Alec G Moore, Tiffany D. Do, Nicholas Ruozzi, and Ryan P. McMahan
Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly
IEEE and ACM International Symposium on Augmented Reality (ISMAR), October 2023.

Chiradeep Roy, Mahsan Nourani, Shivvrat Arya, Mahesh Shanbhag, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, and Vibhav Gogate
Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models
ACM Trans. Interact. Intell. Syst., October 2023.

Yangxiao Lu, Ninad A Khargonkar, Zesheng Xu, Charles Averill, Kamalesh Palanisamy, Kaiyu Hang, Yunhui Guo, Nicholas Ruozzi, Yu Xiang
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
Robotics Science and Systems (RSS), July 2023.

Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi
A New Modeling Framework for Continuous, Sequential Domains
26th International Conference on Artificial Intelligence and Statistics (AISTATS), April 2023.

Hao Xiong, Yangxiao Lu, Nicholas Ruozzi
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs
Neural Information Processing Systems (NeurIPS), December, 2022.

Peter Csikvari, Nicholas Ruozzi, and Shahab Shams
Markov Random Fields, Homomorphism Counting, and Sidorenko's Conjecture
IEEE Transactions on Information Theory, vol.68, no.9, pp.6052-6062, September, 2022.

Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, and Nicholas Ruozzi
Conditionally Tractable Density Estimation using Neural Networks
International Conference on Artificial Intelligence and Statistics (AISTATS), March, 2022.

Yuqiao Chen, Sriraam Natarajan, and Nicholas Ruozzi
Relational Neural Markov Random Fields
International Conference on Artificial Intelligence and Statistics (AISTATS), March, 2022.

Chiradeep Roy, Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate
Explainable activity recognition in videos: Lessons learned
Applied AI Letters. 2021; 2( 4):e59.

Alec G. Moore, Ryan P. McMahan, Hailiang Dong, and Nicholas Ruozzi
Personal Identifiability of User Tracking Data During VR Training
IEEE and ACM International Symposium on Augmented Reality (ISMAR), October, 2021.

Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Vibhav Gogate, Nicholas Ruozzi
Dynamic Cutset Networks
4th Workshop on Tractable Probabilistic Modeling (TPM 2021), July 2021

Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi
Conditionally Tractable Density Estimation using Neural Networks
4th Workshop on Tractable Probabilistic Modeling (TPM 2021), July 2021

Alec G. Moore, Ryan P. McMahan, and Nicholas Ruozzi
Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
Big Data and Cognitive Computing, 5, no. 3: 29, 2021.

Chriadeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, and Vibhav Gogate
Dynamic Cutset Networks
24th International Conference on Artificial Intelligence and Statistics (AISTATS), April, 2021.

Alec G. Moore, Ryan P. McMahan, Hailiang Dong, and Nicholas Ruozzi
Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application
IEEE and ACM International Symposium on Augmented Reality (ISMAR), November, 2020.

Yuqiao Chen*, Yibo Yang*, Sriraam Natarajan, and Nicholas Ruozzi
Hybrid Lifted Variational Inference
International Joint Conference on Artificial Intelligence (IJCAI), July 2020.

Hao Xiong and Nicholas Ruozzi
General Purpose MRF Learning with Neural Network Potentials
International Joint Conference on Artificial Intelligence (IJCAI), July 2020.

Yuqiao Chen, Nicholas Ruozzi, and Sriraam Natarajan
Lifted Message Passing for Hybrid Probabilistic Inference
International Joint Conference on Artificial Intelligence (IJCAI), August 2019.

Yuanzhen Guo*, Hao Xiong*, Yibo Yang*, and Nicholas Ruozzi
One-Shot Marginal MAP Inference in Markov Random Fields
Uncertainty in Artificial Intelligence (UAI), July 2019.

Shahab Shams, Nicholas Ruozzi, and Peter Csikvari
Counting Homomorphisms in Bipartite Graphs
IEEE International Symposium on Information Theory (ISIT), July 2019.

Dawen Liang, Da Tang, Tony Jebara, and Nicholas Ruozzi
Correlated Variational Auto-Encoders
International Conference on Machine Learning (ICML), June 2019.

Chiradeep Roy, Mahsan Nourani, Mahesh Shanbhag, Samia Kabir, Tahrima Rahman, Eric Ragan, Nicholas Ruozzi, and Vibhav Gogate
Explainable Activity Recognition in Videos using Dynamic Cutset Networks
ICML Workshop on Tractable Probabilistic Models (TPM), June 2019.

Dawen Liang, Da Tang, Tony Jebara, and Nicholas Ruozzi
Correlated Variational Auto-Encoders
Workshop on Deep Generative Models for Highly Structured Data. International Conference on Learning Representations (ICLR), May 2019.

Chiradeep Roy, Mahesh Shanbhag, Tahrima Rahman, Nicholas Ruozzi, Vibhav Gogate, Mahsan Nourani, Eric D. Ragan, and Samia Kabir
Explainable Activity Recognition in Videos
Workshop on Explainable Smart Systems, ACM Intelligent User Interfaces (IUI), March 2019.

Yibo Yang, Nicholas Ruozzi, and Vibhav Gogate
Efficient Neural Network Pruning and Quantization by Hard Clustering
Workshop on Network Interpretability, 33rd Conference on Artificial Intelligence (AAAI), January 2019.

Yuanzhen Guo, Hao Xiong, and Nicholas Ruozzi
Marginal Inference in Continuous Markov Random Fields using Mixtures
33rd Conference on Artificial Intelligence (AAAI), January 2019.

Nicholas Ruozzi
Approximate methods for calculating marginals and likelihoods
In M. Maathuis, M. Drton, S. Lauritzen and M. Wainwright (Eds), Handbook of Graphical Models, Chapman & Hall / CRC, 2018.

Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, and Vibhav Gogate
Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks
32nd Conference on Artificial Intelligence (AAAI), February 2018.

Gregory Van Buskirk, Benjamin Raichel, and Nicholas Ruozzi
Sparse Approximate Conic Hulls
Advances in Neural Information Processing Systems (NIPS), December 2017.

Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, and Vibhav Gogate
Efficient Inference for Untied MLNs
26th International Joint Conference on Artificial Intelligence (IJCAI), August 2017.

Nicholas Ruozzi
A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case
Twentieth International Conference on Artificial Intelligence and Statistics (AISTATS), April 2017.

Kui Tang, Nicholas Ruozzi, David Belanger, and Tony Jebara
Bethe learning of graphical models via MAP decoding
Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), May 2016.

Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, and Vibhav Gogate
On parameter tying by quantization
Thirtieth Conference on Artificial Intelligence (AAAI), February 2016.

Nicholas Ruozzi
Approximate MAP inference in continuous MRFs
Advances in Neural Information Processing Systems (NIPS), December 2015.

Nicholas Ruozzi and Tony Jebara
Making pairwise binary graphical models attractive
Advances in Neural Information Processing Systems (NIPS), December 2014.
Spotlight Presentation

Nicholas Ruozzi and Sekhar Tatikonda
Message-passing algorithms: Reparameterizations and splittings
IEEE Transactions on Information Theory, vol.59, no.9, pp.5860-5881, September 2013.

Nicholas Ruozzi and Sekhar Tatikonda
Message-passing algorithms for quadratic minimization
Journal of Machine Learning Research, 14:2287-2314, August 2013.

Nicholas Ruozzi
Beyond log-supermodularity: lower bounds and the Bethe partition function
Uncertainty in Artificial Intelligence (UAI), July 2013.

Nicholas Ruozzi
The Bethe partition function of log-supermodular graphical models
Advances in Neural Information Processing Systems (NIPS), December 2012.

Nicholas Ruozzi
Convergent message-passing in the presence of erasures
Proceedings of the 50th Annual Allerton Conference on Communication, Control, and Computing, October 2012.

Nicholas Ruozzi
Message Passing Algorithms for Optimization

Ph.D. Thesis, Yale University, August 2011.

Nicholas Ruozzi and Sekhar Tatikonda
Convergent and correct message-passing schemes for optimization problems over graphical models

Uncertainty in Artficial Intelligence (UAI), 2010.

Nicholas Ruozzi and Sekhar Tatikonda
Unconstrained minimization of quadratic functions via min-sum

Proceedings of the Conference on Information Sciences and Systems (CISS), Princeton, NJ/USA, March 2010.

Nicholas Ruozzi, Justin Thaler, and Sekhar Tatikonda
Graph covers and quadratic minimization

Proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computing, September 2009.

Dexter Kozen and Nicholas Ruozzi
Applications of metric coinduction
Logical Methods in Computer Science, 5(3:10), 2009.

Nicholas Ruozzi and Sekhar Tatikonda
s-t paths using the min-sum algorithm

Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing, September 2008.

Dexter Kozen and Nicholas Ruozzi
Applications of metric coinduction

In T. Mossakowski et al., editor, Proc. 2nd Conf. Algebra and Coalgebra in Computer Science (CALCO), v. 4624 of LNCS, pages 327-341. Springer, August 2007.

* Denotes equal contributions