Search Results for author:
Found 24 papers, 5 papers with code
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases.
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.
Gram-based Attentive Neural Ordinary Differential Equations Network for Video Nystagmography Classification
Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direction, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
Semantic maps represent the environment using a set of semantically meaningful objects.
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty
By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.
Model Embedding Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL).
Model-based Reinforcement Learning
reinforcement-learning
+1
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping.
Depth Completion via Deep Basis Fitting
In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements.
S2VG: Soft Stochastic Value Gradient method
In this paper, we propose a simple and elegant model-based reinforcement learning algorithm called soft stochastic value gradient method (S2VG).
Model-based Reinforcement Learning
reinforcement-learning
+1
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Nonlinear Distributional Gradient Temporal-Difference Learning
We devise a distributional variant of gradient temporal-difference (TD) learning.
Projection-Free Algorithms in Statistical Estimation
Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.
Fast Global Convergence via Landscape of Empirical Loss
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.
Robotics
SAGA and Restricted Strong Convexity
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.
Linear convergence of SDCA in statistical estimation
In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption.
Linear Convergence of SVRG in Statistical Estimation
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
This paper considers the subspace clustering problem where the data contains irrelevant or corrupted features.
