
Efficient Algorithms for Learning Depth2 Neural Networks with General ReLU Activations
We present polynomial time and sample efficient algorithms for learning ...
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On the benefits of maximum likelihood estimation for Regression and Forecasting
We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
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Semisupervised Active Regression
Labelled data often comes at a high cost as it may require recruiting hu...
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Neural Active Learning with Performance Guarantees
We investigate the problem of active learning in the streaming setting i...
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A Finer Calibration Analysis for Adversarial Robustness
We present a more general analysis of Hcalibration for adversarially ro...
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Calibration and Consistency of Adversarial Surrogate Losses
Adversarial robustness is an increasingly critical property of classifie...
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A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Alongside the wellpublicized accomplishments of deep neural networks th...
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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Training and evaluation of fair classifiers is a challenging problem. Th...
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Adversarial Robustness Across Representation Spaces
Adversarial robustness corresponds to the susceptibility of deep neural ...
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Beyond Individual and Group Fairness
We present a new datadriven model of fairness that, unlike existing sta...
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On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of l...
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Adaptive Sampling to Reduce Disparate Performance
Existing methods for reducing disparate performance of a classifier acro...
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A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair cla...
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Estimating Principal Components under Adversarial Perturbations
Robustness is a key requirement for widespread deployment of machine lea...
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Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Adversarial or test time robustness measures the susceptibility of a cla...
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Efficient active learning of sparse halfspaces with arbitrary bounded noise
In this work we study active learning of homogeneous ssparse halfspaces...
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A Deep Conditioning Treatment of Neural Networks
We study the role of depth in training randomly initialized overparamete...
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Adversarially Robust Low Dimensional Representations
Adversarial or test time robustness measures the susceptibility of a mac...
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On Robustness to Adversarial Examples and Polynomial Optimization
We study the design of computationally efficient algorithms with provabl...
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Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information
Most approaches for ensuring or improving a model's fairness with respec...
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Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitio...
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Fair kCenter Clustering for Data Summarization
In data summarization we want to choose k prototypes in order to summari...
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BiluLinial stability, certified algorithms and the Independent Set problem
We study the notion of BiluLinial stability in the context of Independe...
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Towards Learning Sparsely Used Dictionaries with Arbitrary Supports
Dictionary learning is a popular approach for inferring a hidden basis o...
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Robust Vertex Enumeration for Convex Hulls in High Dimensions
Computation of the vertices of the convex hull of a set S of n points in...
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Clustering SemiRandom Mixtures of Gaussians
Gaussian mixture models (GMM) are the most widely used statistical model...
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On some provably correct cases of variational inference for topic models
Variational inference is a very efficient and popular heuristic used in ...
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The Power of Localization for Efficiently Learning Linear Separators with Noise
We introduce a new approach for designing computationally efficient lear...
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Learning using Local Membership Queries
We introduce a new model of membership query (MQ) learning, where the le...
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Pranjal Awasthi
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