2 citations · 2 across the 32 of their papers we have counts for
23 papers · 1 filter
Intervention, Not Shared Latents: Blocking Visual Shortcuts in Audio-Video Generation
Jian Xu, Delu Zeng, John Paisley
Joint audio--video (AV) generators are trained on data in which \emph{what an event looks like} and \emph{what it sounds like} are spuriously correlated. We present a \emph{control…
Partially Observed Sparse Graphs: The Unknown Sampling Rate is a Tail Index
Jian Xu, Delu Zeng, John Paisley +1
A large graph is often available only in part: a crawl stopped by its budget, a panel, a partial dump. When the sampled fraction is known by design the total edge count follows…
When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking
Jian Xu, Delu Zeng, John Paisley +1
Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise. Because such judges prefer verbose or well-formatted answers,…
Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models
Jian Xu, Delu Zeng, John Paisley +1
Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can still be \emph{statisticall…
Onsager-Machlup Posterior Transport for Deep Gaussian Processes
Jian Xu, Delu Zeng, John Paisley +1
Approximate inference over inducing variables is the central computational bottleneck of Deep Gaussian Processes (DGPs). Existing methods either fit an explicit density $q_ϕ(\bU)$…
Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport
Jian Xu, Delu Zeng, John Paisley +1
Quantum pure-state ensembles live on complex projective space, making flat Euclidean generative modeling geometrically mismatched. We introduce Intrinsic Flow Matching (IFM), a det…
Effective Dimension Governs Generalization in Quantum Kernel Vision Models
Jian Xu, Delu Zeng, John Paisley +1
Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more…
Implicit Variational Rejection Sampling
Jian Xu, Shigui Li, Wei Chen +6
Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions. Traditional VI often relies on the m…
The Right Measure for Physics-Constrained Generation: A Co-Area Correction for Posterior-Consistent PDE Inverse Problems
Jian Xu, Yanning Wu, Delu Zeng +2
Generative models -- diffusion and flow matching -- are increasingly used to solve partial differential equation (PDE) inverse problems, enforcing the governing physics as a \emph{…
Spectral Anatomy of Quantum Gaussian Process Kernels
Jian Xu, Chao Li, Guang Lin +4
Two recent results have reshaped quantum Gaussian processes (QGPs). On the one hand, \citet{lowe2025assessing} rule out the exponential speedups claimed by HHL-based QGP regression…
AQKA: Active Quantum Kernel Acquisition Under a Shot Budget
Jian Xu, Chao Li, Delu Zeng +2
Estimating an quantum kernel from circuit fidelities requires measurement shots, the dominant bottleneck for deployment on near-term hardware. Existing budg…
Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner
Wei Chen, Yubing Wu, Junmei Yang +5
Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based methods also suppress the chosen response when they…
A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation
Wei Chen, Jiacheng Li, Shigui Li +4
Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-dependent. We resolve this by proving that p…
Diffusion Bridge Variational Inference for Deep Gaussian Processes
Jian Xu, Qibin Zhao, John Paisley +1
Deep Gaussian processes (DGPs) enable expressive hierarchical Bayesian modeling but pose substantial challenges for posterior inference, especially over inducing variables. Denoisi…
Neural Bridge Processes
Jian Xu, Yican Liu, Delu Zeng +2
Learning stochastic functions from partially observed context-target pairs requires models that are expressive, uncertainty-aware, and strongly conditioned on inputs. Neural Diffus…
Dequantified Diffusion-Schr{ö}dinger Bridge for Density Ratio Estimation
Wei Chen, Shigui Li, Jiacheng Li +3
Density ratio estimation is fundamental to tasks involving -divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping…
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
Jian Xu, Zhiqi Lin, Shigui Li +4
Bayesian Last Layer (BLL) models focus solely on uncertainty in the output layer of neural networks, demonstrating comparable performance to more complex Bayesian models. However,…
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
Jian Xu, Shian Du, Junmei Yang +3
Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their fle…
Sparse Variational Student-t Processes for Heavy-tailed Modeling
Jian Xu, Delu Zeng, John Paisley
The Gaussian process (GP) is a powerful tool for nonparametric modeling, but its sensitivity to outliers limits its applicability to data distributions with heavy-tails. Studentt p…
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu, Zhiqi Lin, Min Chen +3
Deep Gaussian process models typically employ discrete hierarchies, but recent advancements in differential Gaussian processes (DiffGPs) have extended these models to infinite dept…
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
Jian Xu, Delu Zeng, John Paisley
Deep Gaussian processes (DGPs) provide a robust paradigm for Bayesian deep learning. In DGPs, a set of sparse integration locations called inducing points are selected to approxima…
Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models
Wei Chen, Shian Du, Shigui Li +2
Normalizing Flows (NFs) are widely used in deep generative models for their exact likelihood estimation and efficient sampling. However, they require substantial memory since the l…
Gaussian Process Neural Additive Models
Wei Zhang, Brian Barr, John Paisley
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where int…