Research
Machine Learning Laboratory
I direct the Machine Learning Laboratory. We investigate the problem of machine learning for complex phenomena. We focus on rich settings where data is best modeled as multi-relational, multi-modal networks. We emphasize the joint exploration of theoretical, algorithmic, and applied research and aim to exploit connections within these views to strengthen understanding of each.
Graduate Students
Walid Chaabene
Shuangfei Fan
Elaheh Raisi
Sirui Yao
General Information
Curriculum vitae
bibtex listing
Google scholar author page
2016
A Weakly Supervised Approach for Adaptive Detection of Cyberbullying Roles
B. Huang. Workshop on Computational Methods for CyberSafety. Invited talk.
Abstract
Training Iterative Collective Classifiers with Back-Propagation
S. Fan, B. Huang. Workshop on Mining and Learning with Graphs.
Paper
Cyberbullying Identification Using Participant-Vocabulary Consistency
E. Raisi, B. Huang. #Data4Good: Machine Learning in Social Good Applications. ICML 2016 workshop.
Paper
Stability and Generalization in Structured Prediction
B. London, B. Huang, L. Getoor. Journal of Machine Learning Research. To appear.
Preprint
Machine Learning for Detecting Detrimental Online Social Behavior
B. Huang, E. Raisi. Computing Community Consortium (CCC) Symposium on Computing Research: Addressing National Priorities and Societal Needs.
Video, Poster, CCCBlog post
Capturing Planned Protests from Open Source Indicators
S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, N. Ramakrishnan. Artificial Intelligence Magazine.
Paper
2015
Paired-Dual Learning for Training Hinge-Loss MRFs with Latent Variables
S. Bach, B. Huang, J. Boyd-Graber, L. Getoor. International Conference on Machine Learning (ICML) 2015.
Paper, Talk, Related Talk
The Benefits of Learning with Strongly Convex Approximate Inference
B. London, B. Huang, L. Getoor. International Conference on Machine Learning (ICML) 2015
Paper, Talk
Joint Models of Disagreement and Stance in Online Debate
D. Sridhar, J. Foulds, M. Walker, B. Huang, L. Getoor. Annual Meeting of the Association for Computational Linguistics (ACL) 2015
Paper
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees
S. Bach, B. Huang, L. Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2015
Paper
Planned Protest Modeling in News and Social Media
S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, N. Ramakrishnan. Conference on Innovative Applications of Artificial Intelligence (IAAI) 2015. Deployed Application Award.
Paper
2014
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
S. Fakhraei, B. Huang, L. Raschid, L. Getoor. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Paper
Discovering Evolving Political Vocabulary in Social Media
A. Mahendiran, W. Wang, J. Arredondo, B. Huang, L. Getoor, D. Mares, N. Ramakrishnan. International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC) 2014.
Paper
‘Beating the News’ With EMBERS: Forecasting Civil Unrest Using Open Source Indicators
N. Ramakrishnan, P. Butler, N. Self, R. Khandpur, P. Saraf, W. Wang, J. Cadena, A. Vullikanti, G. Korkmaz, C. Kuhlman, A. Marathe, L. Zhao, H. Ting, B. Huang, A. Srinivasan, K. Trinh, L. Getoor, G. Katz, A. Doyle, C. Ackermann, I. Zavorin, J. Ford, K. Summers, Y. Fayed, J. Arredondo, D. Gupta, D. Mares. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2014.
Paper
Learning Latent Engagement Patterns of Students in Online Courses
A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. AAAI Conference on Artificial Intelligence 2014.
Paper
PAC-Bayesian Collective Stability
B. London, B. Huang, and L. Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2014.
Paper
Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies
S. Bach, B. Huang, L. Getoor. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) 2014.
Paper
On the Strong Convexity of Variational Inference
B. London, B. Huang, L. Getoor. NIPS Workshop on Advances in Variational Inference 2014.
Paper
Collective Classification of Stance and Disagreement in Online Debate Forums
D. Sridhar, J. Foulds, B. Huang, M. Walker, L. Getoor. Bay Area Machine Learning Symposium 2014.
Paper
Probabilistic Soft Logic for Social Good
S. Bach, B. Huang, L. Getoor. KDD Workshop on Data Science for Social Good 2014.
Paper
Understanding MOOC Discussion Forums using Seeded LDA
A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. ACL Workshop on Innovative Use of NLP for Building Educational Applications, 2014.
Paper
Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs
A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. ACM Conference on Learning at Scale, Works-in-Progress Track. 2014.
Paper
2013
A Hypergraph-Partitioned Vertex Programming Approach for Large-Scale Consensus Optimization.
H. Miao, X. Liu, B. Huang, L. Getoor. IEEE International Conference on Big Data, 2013.
Paper
Hinge-Loss Markov Random Fields: Convex Inference for Structured Prediction
S. Bach, B. Huang, B. London, L. Getoor. Conference on Uncertainty in Artificial Intelligence (UAI) 2013.
Paper
Collective Stability in Structured Prediction: Generalization from One Example
B. London, B. Huang, B. Taskar, L. Getoor. International Conference on Machine Learning (ICML) 2013. Oral presentation.
Paper
A Flexible Framework for Probabilistic Models of Social Trust
B. Huang, A. Kimmig, L. Getoor, J. Golbeck. International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP) 2013.
Paper
Large-Margin Structured Learning for Link Ranking
S. Bach, B. Huang, L. Getoor. NIPS Workshop on Frontiers of Network Analysis 2013. Best Student Paper Award.
Paper
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. NIPS Workshop on Data Driven Education 2013.
Paper
PAC-Bayes Generalization Bounds for Randomized Structured Prediction
B. London, B. Huang, B. Taskar, L. Getoor. NIPS Workshop on Perturbation, Optimization, and Statistics 2013.
Paper
Collective Inference and Multi-Relational Learning for Drug-Target Interaction Prediction
S. Fakhraei, B. Huang, L. Getoor. NIPS Workshop on Machine Learning in Computational Biology 2013
Fairness in Assignment Markets with Dual Decomposition
B. Huang. ICML Workshop on Peer Review and Publishing Models 2013.
Paper
Empirical Analysis of Collective Stability
B. Huang, B. London, B. Taskar, L. Getoor. ICML Workshop on Structured Learning 2013
Paper
Collective Activity Detection using Hinge-Loss Markov Random Fields
B. London, S. Khamis, S. Bach, B. Huang, L. Getoor, L. Davis. CVPR Workshop on Structured Prediction 2013
Paper
Learning Latent Groups with Hinge-Loss Markov Random Fields
S. Bach, B. Huang, L. Getoor. ICML Workshop on Interactions between Inference and Learning (Inferning) 2013
Paper
Graph-Based Generalization Bounds for Learning Binary Relations
B. London, B. Huang, and L. Getoor.
arxiv
Multi-Relational Learning Using Weighted Tensor Decomposition with Modular Loss
B. London, T. Rekatsinas, B. Huang, and L. Getoor.
arxiv
2012
Social Group Modeling with Probabilistic Soft Logic
B. Huang, S. Bach, E. Norris, J. Pujara, and L. Getoor. NIPS 2012 Workshop on Social Network and Social Media Analysis: Methods, Models, and Applications.
Paper
Improved Generalization Bounds for Large-Scale Structured Prediction
B. London, B. Huang, and L. Getoor. NIPS 2012 Workshop on Algorithmic and Statistical Approaches for Large Social Networks.
Paper
Multi-Relational Weighted Tensor Decomposition
B. London, T. Rekatsinas, B. Huang, and L. Getoor. NIPS 2012 Workshop on Spectral Learning.
Paper
A Short Introduction to Probabilistic Soft Logic
A. Kimmig, S. Bach, M. Broecheler, B. Huang, and L. Getoor. NIPS 2012 Workshop on Probabilistic Programming: Foundations and Applications. Oral presentation
Paper
Probabilistic Soft Logic for Trust Analysis in Social Networks
B. Huang, A. Kimmig, L. Getoor, and J. Golbeck. International Workshop on Statistical Relational Artificial Intelligence (StaRAI). UAI 2012 workshop.
Paper, poster, and slides
Query-Driven Active Surveying for Collective Classification
G. Namata, B. London, L. Getoor, and B. Huang. International Conference on Machine Learning 2012 Workshop: Mining and Learning with Graphs (MLG). Oral presentation
Paper
Semantic Model Vectors for Complex Video Event Recognition
M. Merler, B. Huang, L. Xie, G. Hua, and A. Natsev. IEEE Transactions on Multimedia, Vol. 14, No. 1, February 2012.
Preprint
Machine Learning for the New York City Power Grid
C. Rudin, D. Waltz, R. Anderson, A. Boulanger, A. Salleb-Aouissi, M. Chow, H. Dutta, P. Gross, B. Huang, S. Ierome, D. Isaac, A. Kressner, R. Passonneau, A. Radeva, and L. Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence. February 2012.
Preprint
2011
Learning a Distance Metric from a Network
B. Shaw, B. Huang, and T. Jebara. Neural Information Processing Systems (NIPS) 2011.
Paper, supplementary appendix, code, poster
Learning a Degree-Augmented Distance Metric from a Network
B. Huang, B. Shaw, and T. Jebara. Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity. NIPS 2011 workshop. Oral presentation
Abstract and slides
Learning with Degree-Based Subgraph Estimation
B. Huang
Ph.D. Dissertation. Columbia University
Thesis
Network Prediction with Degree Distributional Metric Learning
B. Huang, B. Shaw, and T. Jebara. Interdisciplinary Workshop on Information and Decision in Social Networks (WIDS 2011).
Poster and abstract
Fast b-Matching via Sufficient Selection Belief Propagation
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2011.
Paper, code, poster, clarification.
2010
Collaborative Filtering via Rating Concentration
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2010.
Paper, code archive, poster
2009
Learning with Subgraph Estimation and Degree Priors
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium, November 2009.
Paper
Exact Graph Structure Estimation with Degree Priors
B. Huang and T. Jebara. International Conference on Machine Learning and Applications (ICMLA) 2009.
Paper
Alive on Back-Feed Culprit Identification via Machine Learning
B. Huang, A. Salleb-Aouissi, and P. Gross. International Conference on Machine Learning and Applications (ICMLA) 2009. Special Session on Machine Learning in Energy Applications.
Paper
Discovering Characterization Rules from Rankings
A. Salleb-Aouissi, B. Huang, and D. Waltz. International Conference on Machine Learning and Applications (ICMLA) 2009.
Paper
Maximum Entropy Density Estimation with Incomplete Presence-Only Data
B. Huang and A. Salleb-Aouissi. International Conference on Artificial Intelligence and Statistics (AISTATS) 2009.
Paper, poster
Approximating the Permanent with Belief Propagation
B. Huang and T. Jebara. Columbia University Technical Report. 2009
Paper, arxiv, code archive
2008
Maximum Likelihood Graph Structure Estimation with Degree Distributions
B. Huang and T. Jebara. Analyzing Graphs: Theory and Applications, NIPS Workshop, December 2008.
Paper
Vers des Machines a Vecteurs de Support "Actionables": Une Approche Fondee sur le Classement. (Toward Actionable Support Vector Machines: A Ranking Based Approach)
A. Salleb-Aouissi, B. Huang, and D. Waltz. Knowledge Extraction and Management (Extraction et Gestion des Connaissances) EGC 2008, Sophia Antipolis, France. Best Paper Award.
2007
Approximating the Permanent with Belief Propagation
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium 2007.
Poster and abstract
Maximum Entropy Density Estimation with Incomplete Data
B. Huang and A. Salleb-Aouissi. New York Academy of Sciences Machine Learning Symposium 2007.
Poster and abstract.
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2007. Oral presentation.
Paper, slides, code.
2006
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium 2006.
Poster and abstract.
Misc.
spouterprod.c. A useful mex utility to compute sparse outer products in matlab, taking advantage in computation time and memory usage when you are interested in computing something like mask.*(U*V'), where mask is a sparse binary matrix.
Some projects on github.