
Provable Lipschitz Certification for Generative Models
We present a scalable technique for upper bounding the Lipschitz constan...
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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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InstanceOptimal Compressed Sensing via Posterior Sampling
We characterize the measurement complexity of compressed sensing of sign...
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Neural Distributed Source Coding
Distributed source coding is the task of encoding an input in the absenc...
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Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
We propose Intermediate Layer Optimization (ILO), a novel optimization a...
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ModelBased Deep Learning
Signal processing, communications, and control have traditionally relied...
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SMYRF: Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate ...
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Robust compressed sensing of generative models
The goal of compressed sensing is to estimate a high dimensional vector ...
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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...
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Compressed Sensing with Invertible Generative Models and Dependent Noise
We study image inverse problems with invertible generative priors, speci...
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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...
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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...
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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 twodimen...
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CommunicationEfficient Asynchronous Stochastic FrankWolfe over Nuclearnorm Balls
Largescale machine learning training suffers from two prior challenges,...
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SGD Learns OneLayer Networks in WGANs
Generative adversarial networks (GANs) are a widely used framework for l...
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Learning Distributions Generated by OneLayer ReLU Networks
We consider the problem of estimating the parameters of a ddimensional ...
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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...
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PrimalDual Block FrankWolfe
We propose a variant of the FrankWolfe algorithm for solving a class of...
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Onedimensional Deep Image Prior for Time Series Inverse Problems
We extend the Deep Image Prior (DIP) framework to onedimensional signal...
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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...
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Quantifying Perceptual Distortion of Adversarial Examples
Recent work has shown that additive threat models, which only permit the...
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Discrete Attacks and Submodular Optimization with Applications to Text Classification
Adversarial examples are carefully constructed modifications to an input...
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Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative Ad...
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Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
We characterize the effectiveness of a natural and classic algorithm for...
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Experimental Design for CostAware Learning of Causal Graphs
We consider the minimum cost intervention design problem: Given the esse...
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Entropic Latent Variable Discovery
We consider the problem of discovering the simplest latent variable that...
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The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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Compressed Sensing with Deep Image Prior and Learned Regularization
We propose a novel method for compressed sensing recovery using untraine...
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From Centralized to Decentralized Coded Caching
We consider the problem of designing decentralized schemes for coded cac...
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The Robust Manifold Defense: Adversarial Training using Generative Models
Deep neural networks are demonstrating excellent performance on several ...
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ModelPowered Conditional Independence Test
We consider the problem of nonparametric Conditional Independence testi...
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CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
We propose an adversarial training procedure for learning a causal impli...
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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...
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Compressed Sensing using Generative Models
The goal of compressed sensing is to estimate a vector from an underdete...
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Scalable Greedy Feature Selection via Weak Submodularity
Greedy algorithms are widely used for problems in machine learning such ...
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On Approximation Guarantees for Greedy Low Rank Optimization
We provide new approximation guarantees for greedy low rank matrix estim...
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Leveraging Sparsity for Efficient Submodular Data Summarization
The facility location problem is widely used for summarizing large datas...
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Exact MAP Inference by Avoiding Fractional Vertices
Given a graphical model, one essential problem is MAP inference, that is...
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Sparse Quadratic Logistic Regression in Subquadratic Time
We consider support recovery in the quadratic logistic regression settin...
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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...
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CostOptimal Learning of Causal Graphs
We consider the problem of learning a causal graph over a set of variabl...
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Entropic Causality and Greedy Minimum Entropy Coupling
We study the problem of identifying the causal relationship between two ...
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Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
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Entropic Causal Inference
We consider the problem of identifying the causal direction between two ...
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Single Pass PCA of Matrix Products
In this paper we present a new algorithm for computing a low rank approx...
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Contextual Bandits with Latent Confounders: An NMF Approach
Motivated by online recommendation and advertising systems, we consider ...
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Bipartite Correlation Clustering  Maximizing Agreements
In Bipartite Correlation Clustering (BCC) we are given a complete bipart...
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Learning Causal Graphs with Small Interventions
We consider the problem of learning causal networks with interventions, ...
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Sparse PCA via Bipartite Matchings
We consider the following multicomponent sparse PCA problem: given a se...
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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...
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Alexandros G. Dimakis
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Associate Professor, ECE University of Texas at Austin