Proceedings

Proceedings of Machine Learning Research (vol. 73)

Program

PDF (EN, ver.2, Aug. 21), PDF (JP, ver. 2, Aug. 21)

#1, #2,... stand for paper numbers that were sequentially asigned when submitted. Besides the invited and contributed talks, PC asked seven talks for discussion (marked as "*").

Day 1 (Wednesday, September. 20)

(Registration 9:00 -)

9:15-9:30 Openeng

Special Session: Causality (1) chaired by Antti Hyttinen

9:30-10:30Invited TalkKun Zhang Causal Learning and Machine Learning
10:30-10:55#3Jose M. PenaCausal Effect Identification in Alternative Acyclic Directed Mixed Graphs
10:55-11:20#5Jose M. PenaLearning Causal AMP Chain Graphs
11:20-11:50*Hisayuki HaraIdentifiability of directed Graphical Models with a Latent Variable

11:50-13:20 Lunch

Special Session: Causality (2) chaired by Antti Hyttinen

13:20-13:50*Mario Nagase Causal Discovery with Cyclic Structural Equation Models
13:50-14:20*Antti Hyttinen A Constraint Optimization Approach to Causal Discovery from Subsampled Time Series Data

Structure learning (1) chaired by Peter van Beek

14:20-14:45#6Mauro Scanagatta, Giorgio Corani, and Marco Zaffalon Improved Local Search in Bayesian Networks Structure Learning
14:45-15:10#16Kazuki Natori, Masaki Uto, and Maomi Ueno Consistent Learning Bayesian Networks with Thousands of Variables

15:10-15:25 Break

Structure learning (2) chaired by Peter van Beek

15:25-15:50#18Colin Lee and Peter van Beek An experimental analysis of anytime algorithms for Bayesian network structure learning
15:50-16:15#12Yun Zhou, Jiang Wang, and Cheng Zhu Multiple DAGs Learning with Non-negative Matrix Factorization
16:15-16:40#21 Zhigao Guo, Xiaoguang Gao, Ruohai Di, and Hao Ren Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data

Day 2 (Thursday, September. 21)

Discrete & Logic (1) chaired by Masakazu Ishihata

9:00-10:00Invited TalkTaisuke Sato Learning probability by comparison
10:00-10:25#20Hei ChanIncorporating Uncertain Evidence Into Arithmetic Circuits Representing Probability Distributions

10:25-10:40 Break

Discrete & Logic (2) chaired by Masakazu Ishihata

10:40-11:05#17Shan Gao, Masakazu Ishihata and Shin-Ichi MinatoFast Message Passing Algorithm Using ZDD-Based Local Structure Compilation
11:05-11:30#15Teruji Sugaya, Masaaki Nishino, Norihito Yasuda and Shin-Ichi MinatoFast Compilation of s-t Paths on a Graph for Counting and Enumeration
11:30-11:55#19Giso Dal, Peter Lucas and Steffen MichelsReducing the Cost of Probabilistic Knowledge Compilation
11:55-12:20#14Kei Amii, Masaaki Nishino and Akihiro YamamotoOn the Sizes of Decision Diagrams Representing the Set of All Parse Trees of a Context-free Grammar

12:20-13:50 Lunch

Special Session: BDeu (1) chaired by Joe Suzuki

13:50-14:50 Invited TalkWray Buntine Backoff methods for estimating parameters of a Bayesian network

14:50-15:05 Break

Special Session: BDeu (2)

15:05-16:05 Invited Talk Tomi Silander Hyperparameter sensitivity revisited
16:05-16:35 * Maomi Ueno and Shouta Sugahara Does BDe really reflect the prior knowledge?

Day 3 (Friday, September. 22)

Special Session: BDeu (3) chaired by Joe Suzuki

9:00-10:00Invited TalkMarco Scutari Bayesian Dirichlet Bayesian Network Scores and the Maximum Entropy Principle
10:00-10:30*Joe SuzukiBranch and Bound for Bayesian network Structure Learning

10:30-10:45 Break

Machine Learning chaired by Brandon Malone

10:45-11:10#7Keisuke Yamazaki and Yoichi MotomuraHidden Node Detection between Two Observable Nodes Based on Bayesian Clustering
11:10-11:35#11Aditya Jitta and Arto KlamiFew-to-few Cross-domain Object Matching

Applications (1) chaired by Brandon Malone

11:35-12:00*Brandon MaloneBayesian Prediction of Translation from Ribosome Profiling

12:00-13:30 Lunch

Applications (2) chaired by Brandon Malone

13:30-14:30Invited TalkJohn Halloran Analyzing Tandem Mass Spectra: A Graphical Models Perspective
14:30-14:55#24Naoto Takahashi and Yuuji IchisugiRestricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas
14:55-15:25*Yoichi Motomura Bayesian Networks and Probabilistic Latent Semantic Analysis: AI Applications Utilizing Big Data and its Future

15:25-15:30 Closing