
NonExhaustive, Overlapping CoClustering: An Extended Analysis
The goal of coclustering is to simultaneously identify a clustering of ...
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Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
The architecture of Transformer is based entirely on selfattention, and...
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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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AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Deep neural networks have yielded superior performance in many applicati...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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The Limitations of Adversarial Training and the BlindSpot Attack
The adversarial training procedure proposed by Madry et al. (2018) is on...
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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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Nonlinear Inductive Matrix Completion based on Onelayer Neural Networks
The goal of a recommendation system is to predict the interest of a user...
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Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Vanishing and exploding gradients are two of the main obstacles in train...
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Learning Long Term Dependencies via Fourier Recurrent Units
It is a known fact that training recurrent neural networks for tasks tha...
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Learning Nonoverlapping Convolutional Neural Networks with Multiple Kernels
In this paper, we consider parameter recovery for nonoverlapping convol...
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Recovery Guarantees for Onehiddenlayer Neural Networks
In this paper, we consider regression problems with onehiddenlayer neu...
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Similarity Preserving Representation Learning for Time Series Analysis
A considerable amount of machine learning algorithms take instancefeatu...
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Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families
We present a novel kway highdimensional graphical model called the Gen...
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Extreme Stochastic Variational Inference: Distributed and Asynchronous
We propose extreme stochastic variational inference (ESVI), an asynchron...
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Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies
We develop Square Root Graphical Models (SQR), a novel class of parametr...
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Highdimensional Time Series Prediction with Missing Values
Highdimensional time series prediction is needed in applications as div...
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Optimal DecisionTheoretic Classification Using NonDecomposable Performance Metrics
We provide a general theoretical analysis of expected outofsample util...
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PU Learning for Matrix Completion
In this paper, we consider the matrix completion problem when the observ...
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Proximal QuasiNewton for Computationally Intensive L1regularized Mestimators
We consider the class of optimization problems arising from computationa...
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Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
The L1regularized Gaussian maximum likelihood estimator (MLE) has been ...
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Provable Inductive Matrix Completion
Consider a movie recommendation system where apart from the ratings info...
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Orthogonal Matching Pursuit with Replacement
In this paper, we consider the problem of compressed sensing where the g...
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Metric and Kernel Learning using a Linear Transformation
Metric and kernel learning are important in several machine learning app...
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Inderjit S. Dhillon
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Gottesman Family Centennial Professor at UT Austin and Amazon Fellow