20 citations · 28 across the 8 of their papers we have counts for
8 papers
Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR
Yuhang He, Haodong Wu, Siyi Liu +7
Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning ability of Large Language Models (LLMs), but sparse outcome rewards make token-level credit assignment…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…
Interoperability of statistical models in pandemic preparedness: principles and reality
George Nicholson, Marta Blangiardo, Mark Briers +12
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving p…
Couplings for Multinomial Hamiltonian Monte Carlo
Kai Xu, Tor Erlend Fjelde, Charles Sutton +1
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian inference. Recently, Heng & Jacob (2019) studied Metropolis HMC with couplings for unbiased Monte Carlo estim…
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek, Kai Xu, Martin Trapp +2
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a com…
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge +2
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs i…
Dirichlet Fragmentation Processes
Hong Ge, Yarin Gal, Zoubin Ghahramani
Tree structures are ubiquitous in data across many domains, and many datasets are naturally modelled by unobserved tree structures. In this paper, first we review the theory of ran…
A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models
Nilesh Tripuraneni, Shane Gu, Hong Ge +1
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden state…