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08/11/2023
Private Distribution Learning with Public Data: The View from Sample Compression
We study the problem of private distribution learning with access to pub...
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04/18/2023
Impossibility of Characterizing Distribution Learning – a simple solution to a long-standing problem
We consider the long-standing question of finding a parameter of a class...
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02/08/2023
On Computable Online Learning
We initiate a study of computable online (c-online) learning, which we a...
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07/07/2021
Impossibility results for fair representations
With the growing awareness to fairness in machine learning and the reali...
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10/26/2020
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
To date, there has been no formal study of the statistical cost of inter...
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05/28/2019
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled a...
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10/10/2018
Semi-supervised clustering for de-duplication
Data de-duplication is the task of detecting multiple records that corre...
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05/22/2018
Clustering - What Both Theoreticians and Practitioners are Doing Wrong
Unsupervised learning is widely recognized as one of the most important ...
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02/23/2018
Empirical Risk Minimization under Fairness Constraints
We address the problem of algorithmic fairness: ensuring that sensitive ...
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10/14/2017
Agnostic Distribution Learning via Compression
We study sample-efficient distribution learning, where a learner is give...
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06/08/2016
Clustering with Same-Cluster Queries
We propose a framework for Semi-Supervised Active Clustering framework (...
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02/21/2016
Multi-task and Lifelong Learning of Kernels
We consider a problem of learning kernels for use in SVM classification ...
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10/19/2015
Clustering is Easy When ....What?
It is well known that most of the common clustering objectives are NP-ha...
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06/19/2015
Representation Learning for Clustering: A Statistical Framework
We address the problem of communicating domain knowledge from a user to ...
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06/27/2012
