Ruozzi, Nicholas

27 publications

ICCV 2025 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

NeurIPS 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 Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate

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

AISTATS 2023 A New Modeling Framework for Continuous, Sequential Domains Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi

AISTATS 2022 Conditionally Tractable Density Estimation Using Neural Networks Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi

AISTATS 2022 Relational Neural Markov Random Fields Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi

NeurIPS 2022 Boosting the Performance of Generic Deep Neural Network Frameworks with Log-Supermodular CRFs Hao Xiong, Yangxiao Lu, Nicholas Ruozzi

AISTATS 2021 Dynamic Cutset Networks Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate

IJCAI 2020 General Purpose MRF Learning with Neural Network Potentials Hao Xiong, Nicholas Ruozzi

IJCAI 2020 Lifted Hybrid Variational Inference Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi

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

ICLRW 2019 Correlated Variational Auto-Encoders Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi

IJCAI 2019 Lifted Message Passing for Hybrid Probabilistic Inference Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan

AAAI 2019 Marginal Inference in Continuous Markov Random Fields Using Mixtures Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi

UAI 2019 One-Shot Marginal MAP Inference in Markov Random Fields Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi

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

AISTATS 2017 A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case Nicholas Ruozzi

IJCAI 2017 Efficient Inference for Untied MLNs Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate

NeurIPS 2017 Sparse Approximate Conic Hulls Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi

AISTATS 2016 Bethe Learning of Graphical Models via MAP Decoding Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara

AAAI 2016 On Parameter Tying by Quantization Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate

NeurIPS 2015 Exactness of Approximate MAP Inference in Continuous MRFs Nicholas Ruozzi

NeurIPS 2014 Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi, Tony Jebara

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

JMLR 2013 Message-Passing Algorithms for Quadratic Minimization Nicholas Ruozzi, Sekhar Tatikonda

NeurIPS 2012 The Bethe Partition Function of Log-Supermodular Graphical Models Nicholas Ruozzi

UAI 2010 Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models Nicholas Ruozzi, Sekhar Tatikonda