Alexandros G. Dimakis

research

∙ 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...

Giannis Daras, et al. ∙

research

∙ 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...

Giannis Daras, et al. ∙

research

∙ 12/05/2021

Deblurring via Stochastic Refinement

Image deblurring is an ill-posed problem with multiple plausible solutio...

Jay Whang, et al. ∙

research

∙ 10/14/2021

Inverse Problems Leveraging Pre-trained Contrastive Representations

We study a new family of inverse problems for recovering representations...

Sriram Ravula, et al. ∙

research

∙ 08/03/2021

Robust Compressed Sensing MRI with Deep Generative Priors

The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep gen...

Ajil Jalal, et al. ∙

research

∙ 07/06/2021

Provable Lipschitz Certification for Generative Models

We present a scalable technique for upper bounding the Lipschitz constan...

Matt Jordan, et al. ∙

research

∙ 06/23/2021

Fairness for Image Generation with Uncertain Sensitive Attributes

This work tackles the issue of fairness in the context of generative pro...

Ajil Jalal, et al. ∙

research

∙ 06/21/2021

Instance-Optimal Compressed Sensing via Posterior Sampling

We characterize the measurement complexity of compressed sensing of sign...

Ajil Jalal, et al. ∙

research

∙ 06/05/2021

Neural Distributed Source Coding

Distributed source coding is the task of encoding an input in the absenc...

Jay Whang, et al. ∙

research

∙ 02/15/2021

Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

We propose Intermediate Layer Optimization (ILO), a novel optimization a...

Giannis Daras, et al. ∙

research

∙ 12/15/2020

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied...

Nir Shlezinger, et al. ∙

research

∙ 10/11/2020

SMYRF: Efficient Attention using Asymmetric Clustering

We propose a novel type of balanced clustering algorithm to approximate ...

Giannis Daras, et al. ∙

research

∙ 06/16/2020

Robust compressed sensing of generative models

The goal of compressed sensing is to estimate a high dimensional vector ...

Ajil Jalal, et al. ∙

research

∙ 05/12/2020

Deep Learning Techniques for Inverse Problems in Imaging

Recent work in machine learning shows that deep neural networks can be u...

Gregory Ongie, et al. ∙

research

∙ 03/18/2020

Compressed Sensing with Invertible Generative Models and Dependent Noise

We study image inverse problems with invertible generative priors, speci...

Jay Whang, et al. ∙

research

∙ 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...

Matt Jordan, et al. ∙

research

∙ 02/26/2020

Conditional Sampling from Invertible Generative Models with Applications to Inverse Problems

We consider uncertainty aware compressive sensing when the prior distrib...

Erik M. Lindgren, et al. ∙

research

∙ 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...

Giannis Daras, et al. ∙

research

∙ 10/17/2019

Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls

Large-scale machine learning training suffers from two prior challenges,...

Jiacheng Zhuo, et al. ∙

research

∙ 10/15/2019

SGD Learns One-Layer Networks in WGANs

Generative adversarial networks (GANs) are a widely used framework for l...

Qi Lei, et al. ∙

research

∙ 09/04/2019

Learning Distributions Generated by One-Layer ReLU Networks

We consider the problem of estimating the parameters of a d-dimensional ...

Shanshan Wu, et al. ∙

research

∙ 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...

Qi Lei, et al. ∙

research

∙ 06/06/2019

Primal-Dual Block Frank-Wolfe

We propose a variant of the Frank-Wolfe algorithm for solving a class of...

Qi Lei, et al. ∙

research

∙ 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...

Sriram Ravula, et al. ∙

research

∙ 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...

Matt Jordan, et al. ∙

research

∙ 02/21/2019

Quantifying Perceptual Distortion of Adversarial Examples

Recent work has shown that additive threat models, which only permit the...

Matt Jordan, et al. ∙

research

∙ 12/01/2018

Discrete Attacks and Submodular Optimization with Applications to Text Classification

Adversarial examples are carefully constructed modifications to an input...

Qi Lei, et al. ∙

research

∙ 11/26/2018

Adversarial Video Compression Guided by Soft Edge Detection

We propose a video compression framework using conditional Generative Ad...

Sungsoo Kim, et al. ∙

research

∙ 10/28/2018

Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

We characterize the effectiveness of a natural and classic algorithm for...

Shanshan Wu, et al. ∙

research

∙ 10/28/2018

Experimental Design for Cost-Aware Learning of Causal Graphs

We consider the minimum cost intervention design problem: Given the esse...

Erik M. Lindgren, et al. ∙

research

∙ 07/26/2018

Entropic Latent Variable Discovery

We consider the problem of discovering the simplest latent variable that...

Murat Kocaoglu, et al. ∙

research

∙ 06/26/2018

The Sparse Recovery Autoencoder

Linear encoding of sparse vectors is widely popular, but is most commonl...

Shanshan Wu, et al. ∙

research

∙ 06/17/2018

Compressed Sensing with Deep Image Prior and Learned Regularization

We propose a novel method for compressed sensing recovery using untraine...

David Van Veen, et al. ∙

research

∙ 01/23/2018

From Centralized to Decentralized Coded Caching

We consider the problem of designing decentralized schemes for coded cac...

Yitao Chen, et al. ∙

research

∙ 12/26/2017

The Robust Manifold Defense: Adversarial Training using Generative Models

Deep neural networks are demonstrating excellent performance on several ...

Andrew Ilyas, et al. ∙

research

∙ 09/18/2017

Model-Powered Conditional Independence Test

We consider the problem of non-parametric Conditional Independence testi...

Rajat Sen, et al. ∙

research

∙ 09/06/2017

CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

We propose an adversarial training procedure for learning a causal impli...

Murat Kocaoglu, et al. ∙

research

∙ 07/12/2017

Gradient Coding from Cyclic MDS Codes and Expander Graphs

Gradient Descent, and its variants, are a popular method for solving emp...

Netanel Raviv, et al. ∙

research

∙ 03/09/2017

Compressed Sensing using Generative Models

The goal of compressed sensing is to estimate a vector from an underdete...

Ashish Bora, et al. ∙

research

∙ 03/08/2017

Scalable Greedy Feature Selection via Weak Submodularity

Greedy algorithms are widely used for problems in machine learning such ...

Rajiv Khanna, et al. ∙

research

∙ 03/08/2017

On Approximation Guarantees for Greedy Low Rank Optimization

We provide new approximation guarantees for greedy low rank matrix estim...

Rajiv Khanna, et al. ∙

research

∙ 03/08/2017

Leveraging Sparsity for Efficient Submodular Data Summarization

The facility location problem is widely used for summarizing large datas...

Erik M. Lindgren, et al. ∙

research

∙ 03/08/2017

Exact MAP Inference by Avoiding Fractional Vertices

Given a graphical model, one essential problem is MAP inference, that is...

Erik M. Lindgren, et al. ∙

research

∙ 03/08/2017

Sparse Quadratic Logistic Regression in Sub-quadratic Time

We consider support recovery in the quadratic logistic regression settin...

Karthikeyan Shanmugam, et al. ∙

research

∙ 03/08/2017

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

In many machine learning applications, it is important to explain the pr...

Ethan R. Elenberg, et al. ∙

research

∙ 03/08/2017

Cost-Optimal Learning of Causal Graphs

We consider the problem of learning a causal graph over a set of variabl...

Murat Kocaoglu, et al. ∙

research

∙ 01/28/2017

Entropic Causality and Greedy Minimum Entropy Coupling

We study the problem of identifying the causal relationship between two ...

Murat Kocaoglu, et al. ∙

research

∙ 01/10/2017

Identifying Best Interventions through Online Importance Sampling

Motivated by applications in computational advertising and systems biolo...

Rajat Sen, et al. ∙

research

∙ 11/12/2016

Entropic Causal Inference

We consider the problem of identifying the causal direction between two ...

Murat Kocaoglu, et al. ∙

research

∙ 10/21/2016

Single Pass PCA of Matrix Products

In this paper we present a new algorithm for computing a low rank approx...

Shanshan Wu, et al. ∙

Alexandros G. Dimakis

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