4 citations · 10 across the 7 of their papers we have counts for
7 papers
SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels
Han Yang, Tianyu Wang, Xiaowei Hu +1
Existing shadow detection datasets often contain missing or mislabeled shadows, which can hinder the performance of deep learning models trained directly on such data. To address t…
A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization
Yasong Feng, Weijian Luo, Yimin Huang +1
One of the most critical problems in machine learning is HyperParameter Optimization (HPO), since choice of hyperparameters has a significant impact on final model performance. Alt…
A Direct Approximation of AIXI Using Logical State Abstractions
Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng
We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class t…
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning
Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
This paper focuses on inverse reinforcement learning for autonomous navigation using distance and semantic category observations. The objective is to infer a cost function that exp…
Episodic Linear Quadratic Regulators with Low-rank Transitions
Tianyu Wang, Lin F. Yang
Linear Quadratic Regulators (LQR) achieve enormous successful real-world applications. Very recently, people have been focusing on efficient learning algorithms for LQRs when their…
Bandits for BMO Functions
Tianyu Wang, Cynthia Rudin
We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are…