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01/31/2023
The Power of External Memory in Increasing Predictive Model Capacity
One way of introducing sparsity into deep networks is by attaching an ex...
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01/30/2023
Alternating Updates for Efficient Transformers
It is well established that increasing scale in deep transformer network...
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08/08/2022
A Theoretical View on Sparsely Activated Networks
Deep and wide neural networks successfully fit very complex functions to...
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05/03/2022
Do More Negative Samples Necessarily Hurt in Contrastive Learning?
Recent investigations in noise contrastive estimation suggest, both empi...
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07/20/2021
Statistical Estimation from Dependent Data
We consider a general statistical estimation problem wherein binary labe...
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03/11/2021
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions
It is well established that training deep neural networks gives useful r...
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06/12/2020
Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional m...
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04/20/2020
Estimating Ising Models from One Sample
Given one sample X ∈{± 1}^n from an Ising model [X=x]∝(x^ J x/2), whose ...
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03/18/2020
Logistic-Regression with peer-group effects via inference in higher order Ising models
Spin glass models, such as the Sherrington-Kirkpatrick, Hopfield and Isi...
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06/21/2019
Learning from weakly dependent data under Dobrushin's condition
Statistical learning theory has largely focused on learning and generali...
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05/08/2019
Regression from Dependent Observations
The standard linear and logistic regression models assume that the respo...
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11/26/2018
HOGWILD!-Gibbs can be PanAccurate
Asynchronous Gibbs sampling has been recently shown to be fast-mixing an...
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10/03/2018
From Soft Classifiers to Hard Decisions: How fair can we be?
A popular methodology for building binary decision-making classifiers in...
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10/11/2017