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Paul Edmund Chang

6 accepted papers

2026

Efficient Autoregressive Inference for Transformer Probabilistic Models

ICLR 2026poster

Transformer-based models for amortized probabilistic inference, such as neural processes, prior-fitted networks, and tabular foundation models, excel at single-pass *marginal* prediction. However, many real-world applications require coherent *joint distributions* that capture dependencies between p…

Cited by 0SourceScholar
2026

PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference

ICLR 2026poster

Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging modern generative methods like diffusion models to map observed data to model parameters or future predictions. These a…

Cited by 0SourceScholar
2025

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference

AISTATS 2025poster

Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are leading models for probabilistic meta-learning with a tractable objective. Often trained on synthetic data, these models…

Cited by 0SourceScholar
2024

Function-space Parameterization of Neural Networks for Sequential Learning

ICLR 2024poster

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images. To address these…

2023

Memory-Based Dual Gaussian Processes for Sequential Learning

ICML 2023oral

Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning cha…

2021

Dual Parameterization of Sparse Variational Gaussian Processes

NeurIPS 2021poster

Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we improve their computational efficiency by using a dual parameterization where each data example is assigned dual parameters,…