In this paper we study the problem of testing of constrained samplers over high-dimensional distributions with $(\varepsilon,\eta,\delta)$ guarantees. Samplers are increasingly used in a wide range of safety-critical ML applications, and hence the testing problem has gained importance. For $n$-dimensional distributions, the existing state-of-the-art algorithm, $\mathsf{Barbarik2}$, has a worst case query complexity of exponential in $n$ and hence is not ideal for use in practice. Our primary contribution is an exponentially faster algorithm, $\mathsf{Barbarik3}$, that has a query complexity linear in $n$ and hence can easily scale to larger instances. We demonstrate our claim by implementing our algorithm and then comparing it against $\mathsf{Barbarik2}$. Our experiments on the samplers $\mathsf{wUnigen3}$ and $\mathsf{wSTS}$, find that $\mathsf{Barbarik3}$ requires $10\times$ fewer samples for $\mathsf{wUnigen3}$ and $450\times$ fewer samples for $\mathsf{wSTS}$ as compared to $\mathsf{Barbarik2}$.

In this paper we study the problem of testing of constrained samplers over high-dimensional distributions with (\varepsilon,\eta,\delta) guarantees. Samplers are increasingly used in a wide range of safety-critical ML applications, and hence the testing problem has gained importance. For n -dimensional distributions, the existing state-of-the-art algorithm, \mathsf{Barbarik2}, has a worst case query complexity of exponential in n and hence is not ideal for use in practice. Our primary contribution is an exponentially faster algorithm, \mathsf{Barbarik3}, that has a query complexity linear in n and hence can easily scale to larger instances. We demonstrate our claim by implementing our algorithm and then comparing it against \mathsf{Barbarik2} .

In the race to manufacture autonomous vehicles (AVs), safety is crucial yet sometimes overlooked as exemplified by recent headline-making accidents. Researchers at the University of Illinois at Urbana-Champaign are using artificial intelligence (AI) and machine learning to improve the safety of autonomous technology through both software and hardware advances. "Using AI to improve autonomous vehicles is extremely hard because of the complexity of the vehicle's electrical and mechanical components, as well as variability in external conditions, such as weather, road conditions, topography, traffic patterns, and lighting," said Ravi Iyer "Progress is being made, but safety continues to be a significant concern." The group has developed a platform that enables companies to more quickly and cost-effectively address safety in the complex and ever-changing environment of autonomous technology. They are collaborating with many companies in the Bay area, including Samsung, NVIDIA, and a number of start-ups.