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Found 44 papers, 10 papers with code
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
We observe that the performance of offline RL for the RRM problem depends critically on the behavior policy used for data collection, and further propose a novel offline RL solution that leverages heterogeneous datasets collected by different behavior policies.
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.
Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction
The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negativecurvature ratios, network densities, and network sizes (when larger than 500).
Torsion Graph Neural Networks
This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs.
Molecular geometric deep learning
This demonstrates the great potential of novel molecular representations beyond the de facto standard of covalent-bond-based molecular graphs.
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Then, a novel HetPEVI algorithm is proposed, which simultaneously considers the sample uncertainties from a finite number of data samples per data source and the source uncertainties due to a finite number of available data sources.
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the learning process.
On High-dimensional and Low-rank Tensor Bandits
To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank.
Reward Teaching for Federated Multi-armed Bandits
Rigorous analyses demonstrate that when facing clients with UCB1, TWL outperforms TAL in terms of the dependencies on sub-optimality gaps thanks to its adaptive design.
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection.
Accelerating Hybrid Federated Learning Convergence under Partial Participation
In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.
Federated Learning via Indirect Server-Client Communications
In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e. g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients.
Communication and Storage Efficient Federated Split Learning
Federated Split Learning (FSL) preserves the parallel model training principle of FL, with a reduced device computation requirement thanks to splitting the ML model between the server and clients.
Cross-Layer Federated Learning Optimization in MIMO Networks
The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices.
Random Orthogonalization for Federated Learning in Massive MIMO Systems
We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle.
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm.
On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting
In the special case of $m=2$, i. e., pairwise comparison, the resultant bound is tighter than that given by Shah et al., leading to a reduced gap between the error probability upper and lower bounds.
On Federated Learning with Energy Harvesting Clients
Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper.
Learning for Robust Combinatorial Optimization: Algorithm and Application
Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity than conventional solvers.
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmunicatioN -- that closes this gap.
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications.
Federated Linear Contextual Bandits
This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters.
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions
We study a new stochastic multi-player multi-armed bandits (MP-MAB) problem, where the reward distribution changes if a collision occurs on the arm.
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges
We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).
Federated Multi-armed Bandits with Personalization
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of FL with personalization.
Federated Multi-Armed Bandits
We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution.
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples
Does Federated Learning (FL) work when both uplink and downlink communications have errors?
Design and Analysis of Uplink and Downlink Communications for Federated Learning
Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous - a carefully designed quantization and transmission achieves more than 98% of the floating-point baseline accuracy with fewer than 10% of the baseline bandwidth, for majority of the experiments on both i. i. d.
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications
Instead of focusing on the hardness of multiple players, we introduce a new dimension of hardness, called attackability.
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex.
Stochastic Linear Contextual Bandits with Diverse Contexts
In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits.
Decentralized Multi-player Multi-armed Bandits with No Collision Information
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper.
Contextual Constrained Learning for Dose-Finding Clinical Trials
In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.
A Regression Approach to Certain Information Transmission Problems
A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information.
Online Learning with Diverse User Preferences
We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant.
Towards Optimal Power Control via Ensembling Deep Neural Networks
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel.
Cost-aware Cascading Bandits
For the online setting, we propose a Cost-aware Cas- cading Upper Confidence Bound (CC-UCB) algo- rithm, and show that the cumulative regret scales in O(log T ).
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access
Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i. e., reward-minus-cost).
Regional Multi-Armed Bandits
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter.
An Iterative BP-CNN Architecture for Channel Decoding
The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise.
