
Nonstationary Online Regression
Online forecasting under a changing environment has been a problem of in...
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Modelspecific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and nonconvergence in app...
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Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
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Explicit Regularization of Stochastic Gradient Methods through Duality
We consider stochastic gradient methods under the interpolation regime w...
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Importance Sampling via Local Sensitivity
Given a loss function F:X→R^+ that can be written as the sum of losses o...
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Dual IV: A Single Stage Instrumental Variable Regression
We present a novel singlestage procedure for instrumental variable (IV)...
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Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variabl...
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A Differentially Private Kernel TwoSample Test
Kernel twosample testing is a useful statistical tool in determining wh...
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Regularized Kernel and Neural Sobolev Descent: Dynamic MMD Transport
We introduce Regularized Kernel and Neural Sobolev Descent for transport...
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SVRG meets SAGA: kSVRG  A Tale of Limited Memory
In recent years, many variance reduced algorithms for empirical risk min...
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Revisiting FirstOrder Convex Optimization Over Linear Spaces
Two popular examples of firstorder optimization methods over linear spa...
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Sobolev GAN
We propose a new Integral Probability Metric (IPM) between distributions...
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Screening Rules for Convex Problems
We propose a new framework for deriving screening rules for convex optim...
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Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets
The goal of domain adaptation is to adapt models learned on a source dom...
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Subspace Alignment Based Domain Adaptation for RCNN Detector
In this paper, we propose subspace alignment based domain adaptation of ...
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Mind the Gap: Subspace based Hierarchical Domain Adaptation
Domain adaptation techniques aim at adapting a classifier learnt on a so...
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Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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Anant Raj
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