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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...
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05/24/2023
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Actor-critic (AC) methods are widely used in reinforcement learning (RL)...
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02/06/2023
Target-based Surrogates for Stochastic Optimization
We consider minimizing functions for which it is expensive to compute th...
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04/11/2022
Towards Painless Policy Optimization for Constrained MDPs
We study policy optimization in an infinite horizon, γ-discounted constr...
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10/21/2021
Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent
We design step-size schemes that make stochastic gradient descent (SGD) ...
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02/18/2021
SVRG Meets AdaGrad: Painless Variance Reduction
Variance reduction (VR) methods for finite-sum minimization typically re...
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11/23/2020
Geometry-Aware Universal Mirror-Prox
Mirror-prox (MP) is a well-known algorithm to solve variational inequali...
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06/08/2019
Reducing the variance in online optimization by transporting past gradients
Most stochastic optimization methods use gradients once before discardin...
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03/10/2019
Manifold Preserving Adversarial Learning
How to generate semantically meaningful and structurally sound adversari...
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07/06/2018
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
Unsupervised domain adaptation techniques have been successful for a wid...
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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...
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11/05/2015
Stop Wasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance ...
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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...
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04/16/2015