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Found 11 papers, 2 papers with code
Incentivized Exploration of Non-Stationary Stochastic Bandits
We consider two different non-stationary environments: abruptly-changing and continuously-changing, and propose respective incentivized exploration algorithms.
NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM
We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping pipeline based on neural multiresolution voxels, characterized by ultra-fast convergence and incremental expansion capabilities.
Unsupervised Deep Cross-Language Entity Alignment
We outperformed the state-of-the-art method in unsupervised and semi-supervised categories.
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training).
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation
In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data.
Ranked #3 on
Scene Flow Estimation
on Spring
Cooperative Control of Mobile Robots with Stackelberg Learning
Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives.
A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem
We investigate the sparse linear contextual bandit problem where the parameter $\theta$ is sparse.
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL).
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward.
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm
We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems.
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians
In this paper we propose a new Koopman operator approach to the decomposition of nonlinear dynamical systems using Koopman Gramians.
