
Learning and Generalization in Overparameterized Normalizing Flows
In supervised learning, it is known that overparameterized neural networ...
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Rawlsian Fair Adaptation of Deep Learning Classifiers
Groupfairness in classification aims for equality of a predictive utili...
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On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling
We consider the problem of subset selection for ℓ_p subspace approximati...
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The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Training datasets for machine learning often have some form of missingne...
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Ranking for Individual and Group Fairness Simultaneously
Search and recommendation systems, such as search engines, recruiting to...
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Subspace approximation with outliers
The subspace approximation problem with outliers, for given n points in ...
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How do SGD hyperparameters in natural training affect adversarial robustness?
Learning rate, batch size and momentum are three important hyperparamete...
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On Universalized Adversarial and Invariant Perturbations
Convolutional neural networks or standard CNNs (StdCNNs) are translation...
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Universalization of any adversarial attack using very few test examples
Deep learning models are known to be vulnerable not only to inputdepend...
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Invariance vs. Robustness of Neural Networks
We study the performance of neural network models on random geometric tr...
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Quantifying InfraMarginality and Its Tradeoff with Group Fairness
In critical decisionmaking scenarios, optimizing accuracy can lead to a...
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Clustering Perturbation Resilient Instances
Euclidean kmeans is a problem that is NPhard in the worstcase but oft...
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Fair and Diverse DPPbased Data Summarization
Sampling methods that choose a subset of the data proportional to its di...
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On the Complexity of Constrained Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models that arise...
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Amit Deshpande
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