193 citations · 244 across the 22 of their papers we have counts for
6 papers · 1 filter
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
Changyu Chen, Ramesha Karunasena, Thanh Hong Nguyen +2
Many problems in Reinforcement Learning (RL) seek an optimal policy with large discrete multidimensional yet unordered action spaces; these include problems in randomized allocatio…
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Chen Gong, Zhou Yang, Yunpeng Bai +8
Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL…
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO so…
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models
Changyu Chen, Avinandan Bose, Shih-Fen Cheng +1
Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative mod…
Beyond NaN: Resiliency of Optimization Layers in The Face of Infeasibility
Wai Tuck Wong, Sarah Kinsey, Ramesha Karunasena +2
Prior work has successfully incorporated optimization layers as the last layer in neural networks for various problems, thereby allowing joint learning and planning in one neural n…
Measuring Data Collection Diligence for Community Healthcare
Ramesha Karunasena, Mohammad Sarparajul Ambiya, Arunesh Sinha +5
Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of high-quality public health data is a significant challe…