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Found 32 papers, 7 papers with code
Design of UAV flight state recognition and trajectory prediction system based on trajectory feature construction
With the impact of artificial intelligence on the traditional UAV industry, autonomous UAV flight has become a current hot research field.
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step.
Ranked #1 on
Image Super-Resolution
on Set14
Improved Implicit Neural Representation with Fourier Bases Reparameterized Training
We evaluate the proposed Fourier reparameterization method on different INR tasks with various MLP architectures, including vanilla MLP, MLP with positional encoding and MLP with advanced activation function, etc.
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features.
Differentially Private Reward Estimation with Preference Feedback
Within a standard minimax estimation framework, we provide tight upper and lower bounds on the error in estimating $\theta^*$ under both local and central models of DP.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical.
Understanding the Role of Feedback in Online Learning with Switching Costs
To design algorithms that can achieve the minimax regret, it is instructive to consider a more general setting where the learner has a budget of $B$ total observations.
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Under each framework, we consider both joint differential privacy (JDP) and local differential privacy (LDP) models.
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).
On Differentially Private Federated Linear Contextual Bandits
We first establish privacy and regret guarantees under silo-level local differential privacy, which fix the issues present in state-of-the-art algorithm.
(Private) Kernelized Bandits with Distributed Biased Feedback
This problem is motivated by several real-world applications (such as dynamic pricing, cellular network configuration, and policy making), where users from a large population contribute to the reward of the action chosen by a central entity, but it is difficult to collect feedback from all users.
Grant-Free NOMA-OTFS Paradigm: Enabling Efficient Ubiquitous Access for LEO Satellite Internet-of-Things
With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally.
Differentially Private Linear Bandits with Partial Distributed Feedback
To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients' local feedback in a differentially private fashion.
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping.
Distributed Differential Privacy in Multi-Armed Bandits
This protocol achieves ($\epsilon,\delta$) or approximate-DP guarantee by sacrificing an additional additive $O\!\left(\!\frac{K\log T\sqrt{\log(1/\delta)}}{\epsilon}\!\right)\!$ cost in $T$-step cumulative regret.
Learning to Control under Time-Varying Environment
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
On Kernelized Multi-Armed Bandits with Constraints
Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting.
Shuffle Private Linear Contextual Bandits
Prior work largely focus on two trust models of DP: the central model, where a central server is responsible for protecting users sensitive data, and the (stronger) local model, where information needs to be protected directly on user side.
Differentially Private Reinforcement Learning with Linear Function Approximation
Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP).
Adversarial Attack via Dual-Stage Network Erosion
Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations.
Differentially Private Regret Minimization in Episodic Markov Decision Processes
We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP).
Wasserstein Generative Learning of Conditional Distribution
The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution.
Learning Coated Adversarial Camouflages for Object Detectors
An adversary can fool deep neural network object detectors by generating adversarial noises.
Adaptive Control of Differentially Private Linear Quadratic Systems
In this paper, we study the problem of regret minimization in reinforcement learning (RL) under differential privacy constraints.
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.
Weighted Gaussian Process Bandits for Non-stationary Environments
To this end, we develop WGP-UCB, a novel UCB-type algorithm based on weighted Gaussian process regression.
No-Regret Algorithms for Time-Varying Bayesian Optimization
In this paper, we consider the time-varying Bayesian optimization problem.
A Cloud-Based Energy Management Strategy for Hybrid Electric City Bus Considering Real-Time Passenger Load Prediction
The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability.
Local Differential Privacy for Bayesian Optimization
Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee.
Multi-Armed Bandits with Local Differential Privacy
To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.
TopoAna: A generic tool for the event type analysis of inclusive Monte-Carlo samples in high energy physics experiments
To help analysts obtain the physics process information from the truth information of the samples, we develop a physics process analysis program, TopoAna, with C++, ROOT, and LaTeX.
High Energy Physics - Experiment
Explaining Adversarial Examples with Knowledge Representation
This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view.
