Alexandros G. Dimakis

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The University of Texas at Austin

Associate Professor, ECE University of Texas at Austin

SGD Learns One-Layer Networks in WGANs

Generative adversarial networks (GANs) are a widely used framework for l...
10/15/2019 ∙ by Qi Lei, et al. ∙ 44 ∙ share

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Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models

We introduce a new local sparse attention layer that preserves two-dimen...
11/27/2019 ∙ by Giannis Daras, et al. ∙ 41 ∙ share

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Exactly Computing the Local Lipschitz Constant of ReLU Networks

The Lipschitz constant of a neural network is a useful metric for provab...
03/02/2020 ∙ by Matt Jordan, et al. ∙ 24 ∙ share

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Inverting Deep Generative models, One layer at a time

We study the problem of inverting a deep generative model with ReLU acti...
06/18/2019 ∙ by Qi Lei, et al. ∙ 23 ∙ share

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Learning Distributions Generated by One-Layer ReLU Networks

We consider the problem of estimating the parameters of a d-dimensional ...
09/04/2019 ∙ by Shanshan Wu, et al. ∙ 22 ∙ share

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One-dimensional Deep Image Prior for Time Series Inverse Problems

We extend the Deep Image Prior (DIP) framework to one-dimensional signal...
04/18/2019 ∙ by Sriram Ravula, et al. ∙ 18 ∙ share

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

Large-scale machine learning training suffers from two prior challenges,...
10/17/2019 ∙ by Jiacheng Zhuo, et al. ∙ 17 ∙ share

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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...
03/20/2019 ∙ by Matt Jordan, et al. ∙ 16 ∙ share

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Adversarial Video Compression Guided by Soft Edge Detection

We propose a video compression framework using conditional Generative Ad...
11/26/2018 ∙ by Sungsoo Kim, et al. ∙ 12 ∙ share

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Compressed Sensing with Invertible Generative Models and Dependent Noise

We study image inverse problems with invertible generative priors, speci...
03/18/2020 ∙ by Jay Whang, et al. ∙ 11 ∙ share

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Deep Learning Techniques for Inverse Problems in Imaging

Recent work in machine learning shows that deep neural networks can be u...
05/12/2020 ∙ by Gregory Ongie, et al. ∙ 11 ∙ share

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Compressed Sensing with Deep Image Prior and Learned Regularization

We propose a novel method for compressed sensing recovery using untraine...
06/17/2018 ∙ by David Van Veen, et al. ∙ 10 ∙ share

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Quantifying Perceptual Distortion of Adversarial Examples

Recent work has shown that additive threat models, which only permit the...
02/21/2019 ∙ by Matt Jordan, et al. ∙ 10 ∙ share

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Conditional Sampling from Invertible Generative Models with Applications to Inverse Problems

We consider uncertainty aware compressive sensing when the prior distrib...
02/26/2020 ∙ by Erik M. Lindgren, et al. ∙ 10 ∙ share

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The Sparse Recovery Autoencoder

Linear encoding of sparse vectors is widely popular, but is most commonl...
06/26/2018 ∙ by Shanshan Wu, et al. ∙ 6 ∙ share

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Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

We characterize the effectiveness of a natural and classic algorithm for...
10/28/2018 ∙ by Shanshan Wu, et al. ∙ 6 ∙ share

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Experimental Design for Cost-Aware Learning of Causal Graphs

We consider the minimum cost intervention design problem: Given the esse...
10/28/2018 ∙ by Erik M. Lindgren, et al. ∙ 6 ∙ share

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Entropic Latent Variable Discovery

We consider the problem of discovering the simplest latent variable that...
07/26/2018 ∙ by Murat Kocaoglu, et al. ∙ 2 ∙ share

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Primal-Dual Block Frank-Wolfe

We propose a variant of the Frank-Wolfe algorithm for solving a class of...
06/06/2019 ∙ by Qi Lei, et al. ∙ 1 ∙ share

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Model-Powered Conditional Independence Test

We consider the problem of non-parametric Conditional Independence testi...
09/18/2017 ∙ by Rajat Sen, et al. ∙ 0 ∙ share

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CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

We propose an adversarial training procedure for learning a causal impli...
09/06/2017 ∙ by Murat Kocaoglu, et al. ∙ 0 ∙ share

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Gradient Coding from Cyclic MDS Codes and Expander Graphs

Gradient Descent, and its variants, are a popular method for solving emp...
07/12/2017 ∙ by Netanel Raviv, et al. ∙ 0 ∙ share

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Compressed Sensing using Generative Models

The goal of compressed sensing is to estimate a vector from an underdete...
03/09/2017 ∙ by Ashish Bora, et al. ∙ 0 ∙ share

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Scalable Greedy Feature Selection via Weak Submodularity

Greedy algorithms are widely used for problems in machine learning such ...
03/08/2017 ∙ by Rajiv Khanna, et al. ∙ 0 ∙ share

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On Approximation Guarantees for Greedy Low Rank Optimization

We provide new approximation guarantees for greedy low rank matrix estim...
03/08/2017 ∙ by Rajiv Khanna, et al. ∙ 0 ∙ share

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Leveraging Sparsity for Efficient Submodular Data Summarization

The facility location problem is widely used for summarizing large datas...
03/08/2017 ∙ by Erik M. Lindgren, et al. ∙ 0 ∙ share

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Exact MAP Inference by Avoiding Fractional Vertices

Given a graphical model, one essential problem is MAP inference, that is...
03/08/2017 ∙ by Erik M. Lindgren, et al. ∙ 0 ∙ share

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Sparse Quadratic Logistic Regression in Sub-quadratic Time

We consider support recovery in the quadratic logistic regression settin...
03/08/2017 ∙ by Karthikeyan Shanmugam, et al. ∙ 0 ∙ share

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Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

In many machine learning applications, it is important to explain the pr...
03/08/2017 ∙ by Ethan R. Elenberg, et al. ∙ 0 ∙ share

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Cost-Optimal Learning of Causal Graphs

We consider the problem of learning a causal graph over a set of variabl...
03/08/2017 ∙ by Murat Kocaoglu, et al. ∙ 0 ∙ share

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Entropic Causality and Greedy Minimum Entropy Coupling

We study the problem of identifying the causal relationship between two ...
01/28/2017 ∙ by Murat Kocaoglu, et al. ∙ 0 ∙ share

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Identifying Best Interventions through Online Importance Sampling

Motivated by applications in computational advertising and systems biolo...
01/10/2017 ∙ by Rajat Sen, et al. ∙ 0 ∙ share

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Entropic Causal Inference

We consider the problem of identifying the causal direction between two ...
11/12/2016 ∙ by Murat Kocaoglu, et al. ∙ 0 ∙ share

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Single Pass PCA of Matrix Products

In this paper we present a new algorithm for computing a low rank approx...
10/21/2016 ∙ by Shanshan Wu, et al. ∙ 0 ∙ share

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Contextual Bandits with Latent Confounders: An NMF Approach

Motivated by online recommendation and advertising systems, we consider ...
06/01/2016 ∙ by Rajat Sen, et al. ∙ 0 ∙ share

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Bipartite Correlation Clustering -- Maximizing Agreements

In Bipartite Correlation Clustering (BCC) we are given a complete bipart...
03/09/2016 ∙ by Megasthenis Asteris, et al. ∙ 0 ∙ share

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Learning Causal Graphs with Small Interventions

We consider the problem of learning causal networks with interventions, ...
10/30/2015 ∙ by Karthikeyan Shanmugam, et al. ∙ 0 ∙ share

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Sparse PCA via Bipartite Matchings

We consider the following multi-component sparse PCA problem: given a se...
08/04/2015 ∙ by Megasthenis Asteris, et al. ∙ 0 ∙ share

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Stay on path: PCA along graph paths

We introduce a variant of (sparse) PCA in which the set of feasible supp...
06/08/2015 ∙ by Megasthenis Asteris, et al. ∙ 0 ∙ share

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Sparse PCA through Low-rank Approximations

We introduce a novel algorithm that computes the k-sparse principal comp...
03/03/2013 ∙ by Dimitris S. Papailiopoulos, et al. ∙ 0 ∙ share

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The Robust Manifold Defense: Adversarial Training using Generative Models

Deep neural networks are demonstrating excellent performance on several ...
12/26/2017 ∙ by Andrew Ilyas, et al. ∙ 0 ∙ share

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From Centralized to Decentralized Coded Caching

We consider the problem of designing decentralized schemes for coded cac...
01/23/2018 ∙ by Yitao Chen, et al. ∙ 0 ∙ share

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Discrete Attacks and Submodular Optimization with Applications to Text Classification

Adversarial examples are carefully constructed modifications to an input...
12/01/2018 ∙ by Qi Lei, et al. ∙ 0 ∙ share

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Alexandros G. Dimakis

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