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07/02/2023
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
We present the first framework to solve linear inverse problems leveragi...
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06/06/2023
One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers
A key problem when modeling signal integrity for passive filters and int...
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06/05/2023
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Diffusion-based generative models have been used as powerful priors for ...
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05/30/2023
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
We present the first diffusion-based framework that can learn an unknown...
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03/06/2023
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers
We develop a framework for non-asymptotic analysis of deterministic samp...
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02/17/2023
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
Imperfect score-matching leads to a shift between the training and the s...
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11/30/2022
Multiresolution Textual Inversion
We extend Textual Inversion to learn pseudo-words that represent a conce...
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10/20/2022
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
We find a surprising connection between multitask learning and robustnes...
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10/14/2022
Zonotope Domains for Lagrangian Neural Network Verification
Neural network verification aims to provide provable bounds for the outp...
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09/12/2022
Soft Diffusion: Score Matching for General Corruptions
We define a broader family of corruption processes that generalizes prev...
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06/18/2022
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems
We prove fast mixing and characterize the stationary distribution of the...
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06/01/2022
Discovering the Hidden Vocabulary of DALLE-2
We discover that DALLE-2 seems to have a hidden vocabulary that can be u...
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12/05/2021
Deblurring via Stochastic Refinement
Image deblurring is an ill-posed problem with multiple plausible solutio...
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10/14/2021
Inverse Problems Leveraging Pre-trained Contrastive Representations
We study a new family of inverse problems for recovering representations...
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08/03/2021
Robust Compressed Sensing MRI with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep gen...
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07/06/2021
Provable Lipschitz Certification for Generative Models
We present a scalable technique for upper bounding the Lipschitz constan...
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06/23/2021
Fairness for Image Generation with Uncertain Sensitive Attributes
This work tackles the issue of fairness in the context of generative pro...
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06/21/2021
Instance-Optimal Compressed Sensing via Posterior Sampling
We characterize the measurement complexity of compressed sensing of sign...
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06/05/2021
Neural Distributed Source Coding
Distributed source coding is the task of encoding an input in the absenc...
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02/15/2021
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
We propose Intermediate Layer Optimization (ILO), a novel optimization a...
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12/15/2020
Model-Based Deep Learning
Signal processing, communications, and control have traditionally relied...
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10/11/2020
SMYRF: Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate ...
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06/16/2020
Robust compressed sensing of generative models
The goal of compressed sensing is to estimate a high dimensional vector ...
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05/12/2020
Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be u...
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03/18/2020
Compressed Sensing with Invertible Generative Models and Dependent Noise
We study image inverse problems with invertible generative priors, speci...
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03/02/2020
Exactly Computing the Local Lipschitz Constant of ReLU Networks
The Lipschitz constant of a neural network is a useful metric for provab...
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02/26/2020
Conditional Sampling from Invertible Generative Models with Applications to Inverse Problems
We consider uncertainty aware compressive sensing when the prior distrib...
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11/27/2019
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimen...
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10/17/2019
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Large-scale machine learning training suffers from two prior challenges,...
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10/15/2019
SGD Learns One-Layer Networks in WGANs
Generative adversarial networks (GANs) are a widely used framework for l...
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09/04/2019
Learning Distributions Generated by One-Layer ReLU Networks
We consider the problem of estimating the parameters of a d-dimensional ...
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06/18/2019
Inverting Deep Generative models, One layer at a time
We study the problem of inverting a deep generative model with ReLU acti...
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06/06/2019
Primal-Dual Block Frank-Wolfe
We propose a variant of the Frank-Wolfe algorithm for solving a class of...
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04/18/2019
One-dimensional Deep Image Prior for Time Series Inverse Problems
We extend the Deep Image Prior (DIP) framework to one-dimensional signal...
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03/20/2019
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
We propose a novel method for computing exact pointwise robustness of de...
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02/21/2019
Quantifying Perceptual Distortion of Adversarial Examples
Recent work has shown that additive threat models, which only permit the...
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12/01/2018
Discrete Attacks and Submodular Optimization with Applications to Text Classification
Adversarial examples are carefully constructed modifications to an input...
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11/26/2018
Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative Ad...
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10/28/2018
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
We characterize the effectiveness of a natural and classic algorithm for...
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10/28/2018
Experimental Design for Cost-Aware Learning of Causal Graphs
We consider the minimum cost intervention design problem: Given the esse...
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07/26/2018
Entropic Latent Variable Discovery
We consider the problem of discovering the simplest latent variable that...
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06/26/2018
The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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06/17/2018
Compressed Sensing with Deep Image Prior and Learned Regularization
We propose a novel method for compressed sensing recovery using untraine...
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01/23/2018
From Centralized to Decentralized Coded Caching
We consider the problem of designing decentralized schemes for coded cac...
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12/26/2017
The Robust Manifold Defense: Adversarial Training using Generative Models
Deep neural networks are demonstrating excellent performance on several ...
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09/18/2017
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testi...
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09/06/2017
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
We propose an adversarial training procedure for learning a causal impli...
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07/12/2017
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Gradient Descent, and its variants, are a popular method for solving emp...
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03/09/2017
Compressed Sensing using Generative Models
The goal of compressed sensing is to estimate a vector from an underdete...
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03/08/2017
