s-t paths using the min-sum algorithm. - Nicholas Ruozzi; Sekhar Tatikonda (Allerton, 2008)

Graph covers and quadratic minimization. - Nicholas Ruozzi; Justin Thaler; Sekhar Tatikonda (Allerton, 2009)

Applications of Metric Coinduction - Dexter Kozen; Nicholas Ruozzi (Log. Methods Comput. Sci., 2009)

Unconstrained minimization of quadratic functions via min-sum. - Nicholas Ruozzi; Sekhar Tatikonda (CISS, 2010)

Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models. - Nicholas Ruozzi; Sekhar Tatikonda (UAI, 2010)

Convergent message-passing algorithms in the presence of erasures. - Nicholas Ruozzi (Allerton Conference, 2012)

The Bethe Partition Function of Log-supermodular Graphical Models. - Nicholas Ruozzi (NIPS, 2012)

Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function. - Nicholas Ruozzi (UAI, 2013)

Message-passing algorithms for quadratic minimization. - Nicholas Ruozzi; Sekhar Tatikonda (J. Mach. Learn. Res., 2013)

Message-Passing Algorithms: Reparameterizations and Splittings. - Nicholas Ruozzi; Sekhar Tatikonda (IEEE Trans. Inf. Theory, 2013)

Making Pairwise Binary Graphical Models Attractive. - Nicholas Ruozzi; Tony Jebara (NIPS, 2014)

Exactness of Approximate MAP Inference in Continuous MRFs. - Nicholas Ruozzi (NIPS, 2015)

P2V: Effective Website Fingerprinting Using Vector Space Representations. - Khaled Al-Naami; Gbadebo Ayoade; Asim Siddiqui; Nicholas Ruozzi; Latifur Khan; Bhavani Thuraisingham (SSCI, 2015)

Bethe Learning of Conditional Random Fields via MAP Decoding. - Kui Tang; Nicholas Ruozzi; David Belanger 002; Tony Jebara (CoRR, 2015)

On Parameter Tying by Quantization. - Li Chou; Somdeb Sarkhel; Nicholas Ruozzi; Vibhav Gogate (AAAI, 2016)

Bethe Learning of Graphical Models via MAP Decoding. - Kui Tang; Nicholas Ruozzi; David Belanger 002; Tony Jebara (AISTATS, 2016)

A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case. - Nicholas Ruozzi (AISTATS, 2017)

Efficient Inference for Untied MLNs. - Somdeb Sarkhel; Deepak Venugopal; Nicholas Ruozzi; Vibhav Gogate (IJCAI, 2017)

Sparse Approximate Conic Hulls. - Greg Van Buskirk; Benjamin Raichel; Nicholas Ruozzi (NIPS, 2017)

Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. - Li Chou; Pracheta Sahoo; Somdeb Sarkhel; Nicholas Ruozzi; Vibhav Gogate (AAAI, 2018)

Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. - Yibo Yang; Nicholas Ruozzi; Vibhav Gogate (CoRR, 2018)

Marginal Inference in Continuous Markov Random Fields Using Mixtures. - Yuanzhen Guo; Hao Xiong; Nicholas Ruozzi (AAAI, 2019)

Correlated Variational Auto-Encoders. - Da Tang; Dawen Liang; Tony Jebara; Nicholas Ruozzi (ICML, 2019)

Lifted Message Passing for Hybrid Probabilistic Inference. - Yuqiao Chen; Nicholas Ruozzi; Sriraam Natarajan (IJCAI, 2019)

Counting Homomorphisms in Bipartite Graphs. - Shahab Shams; Nicholas Ruozzi; Péter Csikvári (ISIT, 2019)

Explainable Activity Recognition in Videos. - Chiradeep Roy; Mahesh Shanbhag; Mahsan Nourani; Tahrima Rahman; Samia Kabir; Vibhav Gogate; Nicholas Ruozzi; Eric D. Ragan (IUI Workshops, 2019)

One-Shot Inference in Markov Random Fields. - Hao Xiong; Yuanzhen Guo; Yibo Yang; Nicholas Ruozzi (UAI, 2019)

Learning Correlated Latent Representations with Adaptive Priors. - Da Tang; Dawen Liang; Nicholas Ruozzi; Tony Jebara (CoRR, 2019)

Lifted Hybrid Variational Inference. - Yuqiao Chen; Yibo Yang; Sriraam Natarajan; Nicholas Ruozzi (IJCAI, 2020)

General Purpose MRF Learning with Neural Network Potentials. - Hao Xiong; Nicholas Ruozzi (IJCAI, 2020)

Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application. - Alec G. Moore; Ryan P. McMahan; Hailiang Dong; Nicholas Ruozzi (ISMAR, 2020)

Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. - Mahsan Nourani; Chiradeep Roy; Tahrima Rahman; Eric D. Ragan; Nicholas Ruozzi; Vibhav Gogate (CoRR, 2020)

Dynamic Cutset Networks. - Chiradeep Roy; Tahrima Rahman; Hailiang Dong; Nicholas Ruozzi; Vibhav Gogate (AISTATS, 2021)

Personal Identifiability and Obfuscation of User Tracking Data From VR Training Sessions. - Alec G. Moore; Ryan P. McMahan; Hailiang Dong; Nicholas Ruozzi (ISMAR, 2021)

Personal Identifiability of User Tracking Data During VR Training. - Alec G. Moore; Ryan P. McMahan; Hailiang Dong; Nicholas Ruozzi (VR Workshops, 2021)

Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario. - Alec G. Moore; Ryan P. McMahan; Nicholas Ruozzi (Big Data Cogn. Comput., 2021)

Relational Neural Markov Random Fields. - Yuqiao Chen; Sriraam Natarajan; Nicholas Ruozzi (AISTATS, 2022)

Conditionally Tractable Density Estimation using Neural Networks. - Hailiang Dong; Chiradeep Roy; Tahrima Rahman; Vibhav Gogate; Nicholas Ruozzi (AISTATS, 2022)

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

Markov Random Fields, Homomorphism Counting, and Sidorenko's Conjecture. - Péter Csikvári; Nicholas Ruozzi; Shahab Shams (IEEE Trans. Inf. Theory, 2022)

A New Modeling Framework for Continuous, Sequential Domains. - Hailiang Dong; James Amato; Vibhav Gogate; Nicholas Ruozzi (AISTATS, 2023)

Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly. - Alec G. Moore; Tiffany D. Do; Nicholas Ruozzi; Ryan P. McMahan (ISMAR, 2023)

Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction. - Yangxiao Lu; Ninad A. Khargonkar; Zesheng Xu; Charles Averill; Kamalesh Palanisamy; Kaiyu Hang; Yunhui Guo; Nicholas Ruozzi; Yu Xiang 001 (Robotics: Science and Systems, 2023)

CaptainCook4D: A dataset for understanding errors in procedural activities. - Rohith Peddi; Shivvrat Arya; Bharath Challa; Likhitha Pallapothula; Akshay Vyas; Jikai Wang; Qifan Zhang; Vasundhara Komaragiri; Eric D. Ragan; Nicholas Ruozzi; Yu Xiang 001; Vibhav Gogate (CoRR, 2023)

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

Mean Shift Mask Transformer for Unseen Object Instance Segmentation. - Yangxiao Lu; Yuqiao Chen; Nicholas Ruozzi; Yu Xiang 001 (ICRA, 2024)

CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities. - Rohith Peddi; Shivvrat Arya; Bharath Challa; Likhitha Pallapothula; Akshay Vyas; Bhavya Gouripeddi; Qifan Zhang; Jikai Wang; Vasundhara Komaragiri; Eric D. Ragan; Nicholas Ruozzi; Yu Xiang 001; et al. (NeurIPS, 2024)

Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains. - Hailiang Dong; James Amato; Vibhav Gogate; Nicholas Ruozzi (UAI, 2024)

Predictive Task Guidance with Artificial Intelligence in Augmented Reality. - Benjamin Rheault; Shivvrat Arya; Akshay Vyas; Jikai Wang; Rohith Peddi; Brett Benda; Vibhav Gogate; Nicholas Ruozzi; Yu Xiang 001; Eric D. Ragan (VR Workshops, 2024)

Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation. - Yangxiao Lu; Jishnu Jaykumar P; Yunhui Guo; Nicholas Ruozzi; Yu Xiang 001 (CoRR, 2024)

Multimodal Reference Visual Grounding. - Yangxiao Lu; Ruosen Li; Liqiang Jing; Jikai Wang; Xinya Du; Yunhui Guo; Nicholas Ruozzi; Yu Xiang 001 (CoRR, 2025)