Chinmay Hegde

Orcid: 0000-0003-4574-8066

According to our database1, Chinmay Hegde authored at least 134 papers between 2007 and 2024.

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Bibliography

2024
Mitigating the Impact of Attribute Editing on Face Recognition.
CoRR, 2024

AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response.
CoRR, 2024

TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks.
CoRR, 2024

2023
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks.
CoRR, 2023

Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing.
CoRR, 2023

Exploring Dataset-Scale Indicators of Data Quality.
CoRR, 2023

ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models.
CoRR, 2023

PriViT: Vision Transformers for Fast Private Inference.
CoRR, 2023

On the Fine-Grained Hardness of Inverting Generative Models.
CoRR, 2023

Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension.
CoRR, 2023

Distributionally Robust Classification on a Data Budget.
CoRR, 2023

Circumventing Concept Erasure Methods For Text-to-Image Generative Models.
CoRR, 2023

Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos.
CoRR, 2023

ZeroForge: Feedforward Text-to-Shape Without 3D Supervision.
CoRR, 2023

LiT Tuned Models for Efficient Species Detection.
CoRR, 2023

Pathfinding Neural Cellular Automata.
CoRR, 2023

Implicit Regularization for Group Sparsity.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Identity-Preserving Aging of Face Images via Latent Diffusion Models.
Proceedings of the IEEE International Joint Conference on Biometrics, 2023

On The Computational Complexity of Self-Attention.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023

Active Learning for Single Neuron Models with Lipschitz Non-Linearities.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Provable Compressed Sensing With Generative Priors via Langevin Dynamics.
IEEE Trans. Inf. Theory, 2022

The Stochastic Augmented Lagrangian method for domain adaptation.
Knowl. Based Syst., 2022

Sphynx: A Deep Neural Network Design for Private Inference.
IEEE Secur. Priv., 2022

Neural PDE Solvers for Irregular Domains.
CoRR, 2022

Caption supervision enables robust learners.
CoRR, 2022

Gotcha: A Challenge-Response System for Real-Time Deepfake Detection.
CoRR, 2022

Revisiting Self-Distillation.
CoRR, 2022

A Meta-Analysis of Distributionally-Robust Models.
CoRR, 2022

Smooth-Reduce: Leveraging Patches for Improved Certified Robustness.
CoRR, 2022

NURBS-Diff: A Differentiable Programming Module for NURBS.
Comput. Aided Des., 2022

Selective Network Linearization for Efficient Private Inference.
Proceedings of the International Conference on Machine Learning, 2022

Inverse Imaging with Generative Priors Via Langevin Dynamics.
Proceedings of the IEEE International Conference on Acoustics, 2022

Distributed Online Non-convex Optimization with Composite Regret.
Proceedings of the 58th Annual Allerton Conference on Communication, 2022

MDPGT: Momentum-Based Decentralized Policy Gradient Tracking.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis.
IEEE Trans. Inf. Theory, 2021

Fast inverse design of microstructures via generative invariance networks.
Nat. Comput. Sci., 2021

On Consensus-Optimality Trade-offs in Collaborative Deep Learning.
Frontiers Artif. Intell., 2021

Adversarially Robust Learning via Entropic Regularization.
Frontiers Artif. Intell., 2021

Sparse signal recovery from modulo observations.
EURASIP J. Adv. Signal Process., 2021

Adversarial Token Attacks on Vision Transformers.
CoRR, 2021

NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs.
CoRR, 2021

Implicit Sparse Regularization: The Impact of Depth and Early Stopping.
CoRR, 2021

Sphynx: ReLU-Efficient Network Design for Private Inference.
CoRR, 2021

Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models.
CoRR, 2021

Distributed multigrid neural solvers on megavoxel domains.
Proceedings of the International Conference for High Performance Computing, 2021

Implicit Sparse Regularization: The Impact of Depth and Early Stopping.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentiable Spline Approximations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data.
Proceedings of the 38th International Conference on Machine Learning, 2021

Decentralized Deep Learning Using Momentum-Accelerated Consensus.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Sample Efficient Fourier Ptychography for Structured Data.
IEEE Trans. Computational Imaging, 2020

Adversarially Robust Learning via Entropic Regularization.
CoRR, 2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models.
CoRR, 2020

Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery.
CoRR, 2020

ESPN: Extremely Sparse Pruned Networks.
CoRR, 2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models.
Proceedings of the 6th IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2020

High Dynamic Range Imaging Using Deep Image Priors.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

The Benefits of Side Information for Structured Phase Retrieval.
Proceedings of the 28th European Signal Processing Conference, 2020

Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks.
IEEE Trans. Signal Process., 2019

Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements.
IEEE Trans. Inf. Theory, 2019

Provably Accurate Double-Sparse Coding.
J. Mach. Learn. Res., 2019

On Higher-order Moments in Adam.
CoRR, 2019

One-Shot Neural Architecture Search via Compressive Sensing.
CoRR, 2019

Encoding Invariances in Deep Generative Models.
CoRR, 2019

A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations.
CoRR, 2019

Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Linearly Convergent Algorithms for Learning Shallow Residual Networks.
Proceedings of the IEEE International Symposium on Information Theory, 2019

Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval.
Proceedings of the IEEE International Conference on Acoustics, 2019

Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity.
Proceedings of the IEEE International Conference on Acoustics, 2019

Signal Reconstruction From Modulo Observations.
Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019

Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

On the Dynamics of Gradient Descent for Autoencoders.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Leaming Structured Signals Using GAN s with Applications in Denoising and Demixing.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

Fourier Phase Retrieval with Side Information Using Generative Prior.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
Physics-aware Deep Generative Models for Creating Synthetic Microstructures.
CoRR, 2018

Learning ReLU Networks via Alternating Minimization.
CoRR, 2018

Autoencoders Learn Generative Linear Models.
CoRR, 2018

Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

Fast Low-Rank Matrix Estimation for Ill-Conditioned Matrices.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Towards Sample-Optimal Methods for Solving Random Quadratic Equations with Structure.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

On Learning Sparsely Used Dictionaries from Incomplete Samples.
Proceedings of the 35th International Conference on Machine Learning, 2018

Model Corrected Low Rank Ptychography.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018

Solving Linear Inverse Problems Using Gan Priors: An Algorithm with Provable Guarantees.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Sub-Diffraction Imaging Using Fourier Ptychography and Structured Sparsity.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Low Rank Fourier Ptychography.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Phase Retrieval for Signals in Union of Subspaces.
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Algorithmic Aspects of Inverse Problems Using Generative Models.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018

Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

A Provable Approach for Double-Sparse Coding.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Fast Algorithms for Demixing Sparse Signals From Nonlinear Observations.
IEEE Trans. Signal Process., 2017

A Forward-Backward Approach for Visualizing Information Flow in Deep Networks.
CoRR, 2017

Phase Retrieval Using Structured Sparsity: A Sample Efficient Algorithmic Framework.
CoRR, 2017

Collaborative Deep Learning in Fixed Topology Networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Fast, Sample-Efficient Algorithms for Structured Phase Retrieval.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stable recovery of sparse vectors from random sinusoidal feature maps.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Demixing structured superposition signals from periodic and aperiodic nonlinear observations.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Parallel computing heuristics for low-rank matrix completion.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Reconstruction from periodic nonlinearities, with applications to HDR imaging.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016
Bilevel feature selection in nearly-linear time.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2016

Fast recovery from a union of subspaces.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A fast iterative algorithm for demixing sparse signals from nonlinear observations.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Demixing sparse signals from nonlinear observations.
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015
NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings.
IEEE Trans. Signal Process., 2015

Approximation Algorithms for Model-Based Compressive Sensing.
IEEE Trans. Inf. Theory, 2015

Fast Algorithms for Structured Sparsity.
Bull. EATCS, 2015

Efficient Upsampling of Natural Images.
CoRR, 2015

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms.
Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

A Nearly-Linear Time Framework for Graph-Structured Sparsity.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Seismic feature extraction using steiner tree methods.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Approximation-Tolerant Model-Based Compressive Sensing.
Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2014

A fast approximation algorithm for tree-sparse recovery.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Automatic fault localization using the generalized Earth Mover's distance.
Proceedings of the IEEE International Conference on Acoustics, 2014

LIE operators for compressive sensing.
Proceedings of the IEEE International Conference on Acoustics, 2014

Nearly Linear-Time Model-Based Compressive Sensing.
Proceedings of the Automata, Languages, and Programming - 41st International Colloquium, 2014

2013
Nearly optimal linear embeddings into very low dimensions.
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013

2012
Signal Recovery on Incoherent Manifolds.
IEEE Trans. Inf. Theory, 2012

Multi-robot target verification with reachability constraints.
Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics, 2012

Multi-objective sensor-based replanning for a car-like robot.
Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics, 2012

Near-isometric linear embeddings of manifolds.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

SPIN: Iterative signal recovery on incoherent manifolds.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2011
Sampling and Recovery of Pulse Streams.
IEEE Trans. Signal Process., 2011

Go with the flow: Optical flow-based transport operators for image manifolds.
Proceedings of the 49th Annual Allerton Conference on Communication, 2011

2010
Model-based compressive sensing.
IEEE Trans. Inf. Theory, 2010

Joint Manifolds for Data Fusion.
IEEE Trans. Image Process., 2010

Texas Hold 'Em algorithms for distributed compressive sensing.
Proceedings of the IEEE International Conference on Acoustics, 2010

Compressive sensing of a superposition of pulses.
Proceedings of the IEEE International Conference on Acoustics, 2010

High Dimensional Data Fusion via Joint Manifold Learning.
Proceedings of the Manifold Learning and Its Applications, 2010

2009
A Theoretical Analysis of Joint Manifolds
CoRR, 2009

Recovery of compressible signals in unions of subspaces.
Proceedings of the 43rd Annual Conference on Information Sciences and Systems, 2009

Compressive sensing of streams of pulses.
Proceedings of the 47th Annual Allerton Conference on Communication, 2009

2008
Sparse Signal Recovery Using Markov Random Fields.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Random Projections for Manifold Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007


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