Bert Huang

I direct the Machine Learning Laboratory. We investigate machine learning for complex phenomena. Our research addresses challenges inherent in the modeling of the connected world. We focus on a balance of theoretical analysis, algorithm development, and applied research to advance knowledge on the entire spectrum of machine learning science.

 

Graduate Students

 

Undergraduate Students

 

Alumni

 

2020

Attention-Based Graph Evolution
S. Fan, B. Huang. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
Paper

Structured Output Learning with Conditional Generative Flows
Y. Lu, B. Huang. AAAI Conference on Artificial Intelligence.
arXiv

Machine Learning Applications in Orthopaedic Imaging
V. Wang, C. Cheung, A. Kozar, B. Huang. Journal of the American Academy of Orthopaedic Surgeons.
Publisher Link

2019

Adversarial Label Learning
C. Arachie, B. Huang. AAAI Conference on Artificial Intelligence.
Paper

Block Belief Propagation for Parameter Learning in Markov Random Fields
Y. Lu, Z. Liu, B. Huang. AAAI Conference on Artificial Intelligence.
arXiv

Reduced-Bias Co-Trained Ensembles for Weakly Supervised Cyberbullying Detection
E. Raisi, B. Huang. International Conference on Computational Data and Social Networks (CSoNet).
Preprint. Best Paper Runner Up.

Structured Output Learning with Conditional Generative Flows
Y. Lu, B. Huang. ICML Workshop on Invertible Neural Networks and Normalizing Flows.
Paper, Poster

Deep Generative Models for Generating Labeled Graphs
S. Fan, B. Huang. ICLR Workshop on Deep Generative Models for Highly Structured Data.
Paper

Conditional Labeled Graph Generation with GANs
S. Fan, B. Huang. ICLR Workshop on Representation Learning on Graphs and Manifolds.
Paper

2018

Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields
W. Chaabene, B. Huang. IEEE International Conference on Big Data.
Paper, Slides

Weakly Supervised Cyberbullying Detection using Co-trained Ensembles of Embedding Models
E. Raisi, B. Huang. IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM).
Paper.

Sparse-Matrix Belief Propagation
R. Bixler, B. Huang. Conference on Uncertainty in Artificial Intelligence.
Paper, code.

Recurrent Collective Classification
S. Fan, B. Huang. Knowledge and Information Systems.
Springer Online First Link

Weakly Supervised Cyberbullying Detection with Participant-Vocabulary Consistency
E. Raisi, B. Huang. Social Network Analysis and Mining. Publisher Link, Preprint

Weak Supervision and Machine Learning for Online Harassment Detection
B. Huang, E. Raisi. Book Chapter in Online Harassment. Editor: Jennifer Golbeck. Pages 5–28. Springer.
Publisher Link

An Adversarial Labeling Game for Learning from Weak Supervision
C. Arachie, B. Huang. NeurIPS Workshop on Smooth Games Optimization.
Paper.

On the Need for Fairness in Financial Recommendation Engines
S. Yao, B. Huang. NeurIPS Workshop Challenges and Opportunities for AI in Finance.
Paper

Adversarial Learning for Weak Supervision
C. Arachie, B. Huang. Black in Artificial Intelligence Workshop.

Using Hierarchical Clustering and Hoeffding Sampling to Label an Unlabeled Dataset
A. Herbst, B. Huang. Women in Machine Learning Workshop.

What Aspects of Training Data Affect Recommendation Unfairness
S. Yao, B. Huang. Women in Machine Learning Workshop.

Integrating Machine Learning to Improve Optimal Estimation of Atmospheric Composition
B. Huang, C. Arachie, Elena Spinei, Natalya Kramarova, Krzystof Wargan. NASA Goddard Workshop on Artificial Intelligence.

Machine Learning of a Priori Information in Optimal Estimation of Atmospheric Composition
Y. Dong, B. Huang, C. Arachie, Elena Spinei, Natalya Kramarova, Krzystof Wargan. American Geophysical Union (AGU) Fall Meeting.

Machine Learning Approaches for Automated Detection of Cyberviolence
E. Raisi, L. Ireland, B. Huang, J. Hawdon, A. Peguero. Southern Sociological Society Annual Meeting.

Establishing a Virtual Social Laboratory for Investigating Cyberviolence
B. Huang, A. Peguero, J. Hawdon. Southern Sociological Society Annual Meeting.

The Detection of Patellar Tendinopathy Using Machine Learning Analysis of Ultrasound Images
E. Hammet, G. Iliff, S. Rezvani, B. Huang, A. Kozar, V. Wang. Orthopaedic Research Society Annual Meeting.

2017

A Weakly Supervised Deep Model for Cyberbullying Detection
E. Raisi, B. Huang. Women in Machine Learning Workshop.

Fairness and Accuracy in Recommendation with Imbalanced Data Sparsity
S. Yao, B. Huang. Women in Machine Learning Workshop.

Co-trained Ensemble Models for Weakly Supervised Cyberbullying Detection
E. Raisi, B. Huang. NeurIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond. Best Paper Award.
Paper, Poster.

Beyond Parity: Fairness Objectives for Collaborative Filtering
S. Yao, B. Huang. Advances in Neural Information Processing Systems (NeurIPS).
Paper, Video

Cyberbullying Detection with Weakly Supervised Machine Learning
E. Raisi, B. Huang. IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM)
Preprint. Best Paper Prize.

New Fairness Metrics for Recommendation that Embrace Differences
S. Yao, B. Huang. Workshop on Fairness, Accountabilty, and Transparency in Machine Learning (FATML), KDD 2017 Workshop.
arXiv

Online Edge Grafting for Efficient MRF Structure Learning
W. Chaabene, B. Huang.
arXiv

Recurrent Collective Classification
S. Fan, B. Huang. International Conference on Network Science (NetSci) and Satellite Symposium on Machine Learning in Network Science 2017.
Abstract

Detecting Cyber Bullying: But Can it Be Stopped?
Robbie Harris, WVTF Public Radio Interview.
Radio Interview

Virginia Tech professor builds algorithm to detect traces of cyberbullying
Izzy Rossi, News Editor, Collegiate Times.
Press Article

Dispelling five common myths about cyberbullying
Ceci Leonard, Media Relations, Virginia Tech.
Press Article

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. Volume 17.
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

The Benefits of Learning with Strongly Convex Approximate Inference
B. London, B. Huang, L. Getoor. International Conference on Machine Learning (ICML) 2015
Paper, Talk

Paired-Dual Learning for Circumventing the Inference Bottleneck
B. Huang. New Perspectives on Relational Learning, Banff International Research Station for Mathematical Innovation and Discovery
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. NeurIPS 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. NeurIPS 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. NeurIPS 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. NeurIPS Workshop on Data Driven Education 2013.
Paper

PAC-Bayes Generalization Bounds for Randomized Structured Prediction
B. London, B. Huang, B. Taskar, L. Getoor. NeurIPS 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. NeurIPS 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. NeurIPS 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. NeurIPS 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. NeurIPS 2012 Workshop on Spectral Learning.
Paper

A Short Introduction to Probabilistic Soft Logic
A. Kimmig,  S. Bach, M. Broecheler, B. Huang, and L. Getoor. NeurIPS 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 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 (NeurIPS) 2011.
Paper, appendix, and 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. NeurIPS 2011 workshop. Oral presentation
Paper 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).
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, and clarification.

2010

Collaborative Filtering via Rating Concentration
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2010.
Paper, code archive, and 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 and poster

Approximating the Permanent with Belief Propagation
B. Huang and T. Jebara. Columbia University Technical Report. 2009
arXiv and code archive

2008

Maximum Likelihood Graph Structure Estimation with Degree Distributions
B. Huang and T. Jebara. Analyzing Graphs: Theory and Applications, NeurIPS 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.
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 and 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.

Virginia Tech

Causal Reasoning, Spring 2020

Advanced Machine Learning, Fall 2019

Machine Learning, Spring 2019

Introduction to Artificial Intelligence, Fall 2018

Optimization in Machine Learning, Spring 2018

Machine Learning, Fall 2017

Data Analytics II, Spring 2017

Introduction to Artificial Intelligence, Fall 2016

Probabilistic Graphical Models and Structured Prediction, Spring 2016

Machine Learning, Fall 2015

Introduction to Artificial Intelligence, Spring 2015

University of Maryland

Link Mining, Spring 2012 (Co-instructed with Lise Getoor)

Columbia University

Objected Oriented Programming and Design in Java, Spring 2010

Data Structures in Java, Fall 2009

Data Structures and Algorithms, Spring 2009

Introduction to Computer Science and Programming in C, Fall 2008

Please feel free to use these materials for educational purposes, but do not sell the content on these course websites for profit.

Music

When I have free time, I try my best to be a musician. Lately, this is mostly manifested by my playing guitar to entertain my pet budgie.

In graduate school, I created a few recordings experimenting with the electronic instruments built into Apple GarageBand for iPad, which can be streamed from my SoundCloud page.

In my early high school years, I arranged a number of electronic pieces. Later, in high school and college, I explored combining my electronic songs with real voice and instrument recordings (here is a small subset).

Athletics

During graduate school, I earned a brown belt studying with the Columbia University Tae Kwon Do Club.

VT Discovery Analytics Center

LINQS

Columbia Machine Learning

Center for Computational Learning Systems

Editing by Emily

Christopher Huang Photography

Machine learning simulator