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Found 93 papers, 16 papers with code
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.
Hierarchical Deep Counterfactual Regret Minimization
A notable advantage of HDCFR over previous research in this field is its ability to facilitate learning with predefined (human) expertise and foster the acquisition of transferable skills that can be applied to similar tasks.
A Unified Approach for Maximizing Continuous DR-submodular Functions
This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types.
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots.
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward
We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback.
Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback
This paper investigates the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed feedback is assumed to be composite and anonymous.
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning
This improvement is a key to the significant regret improvement in quantum reinforcement learning.
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Federated learning is an emerging machine learning paradigm that enables clients to train collaboratively without exchanging local data.
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization.
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
The framework only requires the offline algorithms to be robust to small errors in function evaluation.
Quantum Heavy-tailed Bandits
In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle.
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State
We compute the approximation error as $\mathcal{O}(e)$ where $e=\frac{1}{\sqrt{N}}\left[\sqrt{|\mathcal{X}|} +\sqrt{|\mathcal{U}|}\right]$.
ODPP: A Unified Algorithm Framework for Unsupervised Option Discovery based on Determinantal Point Process
Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research.
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift
Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research.
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood.
Multi-agent Deep Covering Option Discovery
In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
In this work, we develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning and adapt it with the Expectation-Maximization algorithm in order to directly recover a hierarchical policy from the unannotated demonstrations.
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)
In a special case where the reward, cost, and state transition functions are independent of the action distribution of the population, we prove that the error can be improved to $e=\mathcal{O}(\sqrt{|\mathcal{X}|}/\sqrt{N})$.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning
We show that in a cooperative $N$-agent network, one can design locally executable policies for the agents such that the resulting discounted sum of average rewards (value) well approximates the optimal value computed over all (including non-local) policies.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
A Community-Aware Framework for Social Influence Maximization
We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme.
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+4
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Newton-type methods are popular in federated learning due to their fast convergence.
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm
We propose a novel Conservative Natural Policy Gradient Primal-Dual Algorithm (C-NPG-PD) to achieve zero constraint violation while achieving state of the art convergence results for the objective value function.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?
We prove that, if the reward of each agent is an affine function of the mean-field seen by that agent, then one can approximate such a non-uniform MARL problem via its associated MFC problem within an error of $e=\mathcal{O}(\frac{1}{\sqrt{N}}[\sqrt{|\mathcal{X}|} + \sqrt{|\mathcal{U}|}])$ where $N$ is the population size and $|\mathcal{X}|$, $|\mathcal{U}|$ are the sizes of state and action spaces respectively.
Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks
This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology.
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.
Classical Simulation of Variational Quantum Classifiers using Tensor Rings
This manuscript proposes an algorithm that compresses the quantum state within a circuit using a tensor ring representation which allows for the implementation of VQC based algorithms on a classical simulator at a fraction of the usual storage and computational complexity.
Learning Multi-agent Options for Tabular Reinforcement Learning using Factor Graphs
Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph.
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
To this end, we present LSF-SAC, a novel framework that features a variational inference-based information-sharing mechanism as extra state information to assist individual agents in the value function factorization.
Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case).
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
We establish that the sample complexity to obtain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with respect to the network in accordance with multi-agent optimization.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
To achieve that, we advocate the use of randomized primal-dual approach to solve the CMDP problems and propose a conservative stochastic primal-dual algorithm (CSPDA) which is shown to exhibit $\tilde{\mathcal{O}}\left(1/\epsilon^2\right)$ sample complexity to achieve $\epsilon$-optimal cumulative reward with zero constraint violations.
Concave Utility Reinforcement Learning with Zero-Constraint Violations
We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints.
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
We show that, in these cases, the $K$-class MARL problem can be approximated by MFC with errors given as $e_1=\mathcal{O}(\frac{\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}}{N_{\mathrm{pop}}}\sum_{k}\sqrt{N_k})$, $e_2=\mathcal{O}(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\sum_{k}\frac{1}{\sqrt{N_k}})$ and $e_3=\mathcal{O}\left(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right)$, respectively, where $A, B$ are some constants and $|\mathcal{X}|,|\mathcal{U}|$ are the sizes of state and action spaces of each agent.
DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning
This study proposes the deep relocating option policy (DROP) that supervises vehicle agents to escape from oversupply areas and effectively relocate to potentially underserved areas.
An FEA surrogate model with Boundary Oriented Graph Embedding approach
In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems.
Markov Decision Processes with Long-Term Average Constraints
We consider the problem of constrained Markov Decision Process (CMDP) where an agent interacts with a unichain Markov Decision Process.
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
In this paper, we propose an energy-efficient federated meta-learning framework.
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
Many engineering problems have multiple objectives, and the overall aim is to optimize a non-linear function of these objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Quantum causal inference in the presence of hidden common causes: An entropic approach
We also demonstrate that the proposed approach outperforms the results of classical causal inference for the Tubingen database when the variables are classical by exploiting quantum dependence between variables through density matrices rather than joint probability distributions.
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection
To mitigate this problem in highly dynamic environments, we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to detect the changes in the distribution of experiences, (2) develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment.
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery
Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Monopoly is a popular strategic board game that requires players to make multiple decisions during the game.
Quantum Entropic Causal Inference
This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multi-node quantum networks as well as quantum error correction.
Communication Efficient Parallel Reinforcement Learning
We provide \NAM\ which runs at each agent and prove that the total cumulative regret of $M$ agents is upper bounded as $\Tilde{O}(DS\sqrt{MAT})$ for a Markov Decision Process with diameter $D$, number of states $S$, and number of actions $A$.
Multi-Agent Multi-Armed Bandits with Limited Communication
With our algorithm, LCC-UCB, each agent enjoys a regret of $\tilde{O}\left(\sqrt{({K/N}+ N)T}\right)$, communicates for $O(\log T)$ steps and broadcasts $O(\log K)$ bits in each communication step.
A Supervised Learning Approach for Robust Health Monitoring using Face Videos
Non-contact methods can have additional advantages since they are scalable with any environment where video can be captured, can be used for continuous measurements, and can be used on patients with varying levels of dexterity and independence, from people with physical impairments to infants (e. g., baby camera).
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents
We consider a multi-agent Markov strategic interaction over an infinite horizon where agents can be of multiple types.
A multi-agent evolutionary robotics framework to train spiking neural networks
Rules of the framework select certain bots and their SNNs for reproduction and others for elimination based on their efficacy in capturing food in a competitive environment.
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning
The ubiquitous growth of mobility-on-demand services for passenger and goods delivery has brought various challenges and opportunities within the realm of transportation systems.
DART: aDaptive Accept RejecT for non-linear top-K subset identification
Additionally, our algorithm works on correlated rewards of individual arms.
Blind Decision Making: Reinforcement Learning with Delayed Observations
This paper proposes an approach, where the delay in the knowledge of the state can be used, and the decisions are made based on the available information which may not include the current state information.
Cross Layer Optimization and Distributed Reinforcement Learning Approach for Tile-Based 360 Degree Wireless Video Streaming
Wirelessly streaming high quality 360 degree videos is still a challenging problem.
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning
In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations.
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning
In this paper, we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network.
FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation
Through simulations on a realistic multi-agent urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers & goods.
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).
Model-Free Algorithm and Regret Analysis for MDPs with Long-Term Constraints
This paper uses concepts from constrained optimization and Q-learning to propose an algorithm for CMDP with long-term constraints.
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions
Experimentally, we observe state of the art accuracy and complexity tradeoffs for GP bandit algorithms on various hyper-parameter tuning tasks, suggesting the merits of managing the complexity of GPs in bandit settings
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.
Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding
Doing so permits us to precisely characterize the trade-off between regret bounds of GP bandit algorithms and complexity of the posterior distributions depending on the compression parameter $\epsilon$ for both discrete and continuous action sets.
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints
The proposed algorithm is proved to achieve an $(\epsilon, p)$-PAC policy when the episode $K\geq\Omega(\frac{I^2H^6SA\ell}{\epsilon^2})$, where $S$ and $A$ are the number of states and actions, respectively.
A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning
The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services.
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).
Escaping Saddle Points for Zeroth-order Nonconvex Optimization using Estimated Gradient Descent
Gradient descent and its variants are widely used in machine learning.
Deep Reinforcement Learning Based Power control for Wireless Multicast Systems
However for this system, obtaining optimal power control is intractable because of a very large state space.
Encoders and Decoders for Quantum Expander Codes Using Machine Learning
However, large-scale design of quantum encoders and decoders have to depend on the channel characteristics and require look-up tables which require memory that is exponential in the number of qubits.
Reinforcement Learning for Joint Optimization of Multiple Rewards
Finding optimal policies which maximize long term rewards of Markov Decision Processes requires the use of dynamic programming and backward induction to solve the Bellman optimality equation.
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.
Reinforcement Learning for Mean Field Game
This paper focuses on finding a mean-field equilibrium (MFE) in an action coupled stochastic game setting in an episodic framework.
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed.
A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications
We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors.
Information Theory Information Theory
Stochastic Top-$K$ Subset Bandits with Linear Space and Non-Linear Feedback
Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $K$ out of $N$ options at a given time instant.
Covfefe: A Computer Vision Approach For Estimating Force Exertion
We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos.
FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP
For example, on an experiment conducted over 100 real cellular bandwidth traces of a public dataset that spans different bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to the original dash. js rate adaptation scheme, Festive, BBA, RB, and FastMPC algorithms.
Networking and Internet Architecture Multimedia
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches.
Networking and Internet Architecture Multimedia
Principal Component Analysis with Tensor Train Subspace
Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of low-rank tensors.
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
In this letter, we study the deterministic sampling patterns for the completion of low rank matrix, when corrupted with a sparse noise, also known as robust matrix completion.
Tensor Train Neighborhood Preserving Embedding
In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace.
Efficient Low Rank Tensor Ring Completion
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation.
Rank Determination for Low-Rank Data Completion
Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given.
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data
We provide a deterministic necessary and sufficient condition on the sampling pattern for finite completability.
Deterministic and Probabilistic Conditions for Finite Completability of Low-Tucker-Rank Tensor
We investigate the fundamental conditions on the sampling pattern, i. e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank.
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model
Similar to the Union of Subspaces (UOS) model where each data from each subspace is generated from a (unknown) basis, in the UOPC model each data from each cone is assumed to be generated from a finite number of (unknown) \emph{extreme rays}. To cluster data under this model, we consider several algorithms - (a) Sparse Subspace Clustering by Non-negative constraints Lasso (NCL), (b) Least squares approximation (LSA), and (c) K-nearest neighbor (KNN) algorithm to arrive at affinity between data points.
Low-tubal-rank Tensor Completion using Alternating Minimization
The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data.
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model
Using the matrix product state (MPS) representation of tensor train decompositions, in this paper we propose a tensor completion algorithm which alternates over the matrices (tensors) in the MPS representation.
On Deterministic Conditions for Subspace Clustering under Missing Data
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model.
On deterministic conditions for subspace clustering under missing data
We provide extensive set of simulation results for clustering as well as completion of data under missing entries, under the UoS model.
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model
In this short note we extend some of the recent results on matrix completion under the assumption that the columns of the matrix can be grouped (clustered) into subspaces (not necessarily disjoint or independent).
Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization
In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.
