9 citations · 11 across the 9 of their papers we have counts for
10 papers
Scalable Model-Assisted Multi-Target Estimation in Large Image Collections
Max Hamilton, Jinlin Lai, Daniel Sheldon +1
Computer vision models are increasingly used as measurement tools to estimate population-level quantities from large image collections, but prediction errors introduce bias and the…
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
Jinlin Lai, Charles C. Margossian, Daniel R. Sheldon
Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Effici…
Active Measurement: Efficient Estimation at Scale
Max Hamilton, Jinlin Lai, Wenlong Zhao +2
AI has the potential to transform scientific discovery by analyzing vast datasets with little human effort. However, current workflows often do not provide the accuracy or statisti…
Quasi-random Multi-Sample Inference for Large Language Models
Aditya Parashar, Aditya Vikram Singh, Avinash Amballa +2
Large language models (LLMs) are often equipped with multi-sample decoding strategies. An LLM implicitly defines an arithmetic code book, facilitating efficient and embarrassingly…
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Jinlin Lai, Justin Domke, Daniel Sheldon
Bayesian reasoning in linear mixed-effects models (LMMs) is challenging and often requires advanced sampling techniques like Markov chain Monte Carlo (MCMC). A common approach is t…
Predictive variational inference: Learn the predictively optimal posterior distribution
Jinlin Lai, Antonio Linero, Yuling Yao
Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model missp…
Automatically Marginalized MCMC in Probabilistic Programming
Jinlin Lai, Javier Burroni, Hui Guan +1
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from wri…
Variational Marginal Particle Filters
Jinlin Lai, Justin Domke, Daniel Sheldon
Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Ca…
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies
Jinlin Lai, Lixin Zou, Jiaxing Song
Off-policy evaluation is a key component of reinforcement learning which evaluates a target policy with offline data collected from behavior policies. It is a crucial step towards…
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Haowen Xu, Wenxiao Chen, Jinlin Lai +3
Using powerful posterior distributions is a popular approach to achieving better variational inference. However, recent works showed that the aggregated posterior may fail to match…