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Found 10 papers, 3 papers with code
Improving Computational Complexity in Statistical Models with Second-Order Information
This computational complexity is cheaper than that of the fixed step-size gradient descent algorithm, which is of the order $\mathcal{O}(n^{\tau})$ for some $\tau > 1$, to reach the same statistical radius.
On the computational and statistical complexity of over-parameterized matrix sensing
We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-parameterized matrix sensing.
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data to learn useful semantic representations.
Robust Structured Statistical Estimation via Conditional Gradient Type Methods
However, the existing CG type methods are not robust to data corruption.
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Large-scale non-convex sparsity-constrained problems have recently gained extensive attention.
Primal-Dual Block Generalized Frank-Wolfe
We propose a generalized variant of Frank-Wolfe algorithm for solving a class of sparse/low-rank optimization problems.
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown due to the straggling workers, and high communication costs.
Primal-Dual Block Frank-Wolfe
We propose a variant of the Frank-Wolfe algorithm for solving a class of sparse/low-rank optimization problems.
Bottom-up Object Detection by Grouping Extreme and Center Points
With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem.
Ranked #128 on
Object Detection
on COCO minival
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e. g., SVRG) has made exciting progress (e. g., linear convergence for strongly convex problems).
