
MixnMatch: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
This paper studies the problem of posthoc calibration of machine learni...
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Anomalous Instance Detection in Deep Learning: A Survey
Deep Learning (DL) is vulnerable to outofdistribution and adversarial ...
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Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
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Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
In the past few years, generative models like Generative Adversarial Net...
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Merlin: Enabling Machine LearningReady HPC Ensembles
With the growing complexity of computational and experimental facilities...
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Deep Probabilistic Kernels for SampleEfficient Learning
Gaussian Processes (GPs) with an appropriate kernel are known to provide...
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On the Design of Blackbox Adversarial Examples by Leveraging Gradientfree Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics wh...
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Generative Counterfactual Introspection for Explainable Deep Learning
In this work, we propose an introspection technique for deep neural netw...
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A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
This paper provides a general framework to study the effect of sampling ...
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Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
Material scientists are increasingly adopting the use of machine learnin...
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MRGAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), ...
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MimicGAN: CorruptionMimicking for Blind Image Recovery & Adversarial Defense
Solving inverse problems continues to be a central challenge in computer...
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Universal DecisionBased BlackBox Perturbations: Breaking SecurityThroughObscurity Defenses
We study the problem of finding a universal (imageagnostic) perturbatio...
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Universal Hardlabel BlackBox Perturbations: Breaking SecurityThroughObscurity Defenses
We study the problem of finding a universal (imageagnostic) perturbatio...
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Controlled Random Search Improves Sample Mining and HyperParameter Optimization
A common challenge in machine learning and related fields is the need to...
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ZerothOrder Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zerothorder (gradientfree) optimization acc...
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An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Solving inverse problems continues to be a challenge in a wide array of ...
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HumanMachine Inference Networks For Smart Decision Making: Opportunities and Challenges
The emerging paradigm of HumanMachine Inference Networks (HuMaINs) comb...
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A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
This paper proposes a new approach to construct high quality spacefilli...
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Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines
In this paper, we consider the problem of federated (or decentralized) l...
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Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
This paper considers the problem of high dimensional signal detection in...
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Consensus based Detection in the Presence of Data Falsification Attacks
This paper considers the problem of detection in distributed networks in...
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Bhavya Kailkhura
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