Search Results for author:
Found 18 papers, 4 papers with code
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
Model approximation in MDPs with unbounded per-step cost
How well does an optimal policy $\hat{\pi}^{\star}$ of the approximate model perform when used in the original model $\mathcal{M}$?
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems.
Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation
Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation.
Ranked #1 on
Node Classification
on Amazon-Fraud
Grasping Core Rules of Time Series through Pure Models
Time series underwent the transition from statistics to deep learning, as did many other machine learning fields.
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
The regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system.
Scalable regret for learning to control network-coupled subsystems with unknown dynamics
We consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems connected over a network.
Training a Resilient Q-Network against Observational Interference
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications.
RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS
In this study, we consider a situation in which the agent has access to the generative model which provides us with a next state sample for any given state-action pair, and propose a model to solve a CMDP problem by decomposing the CMDP into a pair of MDPs; \textit{reconnaissance} MDP (R-MDP) and \textit{planning} MDP (P-MDP).
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
Thompson sampling for linear quadratic mean-field teams
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i. e., empirical mean) of the states and controls.
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems
This allows the agents to achieve a regret within $O(\sqrt{T})$ of the regret of the auxiliary single-agent problem.
Learning Latent State Spaces for Planning through Reward Prediction
The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning.
Model-based Reinforcement Learning
reinforcement-learning
+1
Large-Scale Traffic Signal Offset Optimization
The offset optimization problem seeks to coordinate and synchronize the timing of traffic signals throughout a network in order to enhance traffic flow and reduce stops and delays.
Optimization and Control Systems and Control Systems and Control
Online Learning in Planar Pushing with Combined Prediction Model
In this paper, we propose a combined prediction model and an online learning framework for planar push prediction.
Learning Cross-Domain Representation with Multi-Graph Neural Network
In fact, user's behaviors from different domains regarding the same items are usually relevant.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
This regret bound matches the best available bound for weakly communicating MDPs.
