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Found 9 papers, 0 papers with code
Distributed Online Optimization over a Heterogeneous Network
In distributed online optimization over a computing network with heterogeneous nodes, slow nodes can adversely affect the progress of fast nodes, leading to drastic slowdown of the overall convergence process.
Improving Knowledge Distillation with Teacher's Explanation
The teacher in KD is a black-box model, imparting knowledge to the student only through its predictions.
Generative Adversarial Classification Network with Application to Network Traffic Classification
Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values.
Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions
We first show that under relative smoothness, the dynamic regret has an upper bound based on the path length and functional variation.
Gradient and Projection Free Distributed Online Min-Max Resource Optimization
We consider distributed online min-max resource allocation with a set of parallel agents and a parameter server.
Delay-Tolerant Constrained OCO with Application to Network Resource Allocation
We consider online convex optimization (OCO) with multi-slot feedback delay, where an agent makes a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that are possibly time-varying.
Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing
At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of the flow to make a decision.
On the Generalization of Stochastic Gradient Descent with Momentum
While momentum-based methods, in conjunction with stochastic gradient descent (SGD), are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.
On the Generalization of Stochastic Gradient Descent with Momentum
While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.
