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09/07/2023
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
We study the problem of estimating mixtures of Gaussians under the const...
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05/28/2023
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences
We consider the class of noisy multi-layered sigmoid recurrent neural ne...
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03/07/2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
We study the problem of privately estimating the parameters of d-dimensi...
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06/14/2022
Benefits of Additive Noise in Composing Classes with Bounded Capacity
We observe that given two (compatible) classes of functions ℱ and ℋ with...
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03/02/2022
Adversarially Robust Learning with Tolerance
We study the problem of tolerant adversarial PAC learning with respect t...
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11/22/2021
Private and polynomial time algorithms for learning Gaussians and beyond
We present a fairly general framework for reducing (ε, δ) differentially...
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06/03/2021
Privately Learning Mixtures of Axis-Aligned Gaussians
We consider the problem of learning mixtures of Gaussians under the cons...
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10/19/2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
We provide sample complexity upper bounds for agnostically learning mult...
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06/30/2020
Black-box Certification and Learning under Adversarial Perturbations
We formally study the problem of classification under adversarial pertur...
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12/05/2019
On the Sample Complexity of Learning Sum-Product Networks
Sum-Product Networks (SPNs) can be regarded as a form of deep graphical ...
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01/11/2018
Some techniques in density estimation
Density estimation is an interdisciplinary topic at the intersection of ...
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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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06/19/2015
