13 citations · 32 across the 9 of their papers we have counts for
10 papers · 1 filter
RLHF and IIA: Perverse Incentives
Wanqiao Xu, Shi Dong, Xiuyuan Lu +3
Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independen…
Approximate Thompson Sampling via Epistemic Neural Networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +4
Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling from a posterior distribution. Unfortunately, this can become computationally intractab…
Robustness of Epinets against Distributional Shifts
Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari +4
Recent work introduced the epinet as a new approach to uncertainty modeling in deep learning. An epinet is a small neural network added to traditional neural networks, which, toget…
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
Vikranth Dwaracherla, Zheng Wen, Ian Osband +3
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains…
An Analysis of Ensemble Sampling
Chao Qin, Zheng Wen, Xiuyuan Lu +1
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In…
The Neural Testbed: Evaluating Joint Predictions
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +7
Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluat…