How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build …
Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more …
The ability to derive underlying principles from a handful of observations and then generalize to novel situations--known as inductive reasoning--is central to human …
T Zhi-Xuan, J Mann, T Silver… - Advances in neural …, 2020 - proceedings.neurips.cc
People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when we …
K Ellis - Advances in Neural Information Processing …, 2024 - proceedings.neurips.cc
A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, however …
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of …
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic …
We present the first systematic study of effectiveness of robustness transformations on a diverse set of 24 probabilistic programs representing generalized linear models, mixture …
Modern probabilistic programming languages aim to formalize and automate key aspects of probabilistic modeling and inference. Many languages provide constructs for programmable …