45 citations · 50 across the 10 of their papers we have counts for
10 papers
Sequential Bayesian Evaluation of Large Language Model Behavior
Rachel Longjohn, Shang Wu, Saatvik Kher +2
It is increasingly important to evaluate the characteristics of systems based on large language models (LLMs). Evaluations in this context often rely on a curated benchmark set of…
Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space
Giosue Migliorini, Padhraic Smyth
Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional settings. Auxiliary information, such as…
Shifting norms in scholarly publications: trends in readability, objectivity, authorship, and AI use
Padraig Cunningham, Padhraic Smyth, Barry Smyth
Academic and scientific publishing practices have changed significantly in recent years. This paper presents an analysis of 17 million research papers published since 2000 to explo…
Semantic Probabilistic Control of Language Models
Kareem Ahmed, Catarina G Belem, Padhraic Smyth +1
Semantic control entails steering LM generations towards satisfying subtle non-lexical constraints, e.g., toxicity, sentiment, or politeness, attributes that can be captured by a s…
Benchmark Data Repositories for Better Benchmarking
Rachel Longjohn, Markelle Kelly, Sameer Singh +1
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- a…
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning, Eric Nalisnick, Christophe Ley +2
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers…
EventFlow: Forecasting Temporal Point Processes with Flow Matching
Gavin Kerrigan, Kai Nelson, Padhraic Smyth
Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling pa…
A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations
Clement Guilloteau, Gavin Kerrigan, Kai Nelson +4
A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and…
Dynamic Conditional Optimal Transport through Simulation-Free Flows
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
We study the geometry of conditional optimal transport (COT) and prove a dynamical formulation which generalizes the Benamou-Brenier Theorem. Equipped with these tools, we propose…
Stick-Breaking Variational Autoencoders
Eric Nalisnick, Padhraic Smyth
We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Va…