17 citations · 54 across the 9 of their papers we have counts for
9 papers
Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering
Maria I. Gorinova, Macey Baker, Amy Heineike +3
Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and enviro…
A Framework for Evaluating Agentic Skills at Scale
Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich +3
Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across…
Program Analysis of Probabilistic Programs
Maria I. Gorinova
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practi…
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova +3
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge…
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an…
Conditional independence by typing
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton +1
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to r…
Automatic Reparameterisation of Probabilistic Programs
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow mode…
Effect Handling for Composable Program Transformations in Edward2
Dave Moore, Maria I. Gorinova
Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects. They have found sever…
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifice…