Toward the Practical Use of Network Tomography for Internet Topology Discovery. - Brian Eriksson; Gautam Dasarathy; Paul Barford; Robert D. Nowak (INFOCOM, 2010)

On reliability of content identification from databases based on noisy queries. - Gautam Dasarathy; Stark C. Draper (ISIT, 2011)

Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities. - Brian Eriksson; Gautam Dasarathy; Aarti Singh; Robert D. Nowak (AISTATS, 2011)

Covariance sketching. - Gautam Dasarathy; Parikshit Shah; Badri Narayan Bhaskar; Robert D. Nowak (Allerton Conference, 2012)

Efficient Network Tomography for Internet Topology Discovery. - Brian Eriksson; Gautam Dasarathy; Paul Barford; Robert D. Nowak (IEEE/ACM Trans. Netw., 2012)

Sketching Sparse Matrices - Gautam Dasarathy; Parikshit Shah; Badri Narayan Bhaskar; Robert D. Nowak (CoRR, 2013)

New sample complexity bounds for phylogenetic inference from multiple loci. - Gautam Dasarathy; Robert D. Nowak; Sébastien Roch (ISIT, 2014)

S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. - Gautam Dasarathy; Robert D. Nowak; Xiaojin Zhu 001 (COLT, 2015)

Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method. - Gautam Dasarathy; Robert D. Nowak; Sébastien Roch (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

Sketching Sparse Matrices, Covariances, and Graphs via Tensor Products. - Gautam Dasarathy; Parikshit Shah; Badri Narayan Bhaskar; Robert D. Nowak (IEEE Trans. Inf. Theory, 2015)

Active Learning Algorithms for Graphical Model Selection. - Gautam Dasarathy; Aarti Singh; Maria-Florina Balcan; Jong Hyuk Park 004 (AISTATS, 2016)

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (NIPS, 2016)

The Multi-fidelity Multi-armed Bandit. - Kirthevasan Kandasamy; Gautam Dasarathy; Barnabás Póczos; Jeff G. Schneider (NIPS, 2016)

DeepCodec: Adaptive sensing and recovery via deep convolutional neural networks. - Ali Mousavi 003; Gautam Dasarathy; Richard G. Baraniuk (Allerton, 2017)

Multi-fidelity Bayesian Optimisation with Continuous Approximations. - Kirthevasan Kandasamy; Gautam Dasarathy; Jeff G. Schneider; Barnabás Póczos (ICML, 2017)

Sketched covariance testing: A compression-statistics tradeoff. - Gautam Dasarathy; Parikshit Shah; Richard G. Baraniuk (ISIT, 2017)

Coalescent-based species tree estimation: a stochastic Farris transform. - Gautam Dasarathy; Elchanan Mossel; Robert D. Nowak; Sebastien Roch (CoRR, 2017)

Quantile Search with Time-Varying Search Parameter. - John Lipor; Gautam Dasarathy (ACSSC, 2018)

Conveying language through haptics: a multi-sensory approach. - Nathan Dunkelberger; Jennifer L. Sullivan; Joshua Bradley; Nickolas P. Walling; Indu Manickam; Gautam Dasarathy; Ali Israr; Frances W. Y. Lau; Keith Klumb; Brian Knott; Freddy Abnousi; Richard G. Baraniuk; et al. (UbiComp, 2018)

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. - Amirali Aghazadeh; Ryan Spring; Daniel LeJeune; Gautam Dasarathy; Anshumali Shrivastava; Richard G. Baraniuk (ICML, 2018)

IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election. - Indu Manickam; Andrew S. Lan; Gautam Dasarathy; Richard G. Baraniuk (ASONAM, 2019)

Distance-Penalized Active Learning via Markov Decision Processes. - Dingyu Wang; John Lipor; Gautam Dasarathy (DSW, 2019)

A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery. - Ali Mousavi 003; Gautam Dasarathy; Richard G. Baraniuk (ICLR (Poster), 2019)

Gaussian Graphical Model Selection from Size Constrained Measurements. - Gautam Dasarathy (ISIT, 2019)

A Tunable Loss Function for Classification. - Tyler Sypherd; Mario Díaz; Harshit Laddha; Lalitha Sankar; Peter Kairouz; Gautam Dasarathy (CoRR, 2019)

Multi-fidelity Gaussian Process Bandit Optimisation. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (J. Artif. Intell. Res., 2019)

Thresholding Graph Bandits with GrAPL. - Daniel LeJeune; Gautam Dasarathy; Richard G. Baraniuk (AISTATS, 2020)

Regularization via Structural Label Smoothing. - Weizhi Li; Gautam Dasarathy; Visar Berisha (AISTATS, 2020)

Differentiable Programming for Hyperspectral Unmixing Using a Physics-Based Dispersion Model. - John Janiczek; Parth Thaker; Gautam Dasarathy; Christopher S. Edwards; Philip Christensen; Suren Jayasuriya (ECCV (27), 2020)

On the α-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (ISIT, 2020)

On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems. - Parth K. Thaker; Gautam Dasarathy; Angelia Nedic (ISIT, 2020)

Finding the Homology of Decision Boundaries with Active Learning. - Weizhi Li; Gautam Dasarathy; Karthikeyan Natesan Ramamurthy; Visar Berisha (NeurIPS, 2020)

On the alpha-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (CoRR, 2020)

Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model. - Nafiseh Ghoroghchian; Gautam Dasarathy; Stark C. Draper (AISTATS, 2021)

Bayesian Optimization in High-Dimensional Spaces: A Brief Survey. - Mohit Malu; Gautam Dasarathy; Andreas Spanias (IISA, 2021)

State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks. - Behrouz Azimian; Reetam Sen Biswas; Anamitra Pal; Lang Tong 001; Gautam Dasarathy (CoRR, 2021)

Pure Exploration in Multi-armed Bandits with Graph Side Information. - Parth K. Thaker; Nikhil Rao 001; Mohit Malu; Gautam Dasarathy (CoRR, 2021)

Digital medicine and the curse of dimensionality. - Visar Berisha; Chelsea Krantsevich; P. Richard Hahn; Shira Hahn; Gautam Dasarathy; Pavan K. Turaga; Julie Liss (npj Digit. Medicine, 2021)

A Multisensory Approach to Present Phonemes as Language Through a Wearable Haptic Device. - Nathan Dunkelberger; Jennifer L. Sullivan; Joshua Bradley; Indu Manickam; Gautam Dasarathy; Richard G. Baraniuk; Marcia K. O'Malley (IEEE Trans. Haptics, 2021)

Learning the Structure of Large Networked Systems Obeying Conservation Laws. - Anirudh Rayas; Rajasekhar Anguluri; Gautam Dasarathy (NeurIPS, 2022)

Maximizing and Satisficing in Multi-armed Bandits with Graph Information. - Parth Thaker; Mohit Malu; Nikhil Rao 001; Gautam Dasarathy (NeurIPS, 2022)

A label efficient two-sample test. - Weizhi Li; Gautam Dasarathy; Karthikeyan Natesan Ramamurthy; Visar Berisha (UAI, 2022)

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data. - Nima T. Bazargani; Gautam Dasarathy; Lalitha Sankar; Oliver Kosut (CoRR, 2022)

Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version). - Nafiseh Ghoroghchian; Anguluri Rajasekhar; Gautam Dasarathy; Stark C. Draper (CoRR, 2022)

Robust Model Selection of Non Tree-Structured Gaussian Graphical Models. - Abrar Zahin; Rajasekhar Anguluri; Oliver Kosut; Lalitha Sankar; Gautam Dasarathy (CoRR, 2022)

Grid Topology Identification With Hidden Nodes via Structured Norm Minimization. - Anguluri Rajasekhar; Gautam Dasarathy; Oliver Kosut; Lalitha Sankar (IEEE Control. Syst. Lett., 2022)

A Graph-Based Approach to Boundary Estimation With Mobile Sensors. - Sean O. Stalley; Dingyu Wang; Gautam Dasarathy; John Lipor (IEEE Robotics Autom. Lett., 2022)

State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks. - Behrouz Azimian; Reetam Sen Biswas; Shiva Moshtagh; Anamitra Pal; Lang Tong 001; Gautam Dasarathy (IEEE Trans. Instrum. Meas., 2022)

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization. - Tyler Sypherd; Mario Díaz; John Kevin Cava; Gautam Dasarathy; Peter Kairouz; Lalitha Sankar (IEEE Trans. Inf. Theory, 2022)

Differential Analysis for Networks Obeying Conservation Laws. - Anirudh Rayas; Rajasekhar Anguluri; Jiajun Cheng; Gautam Dasarathy (ICASSP, 2023)

Class GP: Gaussian Process Modeling for Heterogeneous Functions. - Mohit Malu; Giulia Pedrielli; Gautam Dasarathy; Andreas Spanias (LION, 2023)

The Sample Complexity of Differential Analysis for Networks that Obey Conservation Laws. - Jiajun Cheng; Anirudh Rayas; Rajasekhar Anguluri; Gautam Dasarathy (IEEECONF, 2024)

Computationally Efficient Active Learning of Gaussian Graphical Models. - Abrar Zahin; Gautam Dasarathy (IEEECONF, 2024)

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 (ICASSP, 2024)

Rapid Change Localization in Dynamic Graphical Models. - Abrar Zahin; Weizhi Li; Gautam Dasarathy (ICASSP, 2024)

ClassBO: Bayesian Optimization for Heterogeneous Functions. - Mohit Malu; Giulia Pedrielli; Gautam Dasarathy; Andreas Spanias (LION, 2024)

Advanced Tutorial: Label-Efficient Two-Sample Tests. - Weizhi Li; Visar Berisha; Gautam Dasarathy (WSC, 2024)

On Characterizations of Potential and Ordinal Potential Games. - Sina Arefizadeh; Angelia Nedich; Gautam Dasarathy (CoRR, 2024)

Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching. - Vineet Sunil Gattani; Junshan Zhang; Gautam Dasarathy (CoRR, 2024)

Structure Learning in Gaussian Graphical Models from Glauber Dynamics. - Vignesh Tirukkonda; Anirudh Rayas; Gautam Dasarathy (CoRR, 2024)

Orthogonality and graph divergence losses promote disentanglement in generative models. - Ankita Shukla; Rishi Dadhich; Rajhans Singh; Anirudh Rayas; Pouria Saidi; Gautam Dasarathy; Visar Berisha; Pavan K. Turaga (Frontiers Comput. Sci., 2024)

Active Sequential Two-Sample Testing. - Weizhi Li; Prad Kadambi; Pouria Saidi; Karthikeyan Natesan Ramamurthy; Gautam Dasarathy; Visar Berisha (Trans. Mach. Learn. Res., 2024)

High dimensional Bayesian optimization for circuit design. - Mohit Malu; Diann Dow; Prateek Sharma; Alexis Cottam; Mat Binggeli; Gautam Dasarathy; Giulia Pedrielli; Andreas Spanias (Intell. Decis. Technol., 2025)

Unraveling overoptimism and publication bias in ML-driven science. - Pouria Saidi; Gautam Dasarathy; Visar Berisha (Patterns, 2025)

Robust Model Selection of Gaussian Graphical Models. - Abrar Zahin; Rajasekhar Anguluri; Lalitha Sankar; Oliver Kosut; Gautam Dasarathy (Trans. Mach. Learn. Res., 2025)

Learning Networks From Wide-Sense Stationary Stochastic Processes. - Anirudh Rayas; Jiajun Cheng; Rajasekhar Anguluri; Deepjyoti Deka; Gautam Dasarathy (IEEE Trans. Signal Inf. Process. over Networks, 2025)

Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health. - Pavel Dolin; Weizhi Li; Gautam Dasarathy; Visar Berisha (CoRR, 2025)