Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees. - Tahrima Rahman; Prasanna V. Kothalkar; Vibhav Gogate (ECML/PKDD (2), 2014)

Learning Ensembles of Cutset Networks. - Tahrima Rahman; Vibhav Gogate (AAAI, 2016)

Merging Strategies for Sum-Product Networks: From Trees to Graphs. - Tahrima Rahman; Vibhav Gogate (UAI, 2016)

Algorithms for the Nearest Assignment Problem. - Sara Rouhani; Tahrima Rahman; Vibhav Gogate (IJCAI, 2018)

Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. - Tahrima Rahman; Shasha Jin; Vibhav Gogate (ICML, 2019)

Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. - Tahrima Rahman; Shasha Jin; Vibhav Gogate (IJCAI, 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)

Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. - Mahsan Nourani; Donald R. Honeycutt; Jeremy E. Block; Chiradeep Roy; Tahrima Rahman; Eric D. Ragan; Vibhav Gogate (CHI Extended Abstracts, 2020)

A Novel Approach for Constrained Optimization in Graphical Models. - Sara Rouhani; Tahrima Rahman; Vibhav Gogate (NeurIPS, 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)

Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. - Mahsan Nourani; Chiradeep Roy; Jeremy E. Block; Donald R. Honeycutt; Tahrima Rahman; Eric D. Ragan; Vibhav Gogate (IUI, 2021)

Novel Upper Bounds for the Constrained Most Probable Explanation Task. - Tahrima Rahman; Sara Rouhani; Vibhav Gogate (NeurIPS, 2021)

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

Learning Tractable Probabilistic Models from Inconsistent Local Estimates. - Shasha Jin; Vasundhara Komaragiri; Tahrima Rahman; Vibhav Gogate (NeurIPS, 2022)

Robust learning of tractable probabilistic models. - Rohith Peddi; Tahrima Rahman; Vibhav Gogate (UAI, 2022)

On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications. - Mahsan Nourani; Chiradeep Roy; Jeremy E. Block; Donald R. Honeycutt; Tahrima Rahman; Eric D. Ragan; Vibhav Gogate (ACM Trans. Interact. Intell. Syst., 2022)

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)

Neural Network Approximators for Marginal MAP in Probabilistic Circuits. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (AAAI, 2024)

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (AISTATS, 2024)

A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (NeurIPS, 2024)

SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (AISTATS, 2025)