| 2008 |
| 62 | EE | Mark Silberstein,
Assaf Schuster,
Dan Geiger,
Anjul Patney,
John D. Owens:
Efficient computation of sum-products on GPUs through software-managed cache.
ICS 2008: 309-318 |
| 61 | EE | Sivan Bercovici,
Dan Geiger,
Liran Shlush,
Karl Skorecki,
Alan Templeton:
Panel Construction for Mapping in Admixed Populations Via Expected Mutual Information.
RECOMB 2008: 435-449 |
| 60 | EE | Ydo Wexler,
Dan Geiger:
Variational Upper and Lower Bounds for Probabilistic Graphical Models.
Journal of Computational Biology 15(7): 721-735 (2008) |
| 2007 |
| 59 | EE | Ydo Wexler,
Dan Geiger:
Variational Upper Bounds for Probabilistic Phylogenetic Models.
RECOMB 2007: 226-237 |
| 58 | EE | Ron Zohar,
Dan Geiger:
Estimation of flows in flow networks.
European Journal of Operational Research 176(2): 691-706 (2007) |
| 2006 |
| 57 | EE | Mark Silberstein,
Dan Geiger,
Assaf Schuster:
A Distributed System for Genetic Linkage Analysis.
GCCB 2006: 110-123 |
| 56 | EE | Mark Silberstein,
Dan Geiger,
Assaf Schuster,
Miron Livny:
Scheduling Mixed Workloads in Multi-grids: The Grid Execution Hierarchy.
HPDC 2006: 291-302 |
| 55 | EE | Dan Geiger,
Christopher Meek,
Ydo Wexler:
A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints.
J. Artif. Intell. Res. (JAIR) 27: 1-23 (2006) |
| 2005 |
| 54 | EE | Ydo Wexler,
Zohar Yakhini,
Yechezkel Kashi,
Dan Geiger:
Finding Approximate Tandem Repeats in Genomic Sequences.
Journal of Computational Biology 12(7): 928-942 (2005) |
| 53 | EE | Dmitry Rusakov,
Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks.
Journal of Machine Learning Research 6: 1-35 (2005) |
| 2004 |
| 52 | EE | Gideon Greenspan,
Dan Geiger:
High density linkage disequilibrium mapping using models of haplotype block variation.
ISMB/ECCB (Supplement of Bioinformatics) 2004: 137-144 |
| 51 | EE | Vladimir Jojic,
Nebojsa Jojic,
Christopher Meek,
Dan Geiger,
Adam C. Siepel,
David Haussler,
David Heckerman:
Efficient approximations for learning phylogenetic HMM models from data.
ISMB/ECCB (Supplement of Bioinformatics) 2004: 161-168 |
| 50 | EE | Ydo Wexler,
Zohar Yakhini,
Yechezkel Kashi,
Dan Geiger:
Finding approximate tandem repeats in genomic sequences.
RECOMB 2004: 223-232 |
| 49 | EE | Maáyan Fishelson,
Dan Geiger:
Optimizing Exact Genetic Linkage Computations.
Journal of Computational Biology 11(2/3): 263-275 (2004) |
| 48 | EE | Gideon Greenspan,
Dan Geiger:
Model-Based Inference of Haplotype Block Variation.
Journal of Computational Biology 11(2/3): 493-504 (2004) |
| 2003 |
| 47 | EE | Maáyan Fishelson,
Dan Geiger:
Optimizing exact genetic linkage computations.
RECOMB 2003: 114-121 |
| 46 | EE | Gideon Greenspan,
Dan Geiger:
Model-based inference of haplotype block variation.
RECOMB 2003: 131-137 |
| 45 | | Ari Frank,
Dan Geiger,
Zohar Yakhini:
A Distance-Based Branch and Bound Feature Selection Algorithm.
UAI 2003: 241-248 |
| 44 | | Dmitry Rusakov,
Dan Geiger:
Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models.
UAI 2003: 501-508 |
| 2002 |
| 43 | | Maáyan Fishelson,
Dan Geiger:
Exact genetic linkage computations for general pedigrees.
ISMB 2002: 189-198 |
| 42 | | Dan Geiger,
Christopher Meek,
Bernd Sturmfels:
Factorization of Discrete Probability Distributions.
UAI 2002: 162-169 |
| 41 | | Dmitry Rusakov,
Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks.
UAI 2002: 438-445 |
| 2001 |
| 40 | EE | Ann Becker,
Dan Geiger:
A sufficiently fast algorithm for finding close to optimal clique trees.
Artif. Intell. 125(1-2): 3-17 (2001) |
| 2000 |
| 39 | EE | Ann Becker,
Dan Geiger,
Christopher Meek:
Perfect Tree-like Markovian Distributions.
UAI 2000: 19-23 |
| 38 | EE | Nir Friedman,
Dan Geiger,
Noam Lotner:
Likelihood Computations Using Value Abstraction.
UAI 2000: 192-200 |
| 37 | EE | Ann Becker,
Reuven Bar-Yehuda,
Dan Geiger:
Randomized Algorithms for the Loop Cutset Problem.
J. Artif. Intell. Res. (JAIR) 12: 219-234 (2000) |
| 1999 |
| 36 | EE | Kristin P. Bennett,
Usama M. Fayyad,
Dan Geiger:
Density-Based Indexing for Approximate Nearest-Neighbor Queries.
KDD 1999: 233-243 |
| 35 | EE | Dan Geiger,
James Cussens:
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions.
UAI 1999: 216-225 |
| 34 | EE | Dan Geiger,
Christopher Meek:
Quantifier Elimination for Statistical Problems.
UAI 1999: 226-235 |
| 33 | EE | Ann Becker,
Reuven Bar-Yehuda,
Dan Geiger:
Random Algorithms for the Loop Cutset Problem.
UAI 1999: 49-56 |
| 32 | EE | Laxmi Parida,
Dan Geiger:
Mass Estimation of DNA Molecules and Extraction of Ordered Restriction Maps in Optical Mapping Imagery.
Algorithmica 25(2-3): 295-310 (1999) |
| 1998 |
| 31 | EE | Dan Geiger:
Graphical Models and Exponential Families.
UAI 1998: 156-165 |
| 30 | | Dan Geiger,
David Heckerman:
Probabilistic relevance relations.
IEEE Transactions on Systems, Man, and Cybernetics, Part A 28(1): 17-25 (1998) |
| 29 | EE | Reuven Bar-Yehuda,
Dan Geiger,
Joseph Naor,
Ron M. Roth:
Approximation Algorithms for the Feedback Vertex Set Problem with Applications to Constraint Satisfaction and Bayesian Inference.
SIAM J. Comput. 27(4): 942-959 (1998) |
| 1997 |
| 28 | | Dan Geiger,
Prakash P. Shenoy:
UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, August 1-3, 1997, Brown University, Providence, Rhode Island, USA
Morgan Kaufmann 1997 |
| 27 | | Kirill Shoikhet,
Dan Geiger:
A Practical Algorithm for Finding Optimal Triangulations.
AAAI/IAAI 1997: 185-190 |
| 26 | | Nir Friedman,
Dan Geiger,
Moisés Goldszmidt:
Bayesian Network Classifiers.
Machine Learning 29(2-3): 131-163 (1997) |
| 1996 |
| 25 | EE | Dan Geiger,
David Heckerman,
Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables.
UAI 1996: 283-290 |
| 24 | EE | Ann Becker,
Dan Geiger:
A sufficiently fast algorithm for finding close to optimal junction trees.
UAI 1996: 81-89 |
| 23 | EE | Dan Geiger,
David Heckerman:
Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets.
Artif. Intell. 82(1-2): 45-74 (1996) |
| 22 | EE | Ann Becker,
Dan Geiger:
Optimization of Pearl's Method of Conditioning and Greedy-Like Approximation Algorithms for the Vertex Feedback Set Problem.
Artif. Intell. 83(1): 167-188 (1996) |
| 1995 |
| 21 | EE | Dan Geiger,
David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks.
UAI 1995: 196-207 |
| 20 | EE | David Heckerman,
Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains.
UAI 1995: 274-284 |
| 19 | EE | David Maxwell Chickering,
Dan Geiger,
David Heckerman:
On Finding a Cycle Basis with a Shortest Maximal Cycle.
Inf. Process. Lett. 54(1): 55-58 (1995) |
| 18 | | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
Machine Learning 20(3): 197-243 (1995) |
| 17 | EE | Amir Eliaz,
Dan Geiger:
Word-level recognition of small sets of hand-written words.
Pattern Recognition Letters 16(10): 999-1009 (1995) |
| 1994 |
| 16 | | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
KDD Workshop 1994: 85-96 |
| 15 | | Reuven Bar-Yehuda,
Dan Geiger,
Joseph Naor,
Ron M. Roth:
Approximation Algorithms for the Vertex Feedback Set Problem with Applications to Constraint Satisfaction and Bayesian Inference.
SODA 1994: 344-354 |
| 14 | EE | Dan Geiger,
David Heckerman:
Learning Gaussian Networks.
UAI 1994: 235-243 |
| 13 | EE | Dan Geiger,
Azaria Paz,
Judea Pearl:
On Testing Whether an Embedded Bayesian Network Represents a Probability Model.
UAI 1994: 244-252 |
| 12 | EE | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
UAI 1994: 293-301 |
| 11 | EE | Ann Becker,
Dan Geiger:
Approximation Algorithms for the Loop Cutset Problem.
UAI 1994: 60-68 |
| 1993 |
| 10 | EE | Dan Geiger,
David Heckerman:
Inference Algorithms for Similarity Networks.
UAI 1993: 326-334 |
| 1992 |
| 9 | EE | Dan Geiger:
An Entropy-based Learning Algorithm of Bayesian Conditional Trees.
UAI 1992: 92-97 |
| 1991 |
| 8 | | Dan Geiger,
Jeffrey A. Barnett:
Optimal Satisficing Tree Searches.
AAAI 1991: 441-445 |
| 7 | EE | Dan Geiger,
David Heckerman:
Advances in Probabilistic Reasoning.
UAI 1991: 118-126 |
| 6 | | Dan Geiger,
Azaria Paz,
Judea Pearl:
Axioms and Algorithms for Inferences Involving Probabilistic Independence
Inf. Comput. 91(1): 128-141 (1991) |
| 1990 |
| 5 | | Dan Geiger,
Azaria Paz,
Judea Pearl:
Learning Causal Trees from Dependence Information.
AAAI 1990: 770-776 |
| 4 | EE | Dan Geiger,
David Heckerman:
separable and transitive graphoids.
UAI 1990: 65-76 |
| 3 | | Dan Geiger,
Judea Pearl:
Logical and algorithmic properties of independence and their application to Bayesian networks.
Ann. Math. Artif. Intell. 2: 165-178 (1990) |
| 1989 |
| 2 | EE | Dan Geiger,
Thomas Verma,
Judea Pearl:
d-Separation: From Theorems to Algorithms.
UAI 1989: 139-148 |
| 1988 |
| 1 | EE | Dan Geiger,
Judea Pearl:
On the logic of causal models.
UAI 1988: 3-14 |