Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

Intelligence relies on an agent's knowledge of what it does not know. This capability can be
assessed based on the quality of joint predictions of labels across multiple inputs. In …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …

Ensemble sampling serves as a practical approximation to Thompson sampling when
maintaining an exact posterior distribution over model parameters is computationally …

Thompson sampling has proven effective across a wide range of stationary bandit
environments. However, as we demonstrate in this paper, it can perform poorly when …

Predictive distributions quantify uncertainties ignored by point estimates. This paper
introduces The Neural Testbed: an open source benchmark for controlled and principled …

Identifying how much a model ${\widehat {p}} _ {\theta}(Y| X) $ knows about the stochastic
real-world process $ p (Y| X) $ it was trained on is important to ensure it avoids producing …

The linearized-Laplace approximation (LLA) has been shown to be effective and efficient in
constructing Bayesian neural networks. It is theoretically compelling since it can be seen as …

We explore uncertainty quantification in large language models (LLMs), with the goal to
identify when uncertainty in responses given a query is large. We simultaneously consider …