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Found 14 papers, 4 papers with code
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms
We study the decentralized multi-player multi-armed bandits (MMAB) problem under a no-sensing setting, where each player receives only their own reward and obtains no information about collisions.
Delayed Random Partial Gradient Averaging for Federated Learning
Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to train a shared model collaboratively while preserving privacy.
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts.
Ranked #1 on
Time Series Forecasting
on Electricity (96)
Improving Autoregressive Training with Dynamic Oracles
Many tasks within NLP can be framed as sequential decision problems, ranging from sequence tagging to text generation.
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).
