
Deformable DETR: Deformable Transformers for EndtoEnd Object Detection
DETR has been recently proposed to eliminate the need for many handdesi...
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Benign Overfitting and Noisy Features
Modern machine learning often operates in the regime where the number of...
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Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
This paper proposes SplitSGD, a new stochastic optimization algorithm wi...
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VLBERT: Pretraining of Generic VisualLinguistic Representations
We introduce a new pretrainable generic representation for visuallingu...
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Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
SLOPE is a relatively new convex optimization procedure for highdimensi...
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Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
With the widespread success of deep neural networks in science and techn...
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Statistical Inference for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
Stochastic gradient descent (SGD) is an immensely popular approach for o...
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Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
Modern statistical inference tasks often require iterative optimization ...
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When Does the First Spurious Variable Get Selected by Sequential Regression Procedures?
Applied statisticians use sequential regression procedures to produce a ...
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Private False Discovery Rate Control
We provide the first differentially private algorithms for controlling t...
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False Discoveries Occur Early on the Lasso Path
In regression settings where explanatory variables have very low correla...
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CommunicationEfficient False Discovery Rate Control via Knockoff Aggregation
The false discovery rate (FDR)the expected fraction of spurious disco...
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A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
We derive a secondorder ordinary differential equation (ODE) which is t...
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Weijie Su
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