTex ]

"Be Like Water: Adaptive Floating Point for Machine Learning"; Yeh, Sterner, Lai, Chuang, Ihler; Int'l Conference on Machine Learning (ICML), June 2022
C81: [ Abstract ] | [ BibTex ] | [ PDF ]

"Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks"; Liang, Xu, McAleer, Hu, Ihler, Abbeel, Fox; Int'l Conference on Machine Learning (ICML), June 2022
C80: [ Abstract ] | [ BibTex ] | [ PDF ]

"Deep learning enhanced dynamic mode decomposition"; Alford-Lago, Curtis, Ihler, Issan; Chaos 32 (3), 033116.
J25: [ Abstract ] | [ BibTex ]

"Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients"; Kennamer, Ishida, Gonzalez-Gaitan, de Souza, Ihler, et al.; IEEE Symposium Series on Computational Intelligence (SSCI), pp 3155-3124, Oct. 2020
C79: [ Abstract ] | [ BibTex ] | [ Link ]

"A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets"; Ma, Zhang, Ihler; Int'l Journal of Comp. Comm. & Control 15(2), Mar. 2020
J24: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Multi-lane short-term traffic forecasting with convolutional LSTM network"; Ma, Zhang, Ihler; IEEE Access 8, pp. 34629--34243, Feb. 2020
J23: [ Abstract ] | [ BibTex ] | [ Link ]

"Scaling Up AND/OR Abstraction Sampling"; Kask, Pezeshki, Broka, Ihler, Dechter; Int'l Join Conference on Artificial Intelligence (IJCAI), pp. 4266--4274, Jan. 2020
C78: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"A Weighted Mini-Bucket Bound for Solving Influence Diagram"; Junkyu Lee, Radu Marinescu, Alexander Ihler, Rina Dechter; Uncertainty in Artificial Intelligence (UAI), July 2019
C77: [ Abstract ] | [ BibTex ] | [ PDF ]

"Empirical Study of MC-Dropout in Various Astronomical Observing Conditions"; Kennamer, Ihler, Kirkby; CVPR Workshop on Uncertainty and Robustness in Deep Visual Learning, June 2019
R13: [ Abstract ] | [ BibTex ]

"Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms"; Qi Lou, Rina Dechter, Alexander Ihler; AAAI Conference on Artificial Intelligence (AAAI), Jan. 2019
C76: [ Abstract ] | [ BibTex ] | [ PDF ]

"Anytime Recursive Best-First Search for Bounding Marginal MAP"; Radu Marinescu, Rina Dechter, Alexander Ihler, Akihiro Kishimoto, Adi Botea; AAAI Conference on Artificial Intelligence (AAAI), Jan. 2019
C75: [ Abstract ] | [ BibTex ] | [ PDF ]

"AND/OR Search for Marginal MAP"; Marinescu, Lee, Dechter, Ihler; Journal of Artificial Intelligence Research 63:875--921
J22: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Lifted Weighted Mini-Bucket"; Nick Gallo, Alexander Ihler; Neural Information Processing Systems (NIPS), Dec. 2018
C74: [ Abstract ] | [ BibTex ] | [ PDF ]

"Trends of Work Transformation in Online Labor Market"; Ma, Zhang, Ihler; INFORMS Annual Meeting, Nov. 2018
R12: [ Abstract ] | [ BibTex ]

"Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation"; Wang, Ihler, Kording, Yarkony; European Conference on Computer Vision (ECCV), Sept. 2018
C73: [ Abstract ] | [ BibTex ] | [ PDF ]

"Finite-sample Bounds for Marginal MAP"; Qi Lou, Rina Dechter, Alexander Ihler; Uncertainty in Artificial Intelligence (UAI), Aug. 2018
C72: [ Abstract ] | [ BibTex ] | [ PDF ]

"Join Graph Decomposition Bounds for Influence Diagrams"; Junkyu Lee, Alexander Ihler, Rina Dechter; Uncertainty in Artificial Intelligence (UAI), Aug. 2018
C71: [ Abstract ] | [ BibTex ] | [ PDF ]

"Abstraction Sampling in Graphical Models"; Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask; Uncertainty in Artificial Intelligence (UAI), Aug. 2018
C70: [ Abstract ] | [ BibTex ] | [ PDF ]

"ContextNet: Deep Learning for Star Galaxy Classification"; Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez; Int'l Conference on Machine Learning (ICML), July 2018
C69: [ Abstract ] | [ BibTex ] | [ PDF ]

"Stochastic Anytime Search for Bounding Marginal MAP"; Marinescu, Dechter, Ihler; Int'l Join Conference on Artificial Intelligence (IJCAI), July 2018
C68: [ Abstract ] | [ BibTex ] | [ PDF ]

"Estimating Warehouse Rental Price using Machine Learning Techniques"; Ma, Zhang, Ihler, Pan; Int'l Journal of Comp. Comm. & Control 13(2), April 2018
J21: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information"; Tao, Hsu, Ihler, Gao, Sorooshian; Journal of Hydrometeorology 19(2):393--408
J20: [ Abstract ] | [ BibTex ] | [ Link ]

"Lifted Generalized Dual Decomposition"; Gallo, Ihler; AAAI Conference on Artificial Intelligence (AAAI), Feb. 2018
C67: [ Abstract ] | [ BibTex ] | [ PDF ]

"Anytime anyspace AND/OR best-first search for bounding marginal MAP"; Lou, Dechter, Ihler; AAAI Conference on Artificial Intelligence (AAAI), Feb. 2018
C66: [ Abstract ] | [ BibTex ] | [ PDF ]

"Generalized Dual Decomposition for Bounding Maximum Expected Utility of Influence Diagrams with Perfect Recall"; Lee, Ihler, Dechter; AAAI Workshop on Planning and Inference, Feb. 2018
R11: [ Abstract ] | [ BibTex ] | [ PDF ]

"Dynamic importance sampling for anytime bounds of the partition function"; Lou, Dechter, Ihler; Neural Information Processing Systems (NIPS), Dec 2017
C65: [ Abstract ] | [ BibTex ] | [ PDF ]

"Abstraction Sampling in Graphical Models"; Dechter, Broka, Kask, Ihler; NIPS Workshop on Advances in Approximate Bayesian Inference, Dec 2017
R10: [ Abstract ] | [ BibTex ] | [ PDF ]

"Belief Propagation in Conditional RBMs for Structured Prediction"; Ping, Ihler; AI & Statistics (AISTATS), April 2017
C64: [ Abstract ] | [ BibTex ] | [ PDF ]

"Anytime Anyspace AND/OR Search for Bounding the Partition Function"; Lou, Dechter, Ihler; AAAI Conference on Artificial Intelligence (AAAI), pp. 860-867, Feb. 2017
C63: [ Abstract ] | [ BibTex ] | [ PDF ]

"Anytime Best+Depth-First Search for Bounding Marginal MAP"; Marinescu, Lee, Dechter, Ihler; AAAI Conference on Artificial Intelligence (AAAI), pp. 3775-3782, Feb. 2017
C62: [ Abstract ] | [ BibTex ] | [ PDF ]

"Learning Infinite RBMs with Frank-Wolfe"; Ping, Liu, Ihler; Neural Information Processing Systems (NIPS), Dec 2016
C61: [ Abstract ] | [ BibTex ] | [ PDF ]

"Deep Neural Networks for Precipitation Estimation from Remotely Sensed Information"; Tao, Gao, Ihler, Hsu, Sorooshian; IEEE Congress on Evolutionary Computation (CEC), July 2016
C60: [ Abstract ] | [ BibTex ] | [ Link ]

"Cell-to-Cell Activity Prediction for Smart Cities"; Cici, Alimpertis, Ihler, Markopoulou; INFOCOM Workshop on Smart Cities and Urban Computing (SmartCity), April 2016
C59: [ Abstract ] | [ BibTex ] | [ PDF ]

"A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products"; Tao, Gao, Hsu, Sorooshian, Ihler; Journal of Hydrometeorology 17(3):931--945
J19: [ Abstract ] | [ BibTex ] | [ Link ]

"From Exact to Anytime Solutions for Marginal MAP"; Lee, Marinescu, Dechter, Ihler; Conference on Artificial Intelligence (AAAI), Feb 2016
C58: [ Abstract ] | [ BibTex ] | [ PDF ]

"Variational Multi−Objective Coordination"; Roijers, Whiteson, Ihler, Oliehoek; NIPS Workshop on Learning‚ Inference and Control of Multi−Agent Systems, Dec 2015
R9: [ Abstract ] | [ BibTex ] | [ PDF ]

"Probabilistic Variational Bounds for Graphical Models"; Liu, Fisher, Ihler; Neural Information Processing Systems (NIPS), Dec 2015
C57: [ Abstract ] | [ BibTex ] | [ PDF ]

"Decomposition Bounds for Marginal MAP"; Ping, Liu, Ihler; Neural Information Processing Systems (NIPS), Dec 2015
C56: [ Abstract ] | [ BibTex ] | [ PDF ]

"Crystal Identification in Positron Emission Tomography Using Probabilistic Graphical Models"; Keator, Ihler; IEEE Transactions on Nuclear Science 62(5), pp. 2102--2112
J18: [ Abstract ] | [ BibTex ] | [ Link ]

"Estimating the Partition Function by Discriminance Sampling"; Liu, Peng, Ihler, Fisher; Uncertainty in Artificial Intelligence (UAI), July 2015
C55: [ Abstract ] | [ BibTex ] | [ PDF ]

"Incremental Region Selection for Mini-bucket Elimination Bounds"; Forouzan, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2015
C54: [ Abstract ] | [ BibTex ] | [ PDF ]

"Boosting Crowdsourcing with Expert Labels: Local vs. Global Effects"; Liu, Ihler, Fisher; Int'l Conference on Information Fusion, July 2015
C53: [ Abstract ] | [ BibTex ] | [ PDF ]

"Pushing Forward Marginal MAP with Best-First Search"; Marinescu, Dechter, Ihler; Int'l Joint Conference on Artificial Intelligence (IJCAI), July 2015
C52: [ Abstract ] | [ BibTex ] | [ PDF ]

"Distributed Estimation, Information Loss and Exponential Families"; Liu, Ihler; Neural Information Processing Systems (NIPS), Dec. 2014
C51: [ Abstract ] | [ BibTex ] | [ PDF ]

"Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics"; Lam, Kask, Dechter, Ihler; Symposium on Combinatorial Search (SoCS), Aug. 2014
C50: [ Abstract ] | [ BibTex ] | [ PDF ]

"AND/OR Search for Marginal MAP"; Marinescu, Dechter, Ihler; Conference on Uncertainty in Artificial Intelligence (UAI), July 2014
C49: [ Abstract ] | [ BibTex ] | [ PDF ]

"Marginal structured SVM with hidden variables"; Ping, Liu, Ihler; International Conference on Machine Learning (ICML), June 2014
C48: [ Abstract ] | [ BibTex ] | [ PDF ]

"Beyond MAP estimation with the track-oriented multiple-hypothesis tracker"; Frank, Smyth, Ihler; IEEE Trans. Signal Processing 62(9):2413--2423.
J17: [ Abstract ] | [ BibTex ] | [ Link ]

"Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification"; Keator, Fallon, Lakatos, Fowlkes, Potkin, Ihler; Human Brain Mapping (HBM) 35(1):38--52.
J16: [ Abstract ] | [ BibTex ] | [ Link ]

"Does better inference mean better learning?"; Gelfand, Dechter, Ihler; NIPS Workshop on Perturbations, Optimization, and Statistics (POS), Dec. 2013
R8: [ Abstract ] | [ BibTex ] | [ PDF ]

"Scoring workers in crowdsourcing: How many control questions are enough?"; Liu, Steyvers, Ihler; Neural Information Processing Systems (NIPS), Dec. 2013
C47: [ Abstract ] | [ BibTex ] | [ PDF ]

"Variational planning for graph-based MDPs"; Cheng, Liu, Chen, Ihler; Neural Information Processing Systems (NIPS), Dec. 2013
C46: [ Abstract ] | [ BibTex ] | [ PDF ]

"On reliable crowdsourcing and the use of ground-truth information"; Liu, Steyvers, Fisher, Ihler; Workshop on Crowdsourcing at Scale, HCOMP, Nov. 2013
R7: [ Abstract ] | [ BibTex ] | [ PDF ]

"Linear Approximation to ADMM for MAP inference"; Forouzan, Ihler; Asian Conf. on Machine Learning (ACML), JMLR W&CP 29, pp48-61, Nov. 2013
C45: [ Abstract ] | [ BibTex ] | [ PDF ]

"Variational Algorithms for Marginal MAP"; Liu, Ihler; Journal of Machine Learning Research (JMLR), 14, pp. 3165-3200.
J15: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Image enhancement in projectors via optical pixel shift and overlay"; Sajadi, Qoc-Lai, Ihler, Gopi, Majumder; Int'l Conference on Computational Photography 2013
C44: [ Abstract ] | [ BibTex ] | [ PDF ]

"Winning the PASCAL 2011 MAP Challenge with Enhanced AND/OR Branch-and-Bound"; Otten, Ihler, Kask, Dechter; NIPS Workshop on Discrete Optimization (DiscML), Dec. 2012
R6: [ Abstract ] | [ BibTex ] | [ PDF ]

"Variational inference for crowdsourcing"; Liu, Peng, Ihler; Neural Information Processing Systems (NIPS), Dec. 2012
C43: [ Abstract ] | [ BibTex ] | [ PDF ]

"Fast planar correlation clustering for image segmentation"; Yarkony, Ihler, Fowlkes; European Conference on Computer Vision (ECCV), Oct. 2012
C42: [ Abstract ] | [ BibTex ] | [ PDF ]

"Belief propagation for structured decision making"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
C41: [ Abstract ] | [ BibTex ] | [ PDF ]

"A cluster-cumulant expansion at the fixed points of belief propagation"; Welling, Gelfand, Ihler; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
C40: [ Abstract ] | [ BibTex ] | [ PDF ]

"Join-graph based cost-shifting schemes"; Ihler, Flerova, Dechter, Otten; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
C39: [ Abstract ] | [ BibTex ] | [ PDF ]

"A graphical model representation of the track-oriented multiple hypothesis tracker"; Frank, Smyth, Ihler; IEEE Conference on Statistical Signal Processing (SSP), Aug. 2012
C38: [ Abstract ] | [ BibTex ] | [ PDF ]

"Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis"; Geyfman et al.; Proc. National Academy of Sciences (PNAS), 109 (29), pp. 11758-11763.
J14: [ Abstract ] | [ BibTex ] | [ Link ]

"Approximating the sum operation for marginal-MAP inference"; Cheng, Chen, Dong, Xu, Ihler; Conference on Artificial Intelligence (AAAI), July 2012
C37: [ Abstract ] | [ BibTex ] | [ PDF ]

"Distributed parameter estimation via pseudo-likelihood"; Liu, Ihler; International Conference on Machine Learning (ICML), June 2012
C36: [ Abstract ] | [ BibTex ] | [ PDF ]

"Mini-bucket elimination with moment matching"; Flerova, Ihler, Dechter, Otten; NIPS Workshop on Discrete Optimization (DiscML), 2011
R5: [ Abstract ] | [ BibTex ] | [ PDF ]

"Adaptive exact inference in graphical models"; Sumer, Acar, Ihler, Mettu; J. Machine Learning Res. (JMLR) 12, Nov. 2011, pp. 3147-3186.
J13: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Variational algorithms for marginal MAP"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), 2011
C35: [ Abstract ] | [ BibTex ] | [ PDF ]

"Planar cycle covering graphs"; Yarkony, Ihler, Fowlkes; Uncertainty in Artificial Intelligence (UAI), 2011 (earlier arXiv version)
C34: [ Abstract ] | [ BibTex ] | [ PDF ]

"Tightening MRF relaxations with planar subproblems"; Yarkony, Morshed, Ihler, Fowlkes; Uncertainty in Artificial Intelligence (UAI), 2011 (corrected)
C33: [ Abstract ] | [ BibTex ] | [ PDF ]

"Fast parallel and adaptive updates for dual-decomposition solvers"; Sumer, Acar, Ihler, Mettu; Conference on Artificial Intelligence (AAAI), 2011
C32: [ Abstract ] | [ BibTex ] | [ PDF ]

"Bounding the Partition Function using Holder's Inequality"; Liu, Ihler; International Conference on Machine Learning (ICML), 2011
C31: [ Abstract ] | [ BibTex ] | [ PDF ]

"Fault Detection via Nonparametric Belief Propagation"; Bickson, Baron, Ihler, Avissar, Dolev; IEEE Trans. Signal Proc. 59(6), June 2011. (arXiv version)
J12: [ Abstract ] | [ BibTex ] | [ Link ]

"Learning Scale Free Networks by Reweighted L1 regularization"; Liu, Ihler; AI & Statistics, April 2011. (Notable paper award)
C30: [ Abstract ] | [ BibTex ] | [ PDF ]

"Multicore Gibbs Sampling in Dense, Unstructured Graphs"; Xu, Ihler; AI & Statistics, April 2011.
C29: [ Abstract ] | [ BibTex ] | [ PDF ]

"Revisiting MAP Estimation, Message Passing and Perfect Graphs"; Foulds, Navaroli, Smyth, Ihler; AI & Statistics, April 2011.
C28: [ Abstract ] | [ BibTex ] | [ PDF ]

"Understanding Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; IEEE Trans. Knowledge Data Engineering, 24(5), pp.952-960, May 2012. (Preliminary version, 2009: TR-09-06, PDF)
J11: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Nonparametric Belief Propagation"; Sudderth, Ihler, Isard, Freeman, Willsky; Communications of the ACM 53(10), Oct. 2010 pp. 95-103.
J10: [ Abstract ] | [ BibTex ] | [ Link ]

"Negative Tree-reweighted Belief Propagation"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2010
C27: [ Abstract ] | [ BibTex ] | [ PDF ]

"Covering Trees and Lower Bounds on Quadratic Assignment"; Yarkony, Fowlkes, Ihler; Computer Vision & Pattern Recognition (CVPR), June 2010
C26: [ Abstract ] | [ BibTex ] | [ PDF ]

"Particle Filtered MCMC-MLE with Connections to Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; Int'l Conf on Machine Learning (ICML), June 2010
C25: [ Abstract ] | [ BibTex ] | [ PDF ]

"Learning with Blocks: Composite Likelihood and Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; AI & Statistics (AISTATS), April 2010
C24: [ Abstract ] | [ BibTex ] | [ PDF ]

"Estimating Replicate Time-Shifts Using Gaussian Process Regression"; Liu, Lin, Anderson, Smyth, Ihler; Bioinformatics 26(6), Mar. 2010, pp. 770-776; doi:10.1093/bioinformatics/btq022
J9: [ Abstract ] | [ BibTex ] | [ Link ]

"Bayesian detection of non-sinusoidal periodic patterns in circadian expression data"; Chudova, Ihler, Lin, Andersen, Smyth; Bioinformatics 25(23), Dec. 2009, pp. 3114-3120; doi: 10.1093/bioinformatics/btp547.
J8: [ Abstract ] | [ BibTex ] | [ Link ]

"Particle-Based Variational Inference for Continuous Systems"; Ihler, Frank, Smyth; Neural Information Processing Systems, Dec. 2009
C23: [ Abstract ] | [ BibTex ] | [ PDF ]

"Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; ICS Technical Report 09-06, Oct. 2009.
R4: [ Abstract ] | [ BibTex ] | [ PDF ]

"A Low Density Lattice Decoder via Non-parametric Belief Propagation"; Bickson, Ihler, Avissar, Dolev; Allerton Conference on Communication, Control, and Computing, Sept. 2009
C22: [ Abstract ] | [ BibTex ] | [ PDF ]

"Adaptive Updates for MAP Configurations with Applications to Bioinformatics"; Acar, Ihler, Mettu, Sumer; in IEEE Statistical Signal Processing (SSP), Sept. 2009
C21: [ Abstract ] | [ BibTex ] | [ PDF ]

"Circadian Clock Genes Contribute to the Regulation of Hair Follicle Cycling"; Lin, Kumar, Geyfman, Chudova, Ihler, Smyth, Paus, Takahashi, Andersen; PLoS Genetics, 5(7):e1000573. July 2009. doi:10.1371/journal.pgen.1000573
J7: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Particle Belief Propagation"; Ihler, McAllester; in Twelfth International Conference on Artificial Intelligence and Statistics (AIStats), April 2009.
C20: [ Abstract ] | [ BibTex ] | [ PDF ]

"Probabilistic Analysis of a Large Scale Urban Traffic Sensor Data Set"; Hutchins, Ihler, Smyth; in Second International Workshop on Knowledge Discovery from Sensor Data 2008, LNCS series #5840, pp. 94-114, 2010.
C19: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation"; Porteous, Newman, Ihler, Asuncion, Smyth, Welling; in ACM Knowledge Discovery and Data Mining (KDD) 2008.
C18: [ Abstract ] | [ BibTex ] | [ PDF ]

"Adaptive Inference in General Graphical Models"; Acar, Ihler, Mettu, Sumer; in Uncertainty in Artificial Intelligence (UAI) 2008.
C17: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Learning to detect events with Markov-modulated Poisson processes"; Ihler, Hutchins, Smyth; ACM Transactions on Knowledge Discovery from Data, Vol 1 Issue 3, Dec. 2007.
J6: [ Abstract ] | [ BibTex ] | [ Link ]

"Graphical Models and Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Kreidl, Moses, Wainwright, Williams, Willsky; in Wireless Sensor Networks: Signal Processing and Communications, Wiley 2007.
B1: [ Abstract ] | [ BibTex ] | [ Link ]

"Adaptive Bayesian Inference"; Acar, Ihler, Mettu, Sumer; in Neural Information Processing Systems (NIPS) 2007.
C16: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Modeling Count Data from Multiple Sensors: A Building Occupancy Model"; Hutchins, Ihler, Smyth; in Computational Advances in Multisensor Adaptive Processing (CAMSAP) 2007.
C15: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Accuracy Bounds for Belief Propagation"; Ihler; in Uncertainty in Artificial Intelligence (UAI) 2007.
C14: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Graphical Models for Statistical Inference and Data Assimilation"; Ihler, Kirshner, Ghil, Robertson, Smyth; Physica D: Nonlinear Phenomena, June 2007. (Survey of graphical model methods)
J5: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

"Learning Time-Intensity Profiles of Human Activity Using Nonparametric Bayesian Models"; Ihler, Smyth; in Neural Information Processing Systems (NIPS) 2006.
C13: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Adaptive Event Detection with Time-Varying Poisson Processes"; Ihler, Hutchins, Smyth; in Knoweldge Discovery and Data Mining (KDD) 2006.
C12: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Distributed Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Moses, Wainwright, Willsky; IEEE Signal Processing Magazine, July 2006.
J4: [ Abstract ] | [ BibTex ] | [ PDF ]

"Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation"; Porteous, Ihler, Smyth, Welling; in Uncertainty in Artificial Intelligence (UAI) 2006.
C11: [ Abstract ] | [ BibTex ] | [ PDF ]

"Particle Filtering Under Communications Constraints"; Ihler, Fisher, Willsky; in Statistical Signal Processing (SSP) 2005.
C10: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Loopy Belief Propagation: Convergence and Effects of Message Errors"; Ihler, Fisher, Willsky; Journal of Machine Learning Research, May 2005. (Full version of NIPS'04 paper)
J3: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; Journal of Selected Areas in Communication, Apr. 2005. (Expanded version of IPSN/ICASSP papers)
J2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Estimating Dependency and Significance for High-Dimensional Data"; Siracusa, Tieu, Ihler, Fisher, Willsky; in ICASSP 2005.
C9: [ Abstract ] | [ BibTex ] | [ PDF ]

"Inference in Sensor Networks: Graphical Models and Particle Methods"; Ihler; Ph.D. Thesis, MIT, 2005
T2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS (zipped) ]

"Message Errors in Belief Propagation"; Ihler, Fisher, Willsky; in Neural Information Processing Systems (NIPS) 2004. (Outstanding Student Paper Award)
C8: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Hypothesis Tests for Statistical Dependency"; Ihler, Fisher, Willsky; IEEE Transactions on Signal Processing, Aug. 2004.
J1: [ Abstract ] | [ BibTex ] | [ PDF ]

"Communications-Constrained Inference"; Ihler, Fisher, Willsky; LIDS Tech Report 2601 (Lossless and lossy encoding of sample-based density estimates)
R3: [ Abstract ] | [ BibTex ] | [ PDF ]

"Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; in ICASSP 2004.
C7: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"An Overview of Fast Multipole Methods"; Ihler; 2004 (MIT Area Exam)
R1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Belief Propagation for Self-Calibration in Sensor Networks"; Ihler, Fisher, Moses, Willsky; in Information Processing in Sensor Networks (IPSN) 2004. (Best Student Paper Award)
C6: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Efficient Multiscale Sampling from Products of Gaussian Mixtures"; Ihler, Sudderth, Freeman, Willsky; in Neural Information Processing Systems (NIPS) 2003.
C5: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; in Computer Vision and Pattern Recognition (CVPR) 2003. (also AI Memo # AIM-2002-020)
C4: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Hypothesis Testing over Factorizations for Data Association"; Ihler, Fisher, Willsky; in Information Processing in Sensor Networks (IPSN) 2003.
C3: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; LIDS Technical Report # 2551, Aug. 2002.
R2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Nonparametric Estimators for Online Signature Authentication"; Ihler, Fisher, Willsky; in ICASSP 2001.
C2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

"Maximally Informative Subspaces: Nonparametric Estimation for Dynamical Systems"; Ihler; Masters' Thesis, MIT, 2000
T1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS (zipped) ]

"Learning Informative Statistics: A Nonparametric Approach"; Fisher, Ihler, Viola; in Neural Information Processing Systems (NIPS) 1999.
C1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]