Search Results for author: Xiuyuan Lu

Found 18 papers, 7 papers with code

ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras

1 code implementation12 Oct 2024 Junkai Niu, Sheng Zhong, Xiuyuan Lu, Shaojie Shen, Guillermo Gallego, Yi Zhou

To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking.

motion prediction Pose Tracking +2

Event-Aided Time-to-Collision Estimation for Autonomous Driving

no code implementations10 Jul 2024 Jinghang Li, Bangyan Liao, Xiuyuan Lu, Peidong Liu, Shaojie Shen, Yi Zhou

Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system.

Autonomous Driving

RLHF and IIA: Perverse Incentives

no code implementations2 Dec 2023 Wanqiao Xu, Shi Dong, Xiuyuan Lu, Grace Lam, Zheng Wen, Benjamin Van Roy

Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independence of irrelevant alternatives (IIA).

reinforcement-learning Reinforcement Learning

Approximate Thompson Sampling via Epistemic Neural Networks

1 code implementation18 Feb 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Further, we demonstrate that the \textit{epinet} -- a small additive network that estimates uncertainty -- matches the performance of large ensembles at orders of magnitude lower computational cost.

Thompson Sampling

Robustness of Epinets against Distributional Shifts

no code implementations1 Jul 2022 Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari, Sven Gowal, Vikranth Dwaracherla, Zheng Wen, Benjamin Van Roy

However, these improvements are relatively small compared to the outstanding issues in distributionally-robust deep learning.

Deep Learning

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping

no code implementations8 Jun 2022 Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy

In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions.

An Analysis of Ensemble Sampling

no code implementations2 Mar 2022 Chao Qin, Zheng Wen, Xiuyuan Lu, Benjamin Van Roy

Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable.

Thompson Sampling

Evaluating High-Order Predictive Distributions in Deep Learning

1 code implementation28 Feb 2022 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Benjamin Van Roy

Previous work has developed methods for assessing low-order predictive distributions with inputs sampled i. i. d.

Deep Learning regression +1

Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting

1 code implementation5 Nov 2021 Xiuyuan Lu, Yi Zhou, Shaojie Shen

In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i. e., the motion segmentation problem) with a monocular event camera.

Clustering Motion Segmentation +1

Epistemic Neural Networks

1 code implementation NeurIPS 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

We introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty.

Reinforcement Learning, Bit by Bit

no code implementations6 Mar 2021 Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.

reinforcement-learning Reinforcement Learning +1

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

1 code implementation16 Dec 2020 Yi Zhou, Guillermo Gallego, Xiuyuan Lu, SiQi Liu, Shaojie Shen

We develop a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.

Motion Segmentation Scene Understanding

Ensemble Sampling

no code implementations NeurIPS 2017 Xiuyuan Lu, Benjamin Van Roy

Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems.

Thompson Sampling

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