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Found 16 papers, 5 papers with code
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms
Stochastic variance reduced methods have shown strong performance in solving finite-sum problems.
FedVLN: Privacy-preserving Federated Vision-and-Language Navigation
Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world.
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization
We show that stochastic acceleration can be achieved under the perturbed iterate framework (Mania et al., 2017) in asynchronous lock-free optimization, which leads to the optimal incremental gradient complexity for finite-sum objectives.
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available.
Local Reweighting for Adversarial Training
However, when tested on attacks different from the given attack simulated in training, the robustness may drop significantly (e. g., even worse than no reweighting).
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
In convex optimization, the problem of finding near-stationary points has not been adequately studied yet, unlike other optimality measures such as the function value.
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates
Specifically, instead of tackling the original objective directly, we construct a shifted objective function that has the same minimizer as the original objective and encodes both the smoothness and strong convexity of the original objective in an interpolation condition.
Convolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment.
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.
Amortized Nesterov's Momentum: Robust and Lightweight Momentum for Deep Learning
Stochastic Gradient Descent (SGD) with Nesterov's momentum is a widely used optimizer in deep learning, which is observed to have excellent generalization performance.
Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including $L_2$-ALSH, Sign-ALSH and Simple-LSH have been proposed.
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick.
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Recent years have witnessed exciting progress in the study of stochastic variance reduced gradient methods (e. g., SVRG, SAGA), their accelerated variants (e. g, Katyusha) and their extensions in many different settings (e. g., online, sparse, asynchronous, distributed).
Direct Acceleration of SAGA using Sampled Negative Momentum
Variance reduction is a simple and effective technique that accelerates convex (or non-convex) stochastic optimization.
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD).
