| 2026 | AAAI | A Novel Approach to Evaluating Evaluation Metrics for Multi-Output Structured Prediction. | Akshay Vyas, Angelo Pimienta, Nicholas Ruozzi |
| 2025 | ICCV | CMB-ML: A Cosmic Microwave Background Dataset for the Oldest Possible Computer Vision Task. | James Amato, Yunan Xie, Leonel Medina-Varela, Ammar Aljerwi, Adam McCutcheon, T. Seth Rippentrop, Kristian Gonzalez, Jacques Delabrouille, Mustapha Ishak, Nicholas Ruozzi |
| 2025 | IROS | Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation. | Yangxiao Lu, Jishnu Jaykumar P, Yunhui Guo, Nicholas Ruozzi, Yu Xiang |
| 2024 | ICRA | Mean Shift Mask Transformer for Unseen Object Instance Segmentation. | Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, Yu Xiang |
| 2024 | VR | 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, Eric D. Ragan |
| 2024 | UAI | Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains. | Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi |
| 2023 | AISTATS | A New Modeling Framework for Continuous, Sequential Domains. | Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi |
| 2023 | ISMAR | 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 |
| 2022 | AISTATS | Relational Neural Markov Random Fields. | Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi |
| 2022 | AISTATS | Conditionally Tractable Density Estimation using Neural Networks. | Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi |
| 2021 | AISTATS | Dynamic Cutset Networks. | Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate |
| 2021 | ISMAR | Personal Identifiability and Obfuscation of User Tracking Data From VR Training Sessions. | Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi |
| 2021 | VR | Personal Identifiability of User Tracking Data During VR Training. | Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi |
| 2020 | IJCAI | Lifted Hybrid Variational Inference. | Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi |
| 2020 | IJCAI | General Purpose MRF Learning with Neural Network Potentials. | Hao Xiong, Nicholas Ruozzi |
| 2020 | ISMAR | 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 |
| 2019 | AAAI | Marginal Inference in Continuous Markov Random Fields Using Mixtures. | Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi |
| 2019 | ICLR | Correlated Variational Auto-Encoders. | Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi |
| 2019 | ICML | Correlated Variational Auto-Encoders. | Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi |
| 2019 | IJCAI | Lifted Message Passing for Hybrid Probabilistic Inference. | Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan |
| 2019 | ISIT | Counting Homomorphisms in Bipartite Graphs. | Shahab Shams, Nicholas Ruozzi, Pter Csikvri |
| 2019 | IUI | Explainable Activity Recognition in Videos. | Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi, Eric D. Ragan |
| 2019 | UAI | One-Shot Inference in Markov Random Fields. | Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi |
| 2018 | AAAI | Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. | Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate |
| 2017 | AISTATS | A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case. | Nicholas Ruozzi |
| 2017 | IJCAI | Efficient Inference for Untied MLNs. | Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate |
| 2016 | AAAI | On Parameter Tying by Quantization. | Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate |
| 2016 | AISTATS | Bethe Learning of Graphical Models via MAP Decoding. | Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara |
| 2013 | UAI | Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function. | Nicholas Ruozzi |
| 2010 | CISS | Unconstrained minimization of quadratic functions via min-sum. | Nicholas Ruozzi, Sekhar Tatikonda |
| 2010 | UAI | Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models. | Nicholas Ruozzi, Sekhar Tatikonda |
| 2007 | CALCO | Applications of Metric Coinduction. | Dexter Kozen, Nicholas Ruozzi |