83 citations · 254 across the 14 of their papers we have counts for
11 papers · 1 filter
Inverse Reinforcement Learning with Multiple Planning Horizons
Jiayu Yao, Weiwei Pan, Finale Doshi-Velez +1
In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons.…
Contextual Conservative Q-Learning for Offline Reinforcement Learning
Ke Jiang, Jiayu Yao, Xiaoyang Tan
Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of prac…
Deep Semi-supervised Learning with Double-Contrast of Features and Semantics
Quan Feng, Jiayu Yao, Zhison Pan +1
In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. Howeve…
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
Mark Penrod, Harrison Termotto, Varshini Reddy +3
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictiv…
Policy Optimization with Sparse Global Contrastive Explanations
Jiayu Yao, Sonali Parbhoo, Weiwei Pan +1
We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaini…
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts
Jiayu Yao, Qingyuan Wu, Quan Feng +1
Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discri…
Power Constrained Bandits
Jiayu Yao, Emma Brunskill, Weiwei Pan +2
Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health…
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Jiayu Yao, Weiwei Pan, Soumya Ghosh +1
Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network. Inference in BNNs, however, is difficult; all inference methods for BNNs are approximate. In t…
Output-Constrained Bayesian Neural Networks
Wanqian Yang, Lars Lorch, Moritz A. Graule +5
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates fu…
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
Melanie F. Pradier, Weiwei Pan, Jiayu Yao +2
As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial. While modern neural networks are making remarkabl…
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Omer Gottesman, Fredrik Johansson, Joshua Meier +16
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL)…