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05/20/2023
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent
Algorithmic stability is an important notion that has proven powerful fo...
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03/30/2023
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
This paper deals with the problem of efficient sampling from a stochasti...
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03/16/2023
Variational Principles for Mirror Descent and Mirror Langevin Dynamics
Mirror descent, introduced by Nemirovski and Yudin in the 1970s, is a pr...
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01/27/2023
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
Heavy-tail phenomena in stochastic gradient descent (SGD) have been repo...
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06/09/2022
Explicit Regularization in Overparametrized Models via Noise Injection
Injecting noise within gradient descent has several desirable features. ...
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06/02/2022
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares
Recent studies have shown that heavy tails can emerge in stochastic opti...
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10/29/2021
Convergence of Uncertainty Sampling for Active Learning
Uncertainty sampling in active learning is heavily used in practice to r...
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11/13/2020
Non-stationary Online Regression
Online forecasting under a changing environment has been a problem of in...
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10/20/2020
Model-specific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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07/06/2020
Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and non-convergence in app...
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07/06/2020
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
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03/30/2020
Explicit Regularization of Stochastic Gradient Methods through Duality
We consider stochastic gradient methods under the interpolation regime w...
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11/04/2019
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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10/27/2019
Dual IV: A Single Stage Instrumental Variable Regression
We present a novel single-stage procedure for instrumental variable (IV)...
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03/06/2019
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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08/01/2018
A Differentially Private Kernel Two-Sample Test
Kernel two-sample testing is a useful statistical tool in determining wh...
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05/30/2018
Regularized Kernel and Neural Sobolev Descent: Dynamic MMD Transport
We introduce Regularized Kernel and Neural Sobolev Descent for transport...
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05/02/2018
SVRG meets SAGA: k-SVRG --- A Tale of Limited Memory
In recent years, many variance reduced algorithms for empirical risk min...
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03/26/2018
Revisiting First-Order Convex Optimization Over Linear Spaces
Two popular examples of first-order optimization methods over linear spa...
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11/14/2017
Sobolev GAN
We propose a new Integral Probability Metric (IPM) between distributions...
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09/23/2016
Screening Rules for Convex Problems
We propose a new framework for deriving screening rules for convex optim...
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03/26/2016
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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07/20/2015
Subspace Alignment Based Domain Adaptation for RCNN Detector
In this paper, we propose subspace alignment based domain adaptation of ...
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01/16/2015
Mind the Gap: Subspace based Hierarchical Domain Adaptation
Domain adaptation techniques aim at adapting a classifier learnt on a so...
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07/21/2014