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Found 31 papers, 7 papers with code
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming
Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions.
Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks
We study the problem of decentralized task offloading and load-balancing in a dense network with numerous devices and a set of edge servers.
Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems
We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production.
Budgeted Recommendation with Delayed Feedback
The delayed feedback of testing results, thus insufficient information for learning, degraded the efficiency of resource allocation.
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER).
Meta Learning in Bandits within Shared Affine Subspaces
We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits.
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world.
Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization
We then develop a model and solution based on the multi-objective multi-armed bandit theory to maximize the network coverage while minimizing the FL delay.
Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards
In our nonstationary environment, variations in the base arms' distributions, causal relationships between rewards, or both, change the reward generation process.
Online Learning with Costly Features in Non-stationary Environments
Maximizing long-term rewards is the primary goal in sequential decision-making problems.
Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards
We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts.
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
In ERC, we propose a regularizer that guides the approximation error tending towards the 1-eigensubspace, resulting in a more efficient and stable path of value approximation.
Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit
We study a structured multi-agent multi-armed bandit (MAMAB) problem in a dynamic environment.
Cooperative Thresholded Lasso for Sparse Linear Bandit
We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension $d$ whereas the reward function depends on only a limited set of features - precisely $s_0 \ll d$.
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS).
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing
Without this assumption, the regret bound of our algorithm is $\gamma_T\log(T)$.
Robustness Implies Fairness in Causal Algorithmic Recourse
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome.
Linear Combinatorial Semi-Bandit with Causally Related Rewards
The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology.
Hypothesis Transfer in Bandits by Weighted Models
We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning.
A Learning-Based Approach to Approximate Coded Computation
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials.
Distributed Task Management in Fog Computing: A Socially Concave Bandit Game
One strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback.
Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization
Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance.
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
Our proposed recommender system employs this policy to learn the users' item preferences online while minimizing runtime.
Personalized Education in the AI Era: What to Expect Next?
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal.
Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions
Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
First, we propose a fully centralized beamforming method that uses the deep deterministic policy gradient algorithm (DDPG) with continuous space.
A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission
The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem.
Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty
The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among many available ones.
Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
In this article, we study the distributed cell association problem for energy harvesting IoT devices in UD-SCNs.
Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model
We consider a distributed downlink user association problem in a small cell network, where small cells obtain the required energy for providing wireless services to users through ambient energy harvesting.
Multi-armed Bandits with Application to 5G Small Cells
This requires the next generation wireless networks to move towards new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services.
