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Found 6 papers, 2 papers with code
Locally Optimal Descent for Dynamic Stepsize Scheduling
We introduce a novel dynamic learning-rate scheduling scheme grounded in theory with the goal of simplifying the manual and time-consuming tuning of schedules in practice.
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Recommender systems play an important role in many content platforms.
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
In the general non-convex smooth optimization setting, we give a simple and efficient algorithm that requires $O( \sigma^2/\epsilon^4 + \tau/\epsilon^2 )$ steps for finding an $\epsilon$-stationary point $x$, where $\tau$ is the \emph{average} delay $\smash{\frac{1}{T}\sum_{t=1}^T d_t}$ and $\sigma^2$ is the variance of the stochastic gradients.
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
We study the iteration complexity of stochastic gradient descent (SGD) for minimizing the gradient norm of smooth, possibly nonconvex functions.
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available.
Efficient First-order Methods for Convex Minimization: a Constructive Approach
We describe a novel constructive technique for devising efficient first-order methods for a wide range of large-scale convex minimization settings, including smooth, non-smooth, and strongly convex minimization.
Optimization and Control Numerical Analysis
