
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Alongside the wellpublicized accomplishments of deep neural networks th...
read it

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Training and evaluation of fair classifiers is a challenging problem. Th...
read it

Adversarial Robustness Across Representation Spaces
Adversarial robustness corresponds to the susceptibility of deep neural ...
read it

Beyond Individual and Group Fairness
We present a new datadriven model of fairness that, unlike existing sta...
read it

On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of l...
read it

Adaptive Sampling to Reduce Disparate Performance
Existing methods for reducing disparate performance of a classifier acro...
read it

A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair cla...
read it

Estimating Principal Components under Adversarial Perturbations
Robustness is a key requirement for widespread deployment of machine lea...
read it

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Adversarial or test time robustness measures the susceptibility of a cla...
read it

Efficient active learning of sparse halfspaces with arbitrary bounded noise
In this work we study active learning of homogeneous ssparse halfspaces...
read it

A Deep Conditioning Treatment of Neural Networks
We study the role of depth in training randomly initialized overparamete...
read it

Adversarially Robust Low Dimensional Representations
Adversarial or test time robustness measures the susceptibility of a mac...
read it

On Robustness to Adversarial Examples and Polynomial Optimization
We study the design of computationally efficient algorithms with provabl...
read it

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information
Most approaches for ensuring or improving a model's fairness with respec...
read it

Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitio...
read it

Fair kCenter Clustering for Data Summarization
In data summarization we want to choose k prototypes in order to summari...
read it

BiluLinial stability, certified algorithms and the Independent Set problem
We study the notion of BiluLinial stability in the context of Independe...
read it

Towards Learning Sparsely Used Dictionaries with Arbitrary Supports
Dictionary learning is a popular approach for inferring a hidden basis o...
read it

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...
read it

Clustering SemiRandom Mixtures of Gaussians
Gaussian mixture models (GMM) are the most widely used statistical model...
read it

On some provably correct cases of variational inference for topic models
Variational inference is a very efficient and popular heuristic used in ...
read it

The Power of Localization for Efficiently Learning Linear Separators with Noise
We introduce a new approach for designing computationally efficient lear...
read it

Learning using Local Membership Queries
We introduce a new model of membership query (MQ) learning, where the le...
read it
Pranjal Awasthi
is this you? claim profile