Publications

Papers

2025

• M. A. Arshad, T. Z. Jubery, T. Roy, R. Nassiri, A. K. Singh, A. Singh, C. Hegde, B. Ganapathysubramanian, A. Balu. A. Krishnamurthy, S. Sarkar, Leveraging Vision Language Models for Specialized Agricultural Tasks, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), February 2025.

2024

• B. Feuer, J. Xu, P. Yubeaton, G. Mittal, N. Cohen, C. Hegde, SELECT: A Benchmark for Image Dataset Curation, Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, December 2024.

• C.-H. Yang, B. Feuer, Z. Jubery, Z. Deng, A. Nakkab, M. Hasan, S. Chiranjeevi, K. Marshall, N. Baishnab, A. K. Singh, A. Singh, S. Sarkar, N. Merchant, C. Hegde, B. Ganapathysubramanian, Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity, Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, December 2024. Spotlight presentation.

• A. Jignasu, K. Marshall, A. K. Mishra, L. N. Rillo, B. Ganapathysubramian, A. Balu. C. Hegde, A. Krishnamurthy, SLICE-100K: A Multimodal Dataset for Extrusion-Based 3D Printing, Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, December 2024.

• B. Feuer, R. Schirrmeister, V. Cherepanova, C. Hegde, F. Hutter, M. Goldblum, N. Cohen, C. White, TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks, Neural Information Processing Systems (NeurIPS), December 2024.

• N. Dhyani, J. Mo, P. Yubeaton, M. Cho, A. Joshi, S. Garg, B. Reagen, C. Hegde, PriViT: Vision Transformers for Fast Private Inference, Transactions on Machine Learning Research (TMLR), October 2024.

• S. Banerjee, S. P. Mullangi, S. Wagle, C. Hegde, N. Memon, Mitigating the Impact of Attribute Editing on Face Recognition, International Joint Conference on Biometrics (IJCB), September 2024.

• B. Feuer, Y. Liu, C. Hegde, J. Freire, ArcheType: Open-Source Column Type Annotation Using Large Language Models, International Conference on Very Large Databases (VLDB), August 2024. code

• G. Mittal, C. Hegde, N. Memon, Gotcha: Real-Time Video Deepfake Detection via Challenge-Response, IEEE European Symposium on Security and Privacy (Euro S&P), July 2024.

• C. White, S. Dooley, M. Roberts, A. Pal, B. Feuer, S. Jain, R. Schwartz-Ziv, N. Jain, K. Saifullah, S. Naidu, C. Hegde, Y. LeCun, T. Goldstein, W. Neiswanger, M. Goldblum, LiveBench: A Challenging, Contamination-Free LLM Benchmark, June 2024.

• N. Saadati, M. Pham, N. Saleem, J. Waite, A. Balu, Z. Jiang, C. Hegde, S. Sarkar, DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2024.

• M. Pham, K. Marshall, C. Hegde, N. Cohen, Robust Concept Erasure Using Task Vectors, April 2024. Short versions in CVPR Workshop on Generative Models in Computer Vision and CVPR Workshop on Responsible Generative AI (ReGenAI), June 2024.

• A. Jignasu, K. Marshall, A. Balu, B. Ganapathysubramanian, C. Hegde, A. Krishnamurthy, Evaluating Large Language Models for G-Code Debugging, Manipulation, and Comprehension, IEEE Workshop on LLM-Aided Design (LAD), June 2024. code.

• A. Gajjar, X. Xu, C. Hegde, C. Musco, W. M. Tai, Y. Li, Agnostic Active Learning of Single Index Models with Linear Sample Complexity, Conference on Learning Theory (COLT), June 2024.

• M. Pham, K. Marshall, N. Cohen, G. Mittal, C. Hegde, Circumventing Concept Erasure Methods for Text-to-Image Generative Models. (Website, code). International Conference on Learning Representations (ICLR), May 2024.

• A. Nirala, A. Joshi, C. Hegde, S. Sarkar, Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing, IEEE Conference on Secure and Trustworthy Machine Learning (SATML), April 2024.

• G. Mittal, A. Jacobsson, K. Marshall, C. Hegde, N. Memon, AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response, February 2024.

• S. Banerjee, G. Mittal, A. Joshi, C. Hegde, N. Memon, Identity-Preserving Aging of Face Images via Latent Diffusion Models, IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024.

• Md Z. Hasan, J. Chen, J. Wang, Md S. Rahman, A. Joshi, S. Velipasalar, C. Hegde, A. Sharma, S. Sarkar, Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos, IEEE Transactions on Intelligent Transportation Systems (ITS), 2024.

• B. Feuer, A. Joshi, M. Cho, S. Chiranjeevi, Z. Deng, A. Balu, A. Singh, S. Sarkar, N. Merchant, A. Singh, B. Ganapathysubramanian, C. Hegde, Zero-Shot Insect Detection via Weak Language Supervision, The Plant Phenome Journal (TPPJ), 2024.

• B. Khara, E. Herron, Z. Jiang, A. Balu, C.-H. Yang, K. Saurabh, A. Jignasu, S. Sarkar, C. Hegde, A. Krishnamurthy, B. Ganapathysubramaniam, Neural PDE Solvers for Irregular Domains, Computer-Aided Design (CAD), 2024.

• B. Khara, A. Joshi, A. Balu, S. Sarkar, A. Krishnamurthy, C. Hegde, B. Ganapathysubramanian, NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs, Engineering with Computers (EWCO), 2024.

2023

• B. Feuer, C. Hegde, Exploring Dataset-Scale Indicators of Data Quality, NeurIPS Workshop on Attributing Model Behavior at Scale (ATTRIB), December 2023.

• B. Feuer, N. Cohen, C. Hegde, Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks, NeurIPS Workshop on Tabular Representation Learning (TRL), December 2023.

• D. McElfresh, S. Khandagale, J. Valverde, V. Prasad, B. Feuer, C. Hegde, G. Ramakrishnan, M. Goldblum, C. White, When Do Neural Nets Outperform Boosted Trees on Tabular Data?, Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, December 2023.

• F. Duman Keles, C. Hegde, On The Fine-Grained Hardness of Inverting Generative Models, September 2023. Short version to appear in NeurIPS Workshop on Mathematics of Modern Machine Learning (M3L), December 2023.

• A. Gajjar, X. Xu, C. Hegde, C. Musco, Improved Bounds for Agnostic Active Learning of Single Index Models, NeurIPS Workshop on Real World Machine Learning (RealML), December 2023.

• M. Pham, K. Marshall, N. Cohen. G. Mittal, C. Hegde, Circumventing Concept Erasure Methods for Text-to-Image Generative Models, August 2023. (Website, code). Short version in NeurIPS Workshop on Diffusion Models, December 2023.

• S. Banerjee, G. Mittal, A. Joshi, C. Hegde, N. Memon, Identity-Preserving Aging of Face Images via Latent Diffusion Models, International Joint Conference on Biometrics (IJCB), September 2023. Invited to Special Issue of IEEE Transactions on Biometrics.

• B. Feuer, A. Joshi, M. Pham, C. Hegde, Distributionally Robust Classification on A Data Budget, Transactions on Machine Learning Research (TMLR), August 2023.

• K. Marshall, M. Pham, A. Joshi, A. Jignasu, A. Balu, A. Krishnamurthy, C. Hegde, ZeroForge: Feedforward Text-to-Shape Without 3D Supervision, June 2023. (Website, code).

• A. Joshi, S. C. Akula, G. Jagatap, C. Hegde, A Few Adversarial Tokens Can Break Vision Transformers, CVPR Workshop on Art of Adversarial Robustness, June 2023.

• J. Li, T. Nguyen, C. Hegde, and R. Wong, Implicit Regularization for Group Sparsity, International Conference on Learning Representations (ICLR), May 2023.

• A. Gajjar, C. Hegde, C. Musco, Active Learning for Single Neuron Models with Lipschitz Non-Linearities, Artificial Intelligence and Statistics (AISTATS), April 2023.

• M. Pham, M. Cho, A. Joshi, C. Hegde, Revisiting Self-Distillation, March 2023.

• F. Duman Keles, P. M. Wijewardena, C. Hegde, On The Computational Complexity of Self-Attention, International Conference on Algorithmic Learning Theory (ALT), February 2023.

• A. Nakkab, B. Feuer, C. Hegde, LiT Tuned Models for Efficient Species Detection, AAAI Workshop on AI for Agriculture and Food Sciences (AIAFS), February 2023. Invited to Special Issue of Plant Phenomics.

• B. Feuer, A. Joshi, M. Cho, K. Jani, S. Chiranjeevi, Z. Deng, A. Balu, N. Merchant, A. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian, C. Hegde, Zero-Shot Insect Detection via Weak Language Supervision, AAAI Workshop on AI for Agriculture and Food Sciences (AIAFS), February 2023.

2022

• B. Feuer, A. Joshi, C. Hegde, Caption Supervision Enables Robust Learners, 2022.

• Md Z. Hasan, A. Joshi, M. Rahman, V. Archana, A. Sharma, C. Hegde, S. Sarkar, DriveCLIP: Zero-shot transfer for distracted driving activity understanding using CLIP, NeurIPS Workshop on Autonomous Driving Systems (ADS), December 2022.

• T. Nguyen, G. Jagatap, C. Hegde, Provable Compressed Sensing with Generative Priors via Langevin Dynamics, IEEE Transactions on Information Theory, November 2022.

• M. Cho, Z. Ghodsi, B. Reagen, S. Garg, C. Hegde, Sphynx: ReLU-Efficient Network Design for Private Inference, IEEE Security and Privacy, October 2022.

• A. Mukherjee, A. Joshi, A. Sharma, C. Hegde, S. Sarkar, Generative Semantic Domain Adaptation for Perception in Autonomous Driving, Journal of Big Data Analytics in Transportation, August 2022.

• M. Cho, A. Joshi, S. Garg, B. Reagen, C. Hegde, Selective Network Linearization for Private Inference, International Conference on Machine Learning (ICML), July 2022.

• B. Feuer, A. Joshi, C. Hegde, A Meta-Analysis of Distributionally Robust Models, ICML Workshop on Principles of Distribution Shift (PODS), July 2022.

• A. Joshi, G. Jagatap, C. Hegde, Adversarial Token Attacks on Vision Transformers, CVPR Workshop on Transformers for Vision (CVPR-T4V), June 2022.

• C. Yang, B. Pokuri, X. Lee, S. Balakrishnan, C. Hegde, S. Sarkar, B. Ganapathysubramaniam, Multi-fidelity machine learning models for structure-property mapping of organic electronics, Computational Material Science, June 2022.

• A. Joshi, M. Pham, M. Cho, L. Boytsov, F. Condessa, J. Z. Kolter, C. Hegde, Smooth-Reduce: Leveraging Patches for Improved Certified Robustness, May 2022.

• A. Prasad, A. Balu, H. Shah, S. Sarkar, C. Hegde, A. Krishnamurthy, NURBS-Diff: A Differentiable Programming Module for NURBS, Computer Aided Design (CAD), May 2022.

• T. Nguyen, G. Jagatap, C. Hegde, Inverse Imaging with Generative Priors via Langevin Dynamics, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2022.

• Z. Jiang, X. Lee, S. Tan, K. Tan, A. Balu, Y. Lee, C. Hegde, S. Sarkar, MDPGT: Momentum-based Decentralized Policy Gradient Tracking, AAAI Conference on Artificial Intelligence (AAAI), February 2022.

• K. Marshall, M. Cho, C. Hegde, Differentiable Design with Dynamic Programming, AAAI Workshop on AI for Design and Manufacturing (ADAM), February 2022.

• M. Cho, K. Nagasubramanian, A. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, C. Hegde, Privacy-Preserving Deep Models for Plant Stress Phenotyping, AAAI Workshop on AI for Agriculture and Food Sciences (AIAFS), February 2022.

• G. Jagatap, A. Joshi, A. B. Chowdhury, S. Garg, C. Hegde, Adversarially Robust Learning via Entropic Regularization, Frontiers in Artificial Intelligence, January 2022.

2021

• M. Cho, A. Balu, A. Joshi, A. Prasad, B. Khara, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy, C. Hegde, Differentiable Spline Approximations, Neural Information Processing Systems (NeurIPS), December 2021.

• J. Li, T. Nguyen, C. Hegde, and R. Wong. Implicit Sparse Regularization: The Impact of Depth and Early Stopping, Neural Information Processing Systems (NeurIPS), December 2021.

• A. Balu, S. Botelho, B. Khara, V. Rao, C. Hegde, S. Sarkar, S. Adavani, A. Krishnamurthy, B. Ganapathysubramanian, Distributed Multigrid Neural Solvers on Megavoxel Domains, International Conference on High Performance Computing (SC), November 2021.

• B. Khara, A. Balu, A. Joshi, S. Sarkar, C. Hegde, A. Krishnamurthy, B. Ganapathysubramanian, DiffNet: Neural Field Solutions to Parametric Partial Differential Equations, October 2021.

• Z. Jiang, A. Balu, C. Hegde, S. Sarkar, On Consensus-Optimality Trade-offs in Collaborative Deep Learning, Frontiers in Artificial Intelligence, September 2021.

• T. Huang, P. Chakraborty, A. Sharma, C. Hegde, Large-Scale Data-Driven Traffic Sensor Health Monitoring, *Journal of Big Data Analytics in Transportation, August 2021.

• T. Nguyen, R. Wong, and C. Hegde. Benefits of jointly training autoencoders: An improved neural tangent kernel analysis, IEEE Transactions on Information Theory, July 2021.

• Y. Esfendiari, S. Tan, A. Balu, Z. Jiang, E. Herron, C. Hegde, S. Sarkar, Cross-Gradient Aggregation for Decentralized Learning from Non-IID data, International Conference on Machine Learning (ICML), July 2021.

• M. Cho, A. Joshi, C. Hegde, ESPN: Extremely Sparse Pruned Networks, IEEE Data Science and Learning Workshop (DSLW), June 2021.

• A. Balu, Z. Jiang, S. Tan, C. Hegde, Y. Lee, S. Sarkar, Decentralized Deep Learning using Momentum-Accelerated Consensus, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), June 2021.

• M. Cho, M. Soltani, C. Hegde One-Shot Neural Architecture Search via Compressive Sensing, ICLR Workshop on Neural Architecture Search, May 2021.

• V. Shah, R. Hyder, S. Asif, C. Hegde Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models, May 2021.

• V. Shah, C. Hegde, Sparse Signal Recovery from Modulo Observations, EURASIP Journal on Advances in Signal Processing, April 2021.

• X. Lee, J. Waite, C. Yang, B. Pokuri, A. Joshi, A. Balu, C. Hegde, B. Ganapathysubramaniam, S. Sarkar, Fast Inverse Design of Microstructures via Generative Invariance Networks, Nature Computational Science, March 2021.

• B. Khara, Aditya Balu, Ameya Joshi, A. Krishnamurthy, S. Sarkar, C. Hegde, B. Ganapathysubramanian, Field Solutions of Parametric PDEs, AAAI Symposium on Machine Learning for Physical Sciences (AAAI-MLPS), March 2021.

• C. Hegde, F. Keinert, E. Weber, A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations, Applied and Numerical Harmonic Analysis, February 2021.

• S. Asif and C. Hegde, The benefits of side information for structured phase retrieval, European Conference on Signal Proc. and Comm. (EUSIPCO), 2020.

2020

• M. Cho, A. Joshi, A. Balu, A. Krishnamurthy, S. Sarkar, B. Ganapathysubramaniam, C. Hegde, Differentiable Programming for Piecewise Polynomial Functions, NeurIPS Workshop on Learning Methods for Combinatorial Algorithms (LMCA), December 2020.

• Z. Jiang, X. Lee, S. Tan, A. Balu, C. Hegde, S. Sarkar, Adaptive Gradient Tracking In Stochastic Optimization, NeurIPS Workshop on Optimization for Machine Learning (OPT), December 2020.

• S. Botelho, A. Joshi, B. Khara, S. Sarkar, C. Hegde, S. Adavani, B. Ganapathysubramanian, Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models, Machine Learning for High Performance Computing (MLHPC), November 2020.

• M. Cho, M. Soltani, and C. Hegde, Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery, July 2020.

• C. Hegde, Learning Sparse Graphs via Sub-Gradient Descent, July 2020.

• G. Jagatap and C. Hegde, High Dynamic Range Imaging Using Deep Image Priors, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020.

• T. Nguyen, Y. Mroueh, S. Hoffman, P. Das, P. Dognin, G. Romano, and C. Hegde, Nano-material configuration design with deep surrogate Langevin dynamics, ICLR Workshop on Deep Learning and Differential Equations, May 2020.

• V. Ahsani, A. Sharma, C. Hegde, S. Knickerbocker, N. Hawkins, Improving Probe-Based Congestion Performance Metrics Accuracy Using Change-Point Detection, Journal of Big Data Analytics in Transportation, April 2020.

• A. Joshi, M. Cho, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde, InvNets: Encoding Statistical and Geometric Constraints in Deep Generative Models, AAAI Conference on Artificial Intelligence (AAAI), February 2020.

• X. Lee, S. Ghadai, K. Tan, C. Hegde, S. Sarkar, Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents, AAAI Conference on Artificial Intelligence (AAAI), February 2020.

2019

• G. Jagatap and C. Hegde, Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors, Neural Information Processing Systems (NeurIPS), December 2019.

• P. Chakraborty, J. Merickel, V. Shah, A. Sharma, C. Hegde, C. Desouza, A. Drinzic, P. Gunaratne, and M. Rizzo, Quantifying Vehicle Control from Physiology in Type-1 Diabetes, vol 20, Traffic Injury Prevention (TIP), November 2019.

• V. Shah and C. Hegde, Signal Reconstruction from Modulo Observations, IEEE GlobalSIP, November 2019.

• A. Joshi, A. Mukherjee, S. Sarkar, and C. Hegde, Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers, International Conference on Computer Vision (ICCV), October 2019.

• G. Jagatap, Z. Chen, S. Nayer, C. Hegde, and N. Vaswani, Sub-diffraction Super-resolution Imaging for Structured Data, IEEE Transactions on Computational Imaging, vol. 6, October 2019.

• T. Nguyen, R. Wong, and C. Hegde, Provably Accurate Double-Sparse Coding, Journal of Machine Learning Research (JMLR), vol. 20, no. 141, September 2019.

• V. Shah, J. Merickel, P. Chakraborty, C. Hegde, A. Sharma, C. Desouza, A. Drincic, P. Gunaratne, and M. Rizzo. Quantifying driver speed behavior from real-time physiology in type 1 diabetes, Intl. Symp. Future Active Safety Technology (FastZero), September 2019.

• J. Merickel, V. Shah, P. Chakraborty, C. Hegde, A. Sharma, C. Desouza, A. Drincic, P. Gunaratne, and M. Rizzo. Impact of physiology and environment on vehicle control behavior in drivers with type 1 diabetes, Intl. Symp. Future Active Safety Technology (FastZero), September 2019.

• P. Chakraborty, C. Hegde, and A. Sharma, Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds, Transportation Research Part-C, vol. 105, p81-99, August 2019.

• M. Soltani and C. Hegde, Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks, IEEE Transactions on Signal Processing, vol. 67, no. 13, p3361-3371, July 2019.

• G. Jagatap and C. Hegde, Sample Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements, IEEE Transactions on Information Theory, vol. 65, no. 7, p4434-4456, July 2019.

• G. Jagatap and C. Hegde, Linearly Convergent Algorithms for Learning Shallow Residual Networks, International Symposium on Information Theory (ISIT), July 2019.

• T. Nguyen, A. Soni, and C. Hegde, Tractable Learning of Sparsely Used Dictionaries from Incomplete Samples, Sampling Theory and Applications (SampTA), July 2019.

• A. Mukherjee, A. Joshi, S. Sarkar, and C. Hegde, Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models, CVPR Workshop on Vision for All Seasons (VAS), June 2019.

• V. Shah, A. Joshi, S. Ghoshal, B. Pokuri, B. Ganapathysubramaniam, S. Sarkar, C. Hegde Encoding Invariances in Deep Generative Models, June 2019.

• M. Cho and C. Hegde, Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2019.

• R. Hyder, V. Shah, C. Hegde, and S. Asif Alternating Phase Projected Gradient Descent With Generative Priors for Compressive Phase Retrieval, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2019.

• T. Nguyen, R. Wong, and C. Hegde, On the Dynamics of Gradient Descent for Autoencoders, Artificial Intelligence and Statistics (AISTATS), April 2019.

2018

• R. Singh, V. Shah, B. Pokuri, B. Ganapathysubramaniam, S. Sarkar, C. Hegde Physics-aware Deep Generative Models for Creating Synthetic Microstructures, NIPS Workshop on Machine Learning for Molecules and Materials (MLMM), December 2018.

• P. Chakraborty, A. Sharma, and C. Hegde, Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach, International Conference on Intelligent Transportation Systems (ITSC), November 2018.

• S. Asif and C. Hegde, Phase Retrieval for Signals in Unions of Subspaces, IEEE GlobalSIP, November 2018.

• C. Hegde, Algorithmic Aspects of Inverse Problems Using Generative Models, Allerton Conference on Communication, Control, and Computing, October 2018.

• G. Jagatap, Z. Chen, C. Hegde, and N. Vaswani, Model-Corrected Low-Rank Ptychography, International Conference on Image Processing (ICIP), October 2018.

• T. Nguyen, A. Soni, and C. Hegde, On Learning Sparsely Used Dictionaries from Incomplete Samples, International Conference on Machine Learning (ICML), July 2018.

• Z. Jiang, A. Balu, C. Hegde, and S. Sarkar, Incremental Consensus-based Collaborative Deep Learning, ICML Workshop on Nonconvex Optimization in Machine Learning, July 2018. *Spotlight.*

• G. Jagatap and C. Hegde, Towards Sample-Optimal Methods for Solving Random Quadratic Equations with Structure, International Symposium on Information Theory (ISIT), June 2018.

• M. Soltani and C. Hegde, Fast Low-Rank Matrix Estimation for Ill-Conditioned Matrices, International Symposium on Information Theory (ISIT), June 2018.

• M. Soltani and C. Hegde, Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation, Artificial Intelligence and Statistics (AISTATS), April 2018. *Oral presentation.*

• V. Shah and C. Hegde, Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2018.

• G. Jagatap, Z. Chen, C. Hegde, and N. Vaswani, Sub-Diffraction Imaging Using Fourier Ptychography and Structured Sparsity, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2018. *Oral presentation.*

• Z. Chen, G. Jagatap, S. Nayer, C. Hegde, and N. Vaswani, Low-Rank Fourier Ptychography, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2018.

• T. Nguyen, R. Wong, and C. Hegde, A Provable Approach for Double-Sparse Coding, AAAI Conference on Artificial Intelligence (AAAI), February 2018. *Oral presentation.*

2017

• G. Jagatap and C. Hegde, Fast, Sample-Efficient Algorithms for Structured Phase Retrieval, Neural Information Processing Systems (NIPS), December 2017.

• Z. Jiang, A. Balu, C. Hegde, and S. Sarkar, Collaborative Deep Learning in Fixed Topology Networks, Neural Information Processing Systems (NIPS), December 2017.

• A. Balu, T. Nguyen, A. Kokate, C. Hegde, and S. Sarkar, A Forward-Backward Approach for Visualizing Information Flow in Deep Networks, NIPS Symposium on Interpretable Machine Learning, December 2017.

• M. Cohen, C. Hegde, S. Jegelka, and L. Schmidt, Efficiently Optimizing over (Non-Convex) Cones via Approximate Projections, NIPS Workshop on Optimization for Machine Learning (OPT), December 2017. *Oral presentation.*

• P. Chakraborty, C. Hegde, and A. Sharma, Trend Filtering in Network Time Series, with Applications to Traffic Incident Detection, NIPS Time Series Workshop (TSW), December 2017.

• M. Soltani and C. Hegde, Fast Low-Rank Matrix Estimation without the Condition Number, December 2017.

• C. Hubbard and C. Hegde, Parallel Computing Heuristics for Matrix Completion, IEEE GlobalSIP Symposium on Accelerating Deep Learning, November 2017.

• M. Soltani and C. Hegde, Demixing Structured Superpositions from Periodic and Aperiodic Nonlinear Observations, IEEE GlobalSIP Symposium on Compressed Sensing and Deep Learning, November 2017.

• V. Shah, M. Soltani and C. Hegde, Reconstruction from Periodic Nonlinearities, with Applications to HDR Imaging, Asilomar Conference on Signals, Systems, and Computers, November 2017.

• M. Soltani and C. Hegde, Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations, IEEE Transactions on Signal Processing, vol. 65, no. 16, p4209-4222, August 2017.

• M. Soltani and C. Hegde, Fast Algorithms for Learning Latent Variables in Graphical Models, ACM KDD Mining and Learning With Graphs (KDD MLG), August 2017.

• B. Wang, C. Gan, J. Yang, C. Hegde, J. Wu, Graph-Based Multiple-Line Outages in Power Transmission Systems, IEEE PES General Meeting (PES-GM), July 2017.

• M. Soltani and C. Hegde, Stable Recovery from Random Sinusoidal Feature Maps, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.

2016

• C. Hegde, P. Indyk, and L. Schmidt, Fast Recovery from a Union of Subspaces, Neural Information Processing Systems (NIPS), December 2016.

• M. Soltani and C. Hegde, Iterative Thresholding for Demixing Structured Superpositions in High Dimensions, NIPS Workshop on Learning in High Dimensions with Structure (LHDS), December 2016. *Oral presentation.*

• M. Soltani and C. Hegde, A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations, IEEE GlobalSIP Symposium on Compressed Sensing and Deep Learning, December 2016.

• M. Soltani and C. Hegde, Demixing Sparse Signals from Nonlinear Observations, Asilomar Conference on Signals, Systems, and Computers, November 2016.

• C. Hubbard, J. Bavslik, C. Hegde, and C. Hu, Data-Driven Prognostics of Li-Ion Rechargeable Battery using Bilinear Kernel Regression, Annual Conference of the Prognostics and Health Management Society (PHM), October 2016.

• C. Hegde, P. Indyk, and L. Schmidt, A Nearly Linear-Time Framework for Graph-Structured Sparsity, International Joint Conferences on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, July 2016. (Invited paper)\

• C. Hegde, Bilevel Feature Selection in Nearly-Linear Time, IEEE Statistical Signal Processing Workshop (SSP), June 2016.\

• C. Hegde, A Fast Algorithm for Demixing Signals with Structured Sparsity, International Conference on Signal Processing and Communications (SPCOM), June 2016. (Invited paper)\

2015

• C. Hegde, P. Indyk, and L. Schmidt, Fast Algorithms for Structured Sparsity, Bulletin of the EATCS, no. 117, p197-228, October 2015.

• C. Hegde, A. C. Sankaranarayanan, W. Yin, and R. G. Baraniuk, NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings, IEEE Transactions on Signal Processing, vol. 63, no. 22, p6109-6121, November 2015.

• C. Hegde, P. Indyk, and L. Schmidt, Approximation Algorithms for Model-Based Compressive Sensing, IEEE Transactions on Information Theory, vol. 61, no. 9, p5129-5147, September 2015.

• C. Hegde, P. Indyk, and L. Schmidt, A Nearly Linear-Time Framework for Graph-Structured Sparsity, International Conference on Machine Learning (ICML), July 2015.
  *Winner of the Best Paper Award. *

• Y. Li, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and K. F. Kelly, Compressive Image Acquisition and Classification via Secant Projections, Journal of Optics, vol. 17, no. 6, June 2015.

• J. Acharya, I. Diakonikolas, C. Hegde, J. Li, L. Schmidt, Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms, ACM Symposium on Principles of Database Systems (PODS), May 2015.

• M. Araya, C. Hegde, P. Indyk, and L. Schmidt, Greedy Strategies for Data Adaptive Shot Selection, Proc. EAGE Annual Meeting, May 2015.

• L. Schmidt, C. Hegde, P. Indyk, L. Lu, X. Chi, and D. Hohl, Seismic Feature Extraction Using Steiner-Tree Methods, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2015.

Before 2015

• C. Hegde, P. Indyk, and L. Schmidt, Nearly Linear-Time Model-Based Compressive Sensing, International Colloquium on Automata, Languages, and Programming (ICALP), July 2014.

• C. Hegde, P. Indyk, and L. Schmidt, A Fast Algorithm for Tree-Sparse Recovery, International Symposium on Information Theory (ISIT), June 2014.

• C. Hegde, A. C. Sankaranarayanan, and R. G. Baraniuk, Lie Operators for Compressive Sensing, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014.

• L. Schmidt, C. Hegde, P. Indyk, J. Kane, L. Lu, D. Hohl, Automatic Fault Localization Using the Generalized Earth Movers Distance Model, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014.

• S. Nagaraj, C. Hegde, A. C. Sankaranarayanan, and R. G. Baraniuk, Optical Flow-Based Transport on Image Manifolds, Applied and Computational Harmonic Analysis, vol. 36, no. 2, p280-301, March 2014.

• C. Hegde, P. Indyk, and L. Schmidt, Approximation-Tolerant Model-Based Compressive Sensing, ACM Symposium on Discrete Algorithms (SODA), January 2014.

• E. Grant, C. Hegde, and P. Indyk, Nearly Optimal Linear Embeddings into Very Low Dimensions, IEEE GlobalSIP Symposium on Sensing and Statistical Inference, December 2013.

• L. Schmidt, C. Hegde, and P. Indyk, The Constrained Earth Movers Distance Model, with Applications to Compressive Sensing, Sampling Theory and Applications (SampTA), July 2013.

• C. Hegde, A. C. Sankaranarayanan, and R. G. Baraniuk, Learning Measurement Matrices for Redundant Dictionaries, Signal Processing with Adaptive Sparse Structured Representations (SPARS), July 2013.

• Y. Li, C. Hegde, R. G. Baraniuk, and K. F. Kelly, Compressive Classification via Secant Projections, Computational Optical Sensing and Imaging (COSI), June 2013.

• C. Hegde and R. G. Baraniuk, Signal Recovery on Incoherent Manifolds, IEEE Transactions on Information Theory, vol. 58, no. 12, p7204-7214, December 2012.

• D. K. Grady, M. Moll, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and L. E. Kavraki, Multi-Robot Target Verification with Reachability Constraints , IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), November 2012.

• D. K. Grady, M. Moll, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and L. E. Kavraki, Multi-Objective Sensor Replanning for a Car-Like Robot, IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), November 2012.

• C. Hegde, A. C. Sankaranarayanan, and R. G. Baraniuk, Near-Isometric Linear Embeddings of Manifolds, IEEE Statistical Signal Processing Workshop (SSP), August 2012.

• C. Hegde and R. G. Baraniuk, SPIN: Iterative Signal Recovery on Incoherent Manifolds, IEEE International Symposium on Information Theory (ISIT), July 2012.

• D. K. Grady, M. Moll, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and L. E. Kavraki, Look Before You Leap: Predictive Sensing and Opportunistic Navigation, IROS Workshop on Open Problems in Motion Planning, September 2011.

• A. C. Sankaranarayanan, C. Hegde, S. Nagaraj, and R. G. Baraniuk, Go with the Flow: Optical Flow-based Transport Operators for Image Manifolds, Allerton Conference on Communication, Control, and Computing, September 2011.

• C. Hegde and R. G. Baraniuk, Sampling and Recovery of Pulse Streams, IEEE Transactions on Signal Processing, vol. 59, no. 4, p1505-1517, April 2011.

• M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk, High-Dimensional Data Fusion via Joint Manifold Learning, AAAI Fall Symposium on Manifold Learning, November 2010.

• M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk, Joint Manifolds for Data Fusion, IEEE Transactions on Image Processing, vol. 19, no. 10, p2580-2594, October 2010.

• R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, Model-Based Compressive Sensing, IEEE Transactions on Information Theory, vol. 56, no. 4, p1982-2001, April 2010.

• C. Hegde and R. G. Baraniuk, Compressive Sensing of a Superposition of Pulses, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2010.

• S. R. Schelle, J. N. Laska, C. Hegde, M. F. Duarte, M. A. Davenport, and R. G. Baraniuk, Texas Hold 'Em Algorithms for Distributed Compressive Sensing, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2010.

• C. Hegde and R. G. Baraniuk, Compressive Sensing of Streams of Pulses, Allerton Conference on Communication, Control, and Computing, September 2009.

• V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, Recovery of Clustered Sparse Signals from Compressive Measurements, Sampling Theory and Applications (SampTA), May 2009.

• C. Hegde, M. F. Duarte, and V. Cevher, Compressive Sensing Recovery of Spike Trains Using a Structured Sparsity Model, Signal Processing with Adaptive Sparse Structured Representations (SPARS), April 2009.
  *Winner of the Best Student Paper Award.*

• M. F. Duarte, C. Hegde, V. Cevher, and R. G. Baraniuk, Recovery of Compressible Signals in Unions of Subspaces, Conference on Information Sciences and Systems (CISS), March 2009.

• V. Cevher, M. F. Duarte, C. Hegde, and R. G. Baraniuk, Sparse Signal Recovery Using Markov Random Fields, Neural Information Processing Systems (NIPS), December 2008.

• C. Hegde, M. B. Wakin, and R. G. Baraniuk , Random Projections for Manifold Learning, Neural Information Processing Systems (NIPS), December 2007.

• M. A. Davenport, C. Hegde, M. B. Wakin, and R. G. Baraniuk, Manifold-Based Approaches for Improved Classification , NIPS Workshop on Topology Learning, December 2007.

• C. Hegde, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk, Efficient Machine Learning Using Random Projections, NIPS Workshop on Efficient Machine Learning, December 2007.

Thesis

• C. Hegde, Nonlinear Signal Models: Geometry, Algorithms, and Analysis
  Ph.D. thesis, ECE Department, Rice University, September 2012.
  *Winner of Ralph Budd Award for Best Thesis in the School of Engineering.*

Books, book chapters, and monographs

• C. Hegde and A. Kamal, Theoretical Foundations of Computer Engineering, Compiled lecture notes, 2017.

• C. Hegde, Principles of Data Analytics, Compiled lecture notes, 2017.

• R. G. Baraniuk, M. A. Davenport, M. F. Duarte, and C. Hegde, An Introduction to Compressive Sensing, Connexions e-textbook, 2011.

Technical reports

• G. Jagatap and C. Hegde, Learning ReLU Networks via Alternating Minimization, June 2018.

• T. Nguyen, R. Wong, and C. Hegde, Autoencoders Learn Generative Linear Models, June 2018.

• M. Soltani and C. Hegde, Improved Algorithms for Matrix Recovery from Rank-One Projections, May 2017.

• C. Hubbard and C. Hegde, GPUFish: A Parallel Computing Framework for Matrix Completion from A Few Observations, November 2016.

• C. Hegde, Bilevel Feature Selection in Nearly-Linear Time, February 2016.

• C. Hegde, A. C. Sankaranarayanan, and R. G. Baraniuk, Learning Manifolds in the Wild, July 2012.

• M. Soltani and C. Hegde, Demixing Sparse Signals from Nonlinear Observations, Iowa State University Technical Report, March 2016.

• C. Hegde, O. C. Tuzel, and F. Porikli, Efficient Upsampling of Natural Images, MERL Technical Report, March 2012.

• M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk, A Theoretical Analysis of Joint Manifolds, Rice University ECE Technical Report TREE0901, January 2009.

• C. Hegde, M. B. Wakin, and R. G. Baraniuk, Random Projections for Manifold Learning: Proofs and Analysis, Rice University ECE Technical Report TREE0710, October 2007.