| 2025 | ISIT | Structure Learning in Gaussian Graphical Models from Glauber Dynamics. | Vignesh Tirukkonda, Anirudh Rayas, Gautam Dasarathy |
| 2024 | ACSSC | The Sample Complexity of Differential Analysis for Networks that Obey Conservation Laws. | Jiajun Cheng, Anirudh Rayas, Rajasekhar Anguluri, Gautam Dasarathy |
| 2024 | ACSSC | Computationally Efficient Active Learning of Gaussian Graphical Models. | Abrar Zahin, Gautam Dasarathy |
| 2024 | ICASSP | Non-Stationary Bandits with Periodic Behavior: Harnessing Ramanujan Periodicity Transforms to Conquer Time-Varying Challenges. | Parth Thaker, Vineet Sunil Gattani, Vignesh Tirukkonda, Pouria Saidi, Gautam Dasarathy |
| 2024 | ICASSP | Rapid Change Localization in Dynamic Graphical Models. | Abrar Zahin, Weizhi Li, Gautam Dasarathy |
| 2024 | WSC | Advanced Tutorial: Label-Efficient Two-Sample Tests. | Weizhi Li, Visar Berisha, Gautam Dasarathy |
| 2023 | ICASSP | Differential Analysis for Networks Obeying Conservation Laws. | Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng, Gautam Dasarathy |
| 2022 | UAI | A label efficient two-sample test. | Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha |
| 2021 | AISTATS | Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model. | Nafiseh Ghoroghchian, Gautam Dasarathy, Stark C. Draper |
| 2020 | AISTATS | Thresholding Graph Bandits with GrAPL. | Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk |
| 2020 | AISTATS | Regularization via Structural Label Smoothing. | Weizhi Li, Gautam Dasarathy, Visar Berisha |
| 2020 | ECCV | Differentiable Programming for Hyperspectral Unmixing Using a Physics-Based Dispersion Model. | John Janiczek, Parth Thaker, Gautam Dasarathy, Christopher S. Edwards, Philip Christensen, Suren Jayasuriya |
| 2020 | ISIT | On the α-loss Landscape in the Logistic Model. | Tyler Sypherd, Mario Daz, Lalitha Sankar, Gautam Dasarathy |
| 2020 | ISIT | On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems. | Parth K. Thaker, Gautam Dasarathy, Angelia Nedic |
| 2019 | ICLR | A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery. | Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk |
| 2019 | ISIT | Gaussian Graphical Model Selection from Size Constrained Measurements. | Gautam Dasarathy |
| 2018 | ACSSC | Quantile Search with Time-Varying Search Parameter. | John Lipor, Gautam Dasarathy |
| 2018 | ICML | MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. | Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk |
| 2017 | ACSSC | Sketched covariance testing: A compression-statistics tradeoff. | Gautam Dasarathy, Parikshit Shah, Richard G. Baraniuk |
| 2017 | ICML | Multi-fidelity Bayesian Optimisation with Continuous Approximations. | Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabs Pczos |
| 2017 | ISIT | Sketched covariance testing: A compression-statistics tradeoff. | Gautam Dasarathy, Parikshit Shah, Richard G. Baraniuk |
| 2016 | AISTATS | Active Learning Algorithms for Graphical Model Selection. | Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park |
| 2015 | COLT | S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. | Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu |
| 2014 | ISIT | New sample complexity bounds for phylogenetic inference from multiple loci. | Gautam Dasarathy, Robert D. Nowak, Sbastien Roch |
| 2011 | ISIT | On reliability of content identification from databases based on noisy queries. | Gautam Dasarathy, Stark C. Draper |
| 2010 | INFOCOM | Toward the Practical Use of Network Tomography for Internet Topology Discovery. | Brian Eriksson, Gautam Dasarathy, Paul Barford, Robert D. Nowak |