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Found 15 papers, 6 papers with code
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
Improving Text-to-Image Synthesis Using Contrastive Learning
To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.
Ranked #9 on
Text-to-Image Generation
on CUB
Learning to generalize Dispatching rules on the Job Shop Scheduling
Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.
MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids
Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability.
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions.
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gradient descent method (SGD).
Matrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints.
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches
This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.
Distributed Learning With Sparsified Gradient Differences
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
Learning to Control under Time-Varying Environment
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
Regularization of the policy updates for stabilizing Mean Field Games
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.
Efficient Conformal Prediction under Data Heterogeneity
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions.
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery.
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive.
