Reza Babanezhad | DeepAI

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∙ 05/25/2023

Fast Online Node Labeling for Very Large Graphs

This paper studies the online node classification problem under a transd...

0 Baojian Zhou, et al. ∙

∙ 05/24/2023

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Actor-critic (AC) methods are widely used in reinforcement learning (RL)...

0 Sharan Vaswani, et al. ∙

∙ 02/06/2023

Target-based Surrogates for Stochastic Optimization

We consider minimizing functions for which it is expensive to compute th...

0 Jonathan Wilder Lavington, et al. ∙

∙ 04/11/2022

Towards Painless Policy Optimization for Constrained MDPs

We study policy optimization in an infinite horizon, γ-discounted constr...

2 Arushi Jain, et al. ∙

∙ 10/21/2021

Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent

We design step-size schemes that make stochastic gradient descent (SGD) ...

3 Sharan Vaswani, et al. ∙

∙ 02/18/2021

SVRG Meets AdaGrad: Painless Variance Reduction

Variance reduction (VR) methods for finite-sum minimization typically re...

4 Benjamin Dubois-Taine, et al. ∙

∙ 11/23/2020

Geometry-Aware Universal Mirror-Prox

Mirror-prox (MP) is a well-known algorithm to solve variational inequali...

0 Reza Babanezhad, et al. ∙

∙ 06/08/2019

Reducing the variance in online optimization by transporting past gradients

Most stochastic optimization methods use gradients once before discardin...

0 Sébastien M. R. Arnold, et al. ∙

∙ 03/10/2019

Manifold Preserving Adversarial Learning

How to generate semantically meaningful and structurally sound adversari...

14 Ousmane Amadou Dia, et al. ∙

∙ 07/06/2018

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning

Unsupervised domain adaptation techniques have been successful for a wid...

1 Issam Laradji, et al. ∙

∙ 03/01/2018

A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage

Standard collaborative filtering approaches for top-N recommendation are...

0 Zainab Zolaktaf, et al. ∙

∙ 11/05/2015

Stop Wasting My Gradients: Practical SVRG

We present and analyze several strategies for improving the performance ...

0 Reza Babanezhad, et al. ∙

∙ 10/31/2015

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions

Several recent works have explored stochastic gradient methods for varia...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 04/16/2015

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

We apply stochastic average gradient (SAG) algorithms for training condi...

1 Mark Schmidt, et al. ∙

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