19 citations · 51 across the 14 of their papers we have counts for
12 papers · 1 filter
From Random Search to Bandit Learning in Metric Measure Spaces
Chuying Han, Yasong Feng, Tianyu Wang
Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, li…
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…
Latent Policies for Adversarial Imitation Learning
Tianyu Wang, Nikhil Karnwal, Nikolay Atanasov
This paper considers learning robot locomotion and manipulation tasks from expert demonstrations. Generative adversarial imitation learning (GAIL) trains a discriminator that disti…
Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds
Yasong Feng, Tianyu Wang
We study stochastic zeroth order gradient and Hessian estimators for real-valued functions in . We show that, via taking finite difference along random orthogonal dir…
Lipschitz Bandits with Batched Feedback
Yasong Feng, Zengfeng Huang, Tianyu Wang
In this paper, we study Lipschitz bandit problems with batched feedback, where the expected reward is Lipschitz and the reward observations are communicated to the player in batche…
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata
Tianyu Wang, Nikolay Atanasov
This paper presents a method for learning logical task specifications and cost functions from demonstrations. Constructing specifications by hand is challenging for complex objecti…
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang +9
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Mat…
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…
Adaptive Discretization for Model-Based Reinforcement Learning
Sean R. Sinclair, Tianyu Wang, Gauri Jain +2
We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm in large (potentially continuous) state-action sp…
Learning Navigation Costs from Demonstration in Partially Observable Environments
Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments. The objective is to infe…