3 citations · 7 across the 11 of their papers we have counts for
13 papers
Multigroup Fairness and Omniprediction: Separations and Equivalences
Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade +3
Omniprediction is a learning guarantee which requires a single predictor to be competitive relative to the best hypothesis from a benchmark class for any loss chosen from a family…
Differentially Private Verification of Distribution Properties
Elbert Du, Cynthia Dwork, Pranay Tankala +1
A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of verifying properties of distr…
The Importance of Being Smoothly Calibrated
Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar +1
Recent work has highlighted the centrality of smooth calibration [Kakade and Foster, 2008] as a robust measure of calibration error. We generalize, unify, and extend previous resul…
Efficient Calibration for Decision Making
Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar +1
A decision-theoretic characterization of perfect calibration is that an agent seeking to minimize a proper loss in expectation cannot improve their outcome by post-processing a per…
Efficient and Private Property Testing via Indistinguishability
Cynthia Dwork, Pranay Tankala
Given a small random sample of -bit strings labeled by an unknown Boolean function, which properties of this function can be tested computationally efficiently? We show an equiv…
Supersimulators
Cynthia Dwork, Pranay Tankala
We prove that every randomized Boolean function admits a supersimulator: a randomized polynomial-size circuit whose output on random inputs cannot be efficiently distinguished from…
From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs
Cynthia Dwork, Chris Hays, Nicole Immorlica +2
Professional networks provide invaluable entree to opportunity through referrals and introductions. A rich literature shows they also serve to entrench and even exacerbate a status…
Differentially Private Learning Beyond the Classical Dimensionality Regime
Cynthia Dwork, Pranay Tankala, Linjun Zhang
We initiate the study of differentially private learning in the proportional dimensionality regime, in which the number of data samples and problem dimension approach infin…
From Pseudorandomness to Multi-Group Fairness and Back
Cynthia Dwork, Daniel Lee, Huijia Lin +1
We identify and explore connections between the recent literature on multi-group fairness for prediction algorithms and the pseudorandomness notions of leakage-resilience and graph…
Privately Estimating a Gaussian: Efficient, Robust and Optimal
Daniel Alabi, Pravesh K. Kothari, Pranay Tankala +2
In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on…
Weighed l1 on the simplex: Compressive sensing meets locality
Abiy Tasissa, Pranay Tankala, Demba Ba
Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks. The standard sett…
On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala +2
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from g…
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
Pranay Tankala, Abiy Tasissa, James M. Murphy +1
We propose K-Deep Simplex(KDS) which, given a set of data points, learns a dictionary comprising synthetic landmarks, along with representation coefficients supported on a simplex.…