Search Results for author:
Found 31 papers, 4 papers with code
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
To this end, the primary objective of this work is to build a fundamental understanding on ``\textit{whether and when online learning can be significantly accelerated by a warm-start policy from offline RL?}''.
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
It is known that the estimation bias hinges heavily on the ensemble size (i. e., the number of Q-function approximators used in the target), and that determining the `right' ensemble size is highly nontrivial, because of the time-varying nature of the function approximation errors during the learning process.
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing
Inspired by the success of Self-supervised learning (SSL) in learning visual representations from unlabeled data, a few recent works have studied SSL in the context of continual learning (CL), where multiple tasks are learned sequentially, giving rise to a new paradigm, namely self-supervised continual learning (SSCL).
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift.
Algorithm Design for Online Meta-Learning with Task Boundary Detection
More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update in order to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks.
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced.
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively.
Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services
Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse.
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective
To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL.
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
TRGP: Trust Region Gradient Projection for Continual Learning
To tackle this challenge, we propose Trust Region Gradient Projection (TRGP) for continual learning to facilitate the forward knowledge transfer based on an efficient characterization of task correlation.
Model-Based Offline Meta-Reinforcement Learning with Regularization
In particular, we devise a new meta-Regularized model-based Actor-Critic (RAC) method for within-task policy optimization, as a key building block of MerPO, using conservative policy evaluation and regularized policy improvement; and the intrinsic tradeoff therein is achieved via striking the right balance between two regularizers, one based on the behavior policy and the other on the meta-policy.
Communication-Efficient Distributed SGD with Compressed Sensing
We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge devices imposes a significant bottleneck for the optimization procedure.
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipments' (F-UEs') quality of services (QoS).
Robust Event Classification Using Imperfect Real-world PMU Data
Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e. g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers.
GROWN: GRow Only When Necessary for Continual Learning
To address this issue, continual learning has been developed to learn new tasks sequentially and perform knowledge transfer from the old tasks to the new ones without forgetting.
Distributed Learning with Strategic Users: A Repeated Game Approach
We consider a distributed learning setting where strategic users are incentivized, by a cost-sensitive fusion center, to train a learning model based on local data.
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data.
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence
Learning generative models is challenging for a network edge node with limited data and computing power.
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity.
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Specifically, we assume that agents maintain local estimates of the global state based on their local information and communications with neighbors.
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
To overcome these challenges, we explore continual edge learning capable of leveraging the knowledge transfer from previous tasks.
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed network-level OCO for meta-model updates via limited communication, which however is not well understood.
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally.
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions.
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which enables dynamical selection of a subset of filters for more efficient local computation given the data input.
System Identification via Meta-Learning in Linear Time-Varying Environments
System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexity is challenging and elusive, even for linear time-varying (LTV) systems.
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Thus motivated, we propose a new training method called \textit{kernel-wise Soft Mask} (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task, while using the same backbone model.
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning
Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge.
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence.
