Search Results for author:
Found 10 papers, 2 papers with code
Version age-based client scheduling policy for federated learning
Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL.
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework.
Learning Mutually Informed Representations for Characters and Subwords
In this paper, we introduce the entanglement model, aiming to combine character and subword language models.
Branch & Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints
Combining machine learning and constrained optimization, Predict+Optimize tackles optimization problems containing parameters that are unknown at the time of solving.
Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints
First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints.
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving.
Reinforcement Learning for Predict+Optimize
Predict+Optimize (P+O) is a machine learning framework for optimization problems with unknown parameters.
"Drunk Man" Saves Our Lives: Route Planning by a Biased Random Walk Mode
Based on the hurricane striking Puerto Rico in 2017, we developed a transportable disaster response system "DroneGo" featuring a drone fleet capable of delivering the medical package and videoing roads.
Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition
Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture.
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
Such attacks are successful mainly because of the poor representation power of the naive Bayes (NB) model, with only a single (component) density to represent spam (plus a possible attack).
