Bert Huang

My research group investigates 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.

 

 

Alumni

 

2021

Constrained Label Learning for Weakly Supervised Learning
Chidubem Arachie, Bert Huang. International Conference in Uncertainty in Artificial Intelligence.
Paper

A General Framework for Adversarial Label Learning
Chidubem Arachie, Bert Huang. Journal of Machine Learning Research.
Paper

Personalized Regularization Learning for Fairer Matrix Factorization
Sirui Yao, Bert Huang. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
Paper

A Novel Approach to Social Media Guideline Design and Its Application to Automated Vehicle Events
Hananeh Alambeigi, Alec Smith, Ran Wei, Anthony McDonald, Chidubem Arachie, Bert Huang. Human Factors and Ergonomics Society Annual Meeting.
Paper

2020

Woodbury Transformations for Deep Generative Flows
You Lu, Bert Huang. Advances in Neural Information Processing Systems (NeurIPS).
arXiv Preprint (Stay tuned for a camera-ready update.)

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

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

Preconditions for Guardianship Interventions in Cyberbullying: Incident Interpretation, Collective and Automated Efficacy, and Relative Popularity of Bullies
Leanna Ireland, James Hawdon, Bert Huang, Anthony Peguero. Computers in Human Behavior.
Paper

Machine Learning Approach For The Objective Sonographic Assessment Of Patellar Tendons In Collegiate Basketball Athletes
Carrie Cheung, You Lu, Al Kozar, Mary Mitchell, Jacob Turnbull, Bert Huang, Vincent Wang. Biomedical Engineering Society Annual Meeting.

Woodbury Transformations for Deep Generative Flows
You Lu, Bert Huang. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.
Paper, Spotlight Talk.

Machine Learning Applications in Orthopaedic Imaging
Vincent Wang, Carrie Cheung, Albert Kozar, Bert Huang. Journal of the American Academy of Orthopaedic Surgeons.
Publisher Link

2019

Adversarial Label Learning
Chidubem Arachie, Bert Huang. AAAI Conference on Artificial Intelligence.
Paper, Code.

Block Belief Propagation for Parameter Learning in Markov Random Fields
You Lu, Zhiyuan Liu, Bert Huang. AAAI Conference on Artificial Intelligence.
arXiv

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

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

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

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

2018

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

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

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

Recurrent Collective Classification
Shuangfei Fan, Bert Huang. Knowledge and Information Systems.
Springer Online First Link

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

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

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

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

Adversarial Learning for Weak Supervision
Chidubem Arachie, Bert Huang. Black in Artificial Intelligence Workshop.

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

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

Integrating Machine Learning to Improve Optimal Estimation of Atmospheric Composition
Bert Huang, Chidubem 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
Yun Dong, Bert Huang, Chidubem Arachie, Elena Spinei, Natalya Kramarova, Krzystof Wargan. American Geophysical Union (AGU) Fall Meeting.

Machine Learning Approaches for Automated Detection of Cyberviolence
Elaheh Raisi, Leanna Ireland, Bert Huang, James Hawdon, Anthony Peguero. Southern Sociological Society Annual Meeting.

Establishing a Virtual Social Laboratory for Investigating Cyberviolence
Bert Huang, Anthony Peguero, James Hawdon. Southern Sociological Society Annual Meeting.

The Detection of Patellar Tendinopathy Using Machine Learning Analysis of Ultrasound Images
Ellen Hammet, Grady Iliff, S. Rezvani, Bert Huang, Albert Kozar, Vincent Wang. Orthopaedic Research Society Annual Meeting.

2017

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

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

Co-trained Ensemble Models for Weakly Supervised Cyberbullying Detection
Elaheh Raisi, Bert 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
Sirui Yao, Bert Huang. Advances in Neural Information Processing Systems (NeurIPS).
Paper, Video

Cyberbullying Detection with Weakly Supervised Machine Learning
Elaheh Raisi, Bert 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
Sirui Yao, Bert Huang. Workshop on Fairness, Accountabilty, and Transparency in Machine Learning (FATML), KDD 2017 Workshop.
arXiv

Online Edge Grafting for Efficient MRF Structure Learning
Walid Chaabene, Bert Huang.
arXiv

Recurrent Collective Classification
Shuangfei Fan, Bert 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
Bert Huang. Workshop on Computational Methods for CyberSafety. Invited talk.
Abstract

Training Iterative Collective Classifiers with Back-Propagation
Shuangfei Fan, Bert Huang. Workshop on Mining and Learning with Graphs.
Paper

Cyberbullying Identification Using Participant-Vocabulary Consistency
Elaheh Raisi, Bert Huang. #Data4Good: Machine Learning in Social Good Applications. ICML 2016 workshop.
Paper

Stability and Generalization in Structured Prediction
Ben London, Bert Huang, Lise Getoor. Journal of Machine Learning Research. Volume 17.
Preprint

Machine Learning for Detecting Detrimental Online Social Behavior
Bert Huang, Elaheh 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
Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, Naren Ramakrishnan. Artificial Intelligence Magazine.
Paper

2015

Paired-Dual Learning for Training Hinge-Loss MRFs with Latent Variables
Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor. International Conference on Machine Learning (ICML) 2015.
Paper, Talk

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

Paired-Dual Learning for Circumventing the Inference Bottleneck
Bert 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
Dhanya Sridhar, James Foulds, Marilyn Walker, Bert Huang, Lise 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
Stephen Bach, Bert Huang, Lise Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2015
Paper

Planned Protest Modeling in News and Social Media
Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, Naren 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
Shobeir Fakhraei, Bert Huang, Louiqa Raschid, Lise Getoor. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Paper

Discovering Evolving Political Vocabulary in Social Media
Aravindan Mahendiran, Wei Wang, Jaime Arredondo, Bert Huang, Lise Getoor, David Mares, Naren 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
Naren Ramakrishnan, Patric Butler, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Hua Ting, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2014.
Paper

Learning Latent Engagement Patterns of Students in Online Courses
Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor. AAAI Conference on Artificial Intelligence 2014.
Paper

PAC-Bayesian Collective Stability
Ben London, Bert Huang, and Lise Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2014.
Paper

Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies
Stephen Bach, Bert Huang, Lise Getoor. NeurIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) 2014.
Paper

On the Strong Convexity of Variational Inference
Ben London, Bert Huang, Lise Getoor. NeurIPS Workshop on Advances in Variational Inference 2014.
Paper

Collective Classification of Stance and Disagreement in Online Debate Forums
Dhanya Sridhar, James Foulds, Bert Huang, Marilyn Walker, Lise Getoor. Bay Area Machine Learning Symposium 2014.
Paper

Probabilistic Soft Logic for Social Good
Stephen Bach, Bert Huang, Lise Getoor. KDD Workshop on Data Science for Social Good 2014.
Paper

Understanding MOOC Discussion Forums using Seeded LDA
Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor. ACL Workshop on Innovative Use of NLP for Building Educational Applications, 2014.
Paper

Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs
Arti Ramesh, Dan Goldwasser, Bert Huang, H. Daumé III, Lise 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.
Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor. IEEE International Conference on Big Data, 2013.
Paper

Hinge-Loss Markov Random Fields: Convex Inference for Structured Prediction
Stephen Bach, Bert Huang, Ben London, Lise Getoor. Conference on Uncertainty in Artificial Intelligence (UAI) 2013.
Paper

Collective Stability in Structured Prediction: Generalization from One Example
Ben London, Bert Huang, Ben Taskar, Lise Getoor. International Conference on Machine Learning (ICML) 2013. Oral presentation.
Paper

A Flexible Framework for Probabilistic Models of Social Trust
Bert Huang, Angelika Kimmig, Lise Getoor, Jennifer Golbeck. International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP) 2013.
Paper

Large-Margin Structured Learning for Link Ranking
Stephen Bach, Bert Huang, Lise Getoor. NeurIPS Workshop on Frontiers of Network Analysis 2013. Best Student Paper Award.
Paper

Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor. NeurIPS Workshop on Data Driven Education 2013.
Paper

PAC-Bayes Generalization Bounds for Randomized Structured Prediction
Ben London, Bert Huang, Ben Taskar, Lise Getoor. NeurIPS Workshop on Perturbation, Optimization, and Statistics 2013.
Paper

Collective Inference and Multi-Relational Learning for Drug-Target Interaction Prediction
Shobeir Fakhraei, Bert Huang, Lise Getoor. NeurIPS Workshop on Machine Learning in Computational Biology 2013

Fairness in Assignment Markets with Dual Decomposition
Bert Huang. ICML Workshop on Peer Review and Publishing Models 2013.
Paper

Empirical Analysis of Collective Stability
Bert Huang, Ben London, Ben Taskar, Lise Getoor. ICML Workshop on Structured Learning 2013
Paper

Collective Activity Detection using Hinge-Loss Markov Random Fields
Ben London, Sameh Khamis, Stephen Bach, Bert Huang, Lise Getoor, Larry Davis. CVPR Workshop on Structured Prediction 2013
Paper

Learning Latent Groups with Hinge-Loss Markov Random Fields
Stephen Bach, Bert Huang, Lise Getoor. ICML Workshop on Interactions between Inference and Learning (Inferning) 2013
Paper

Graph-Based Generalization Bounds for Learning Binary Relations
Ben London, Bert Huang, and Lise Getoor.
arXiv

Multi-Relational Learning Using Weighted Tensor Decomposition with Modular Loss
Ben London, Thodoris Rekatsinas, Bert Huang, and Lise Getoor.
arXiv

2012

Social Group Modeling with Probabilistic Soft Logic
Bert Huang, Stephen Bach, Eric Norris, Jay Pujara, and Lise Getoor. NeurIPS 2012 Workshop on Social Network and Social Media Analysis: Methods, Models, and Applications.
Paper

Improved Generalization Bounds for Large-Scale Structured Prediction
Ben London, Bert Huang, and Lise Getoor. NeurIPS 2012 Workshop on Algorithmic and Statistical Approaches for Large Social Networks.
Paper

Multi-Relational Weighted Tensor Decomposition
Ben London, T. Rekatsinas, Bert Huang, and Lise Getoor. NeurIPS 2012 Workshop on Spectral Learning.
Paper

A Short Introduction to Probabilistic Soft Logic
Angelika Kimmig,  Stephen Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. NeurIPS 2012 Workshop on Probabilistic Programming: Foundations and Applications. Oral presentation
Paper

Probabilistic Soft Logic for Trust Analysis in Social Networks
Bert Huang, Angelika Kimmig, Lise 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
Galileo Namata, Ben London, Lise Getoor, and Bert 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
Michele Merler, Bert Huang, Lexing Xie, Gang Hua, and Apostol Natsev. IEEE Transactions on Multimedia, Vol. 14, No. 1, February 2012.
Preprint

Machine Learning for the New York City Power Grid
Cynthia Rudin, David Waltz, Roger Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome, Delfina Isaac, Arthur Kressner, Rebecca Passonneau, Axinia Radeva, and Leon Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence. February 2012.
Preprint

2011

Learning a Distance Metric from a Network
Blake Shaw, Bert Huang, and Tony Jebara. Neural Information Processing Systems (NeurIPS) 2011.
Paper, appendix, and poster

Learning a Degree-Augmented Distance Metric from a Network
Bert Huang, Blake Shaw, and Tony Jebara. Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity. NeurIPS 2011 workshop. Oral presentation
Paper and slides

Learning with Degree-Based Subgraph Estimation
Bert Huang
Ph.D. Dissertation. Columbia University
Thesis

Network Prediction with Degree Distributional Metric Learning
Bert Huang, Blake Shaw, and Tony Jebara. Interdisciplinary Workshop on Information and Decision in Social Networks (WIDS 2011).
abstract

Fast b-Matching via Sufficient Selection Belief Propagation
Bert Huang and Tony Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2011.
Paper, code, poster, and clarification.

2010

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

2009

Learning with Subgraph Estimation and Degree Priors
Bert Huang and Tony Jebara. New York Academy of Sciences Machine Learning Symposium, November 2009.
Paper

Exact Graph Structure Estimation with Degree Priors
Bert Huang and Tony Jebara. International Conference on Machine Learning and Applications (ICMLA) 2009.
Paper

Alive on Back-Feed Culprit Identification via Machine Learning
Bert Huang, Ansaf Salleb-Aouissi, and Philip Gross. International Conference on Machine Learning and Applications (ICMLA) 2009. Special Session on Machine Learning in Energy Applications.
Paper

Discovering Characterization Rules from Rankings
Ansaf Salleb-Aouissi, Bert Huang, and David Waltz. International Conference on Machine Learning and Applications (ICMLA) 2009.
Paper

Maximum Entropy Density Estimation with Incomplete Presence-Only Data
Bert Huang and Ansaf Salleb-Aouissi. International Conference on Artificial Intelligence and Statistics (AISTATS) 2009.
Paper and poster

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

2008

Maximum Likelihood Graph Structure Estimation with Degree Distributions
Bert Huang and Tony 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)
Ansaf Salleb-Aouissi, Bert Huang, and David 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
Bert Huang and Tony Jebara. New York Academy of Sciences Machine Learning Symposium 2007.
abstract

Maximum Entropy Density Estimation with Incomplete Data
Bert Huang and Ansaf Salleb-Aouissi. New York Academy of Sciences Machine Learning Symposium 2007.
Poster and abstract.

Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Bert Huang and Tony 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
Bert Huang and Tony 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.

Tufts University

Communicating with Data, Spring 2022

Artificial Intelligence: Algorithms, Ethics, and Policy, Fall 2021 (Co-instructed with Josephine Wolff)

Machine Learning with Limited Annotation, Spring 2021

From Basic Modeling to Deep Learning in PyTorch, Fall 2020

Challenges of Fairness in Machine Learning, Fall 2020

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

Editing by Emily

Christopher Huang Photography

Machine learning simulator