John Paisley

ICML Conference 2025 Conference Paper

Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation

• Wei Chen 0165
• Shigui Li
• Jiacheng Li
• Junmei Yang
• John Paisley
• Delu Zeng

Density ratio estimation is fundamental to tasks involving f-divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping supports — the density-chasm and the support-chasm problems. Additionally, prior approaches yield divergent time scores near boundaries, leading to instability. We design $\textbf{D}^3\textbf{RE}$, a unified framework for robust, stable and efficient density ratio estimation. We propose the dequantified diffusion bridge interpolant (DDBI), which expands support coverage and stabilizes time scores via diffusion bridges and Gaussian dequantization. Building on DDBI, the proposed dequantified Schrödinger bridge interpolant (DSBI) incorporates optimal transport to solve the Schrödinger bridge problem, enhancing accuracy and efficiency. Our method offers uniform approximation and bounded time scores in theory, and outperforms baselines empirically in mutual information and density estimation tasks.

UAI Conference 2025 Conference Paper

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

• Jian Xu 0021
• Shian Du
• Junmei Yang
• Qianli Ma 0001
• Delu Zeng
• John Paisley

Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach is primarily limited to analyzing simple data structures, as the generation of an effective proposal distribution can become quite challenging in high-dimensional spaces or with complex data sets. In this work, we propose VAIS-GPLVM, a variational Annealed Importance Sampling method that leverages time-inhomogeneous unadjusted Langevin dynamics to construct the variational posterior. By transforming the posterior into a sequence of intermediate distributions using annealing, we combine the strengths of Sequential Monte Carlo samplers and VI to explore a wider range of posterior distributions and gradually approach the target distribution. We further propose an efficient algorithm by reparameterizing all variables in the evidence lower bound (ELBO). Experimental results on both toy and image datasets demonstrate that our method outperforms state-of-the-art methods in terms of tighter variational bounds, higher log-likelihoods, and more robust convergence.

AAAI Conference 2024 Conference Paper

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 interpretable and explainable models are required. The recent development of Neural Additive Models (NAMs) poses a major step in the direction of interpretable deep learning for tabular datasets. In this paper, we propose a new subclass of NAMs that utilize a single-layer neural network construction of the Gaussian process via random Fourier features, which we call Gaussian Process Neural Additive Models (GP-NAM). GP-NAMs have the advantage of a convex objective function and number of trainable parameters that grows linearly with feature dimensions. It suffers no loss in performance compared with deeper NAM approaches because GPs are well-suited to learning complex non-parametric univariate functions. We demonstrate the performance of GP-NAM on several tabular datasets, showing that it achieves comparable performance in both classification and regression tasks with a massive reduction in the number of parameters.

ICML Conference 2024 Conference Paper

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference

• Jian Xu 0021
• Delu Zeng
• John Paisley

Deep Gaussian processes (DGPs) provide a robust paradigm in Bayesian deep learning. In DGPs, a set of sparse integration locations called inducing points are selected to approximate the posterior distribution of the model. This is done to reduce computational complexity and improve model efficiency. However, inferring the posterior distribution of inducing points is not straightforward. Traditional variational inference techniques methods to approximate the posterior often leads to significant bias. To address this issue, we propose an alternative named Denoising Diffusion Variational Inference (DDVI) that utilizes a denoising diffusion stochastic differential equation (SDE) for generating posterior samples of inducing variables. We refer to the score matching method in the denoising diffusion model to approximate challenging score functions using a neural network. Furthermore, by combining classical mathematical theory of SDE with the minimization of KL divergence between the approximate and true processes, we propose a novel explicit variational lower bound for the marginal likelihood function of DGP. Through extensive experiments on various datasets and comparisons with baseline methods, we empirically demonstrate the effectiveness of the DDVI method in posterior inference of inducing points for DGP models.

AAAI Conference 2023 Conference Paper

Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior

• Huangxing Lin
• Yihong Zhuang
• Xinghao Ding
• Delu Zeng
• Yue Huang
• Xiaotong Tu
• John Paisley

We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as an Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We apply IDDP to various denoising tasks using only observed corrupted data and show that it achieves better denoising results than other self-supervised denoising methods.

JBHI Journal 2020 Journal Article

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

• Liyan Sun
• Jiexiang Wang
• Yue Huang
• Xinghao Ding
• Hayit Greenspan
• John Paisley

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing a normal-looking image corresponding to one containing lesions, and thus requires a special design. Being able to provide a “normal” counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments. In the other aspect, the ANT-GAN model is also capable of producing highly realistic lesion-containing image corresponding to the healthy one, which shows the potential in data augmentation verified in our experiments.

UAI Conference 2020 Conference Paper

Risk Bounds for Low Cost Bipartite Ranking

• San Gultekin
• John Paisley

Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent the prohibitive sample cost, many recent work focus on stochastic gradient-based methods. In this paper we consider an alternative approach, which leverages the structure of the widely-adopted pairwise squared loss, to obtain a stochastic and low cost algorithm that does not require stochastic gradients or learning rates. Using a novel uniform risk bound—based on matrix and vector concentration inequalities—we show that the sample size required for competitive performance against the all-pairs batch algorithm does not have a quadratic dependence. Generalization bounds for both the batch and low cost stochastic algorithms are presented. Experimental results show significant speed gain against the batch algorithm, as well as competitive performance against state-of-the-art bipartite ranking algorithms on real datasets.

NeurIPS Conference 2019 Conference Paper

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI

• Tao Tu
• John Paisley
• Stefan Haufe
• Paul Sajda

Inferring effective connectivity between spatially segregated brain regions is important for understanding human brain dynamics in health and disease. Non-invasive neuroimaging modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are often used to make measurements and infer connectivity. However most studies do not consider integrating the two modalities even though each is an indirect measure of the latent neural dynamics and each has its own spatial and/or temporal limitations. In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data. Our method first identifies task-dependent and subject-dependent regions of interest (ROI) based on the analysis of fMRI data. Directed influences between the latent neural states at these ROIs are then modeled as a multivariate autogressive (MVAR) process driven by various exogenous inputs. The latent neural dynamics give rise to the observed scalp EEG measurements via a biophysically informed linear EEG forward model. We use a mean-field variational Bayesian approach to infer the posterior distribution of latent states and model parameters. The performance of the model was evaluated on two sets of simulations. Our results emphasize the importance of obtaining accurate spatial localization of ROIs from fMRI. Finally, we applied the model to simultaneously recorded EEG-fMRI data from 10 subjects during a Face-Car-House visual categorization task and compared the change in connectivity induced by different stimulus categories.

NeurIPS Conference 2019 Conference Paper

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

• Jeremiah Liu
• John Paisley
• Marianthi-Anna Kioumourtzoglou
• Brent Coull

Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. BNE augments a model’s prediction and distribution functions using Bayesian nonparametric machinery. It has a theoretical guarantee in that it robustly estimates the uncertainty patterns in the data distribution, and can decompose its overall predictive uncertainty into distinct components that are due to different sources of noise and error. We show that our method achieves accurate uncertainty estimates under complex observational noise, and illustrate its real-world utility in terms of uncertainty decomposition and model bias detection for an ensemble in predict air pollution exposures in Eastern Massachusetts, USA.

ICML Conference 2019 Conference Paper

Random Function Priors for Correlation Modeling

• Aonan Zhang
• John Paisley

The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n, \theta)$, where $\theta\mathrel{\mathop: }=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\theta_k$ used to express $X_n$. In this paper, we introduce random function priors for $Z_n$ for modeling correlations among its $K$ dimensions $Z_{n1}$ through $Z_{nK}$, which we call population random measure embedding (PRME). Our model can be viewed as a generalized paintbox model \cite{Broderick13} using random functions, and can be learned efficiently with neural networks via amortized variational inference. We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.

AAAI Conference 2018 Conference Paper

Compressed Sensing MRI Using a Recursive Dilated Network

• Liyan Sun
• Zhiwen Fan
• Yue Huang
• Xinghao Ding
• John Paisley

Compressed sensing magnetic resonance imaging (CS-MRI) is an active research topic in the field of inverse problems. Conventional CS-MRI algorithms usually exploit the sparse nature of MRI in an iterative manner. These optimizationbased CS-MRI methods are often time-consuming at test time, and are based on fixed transform bases or shallow dictionaries, which limits modeling capacity. Recently, deep models have been introduced to the CS-MRI problem. One main challenge for CS-MRI methods based on deep learning is the trade-off between model performance and network size. We propose a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters. We adopt dilated convolutions in each recursive block to aggregate multi-scale information within the MRI. We also adopt a modified shortcut strategy to help features flow into deeper layers. Experimental results show that the proposed RDN model achieves state-of-the-art performance in CS-MRI while using far fewer parameters than previously required.

ICML Conference 2018 Conference Paper

CRVI: Convex Relaxation for Variational Inference

• Ghazal Fazelnia
• John Paisley

We present a new technique for solving non-convex variational inference optimization problems. Variational inference is a widely used method for posterior approximation in which the inference problem is transformed into an optimization problem. For most models, this optimization is highly non-convex and so hard to solve. In this paper, we introduce a new approach to solving the variational inference optimization based on convex relaxation and semidefinite programming. Our theoretical results guarantee very tight relaxation bounds that get nearer to the global optimal solution than traditional coordinate ascent. We evaluate the performance of our approach on regression and sparse coding.

ICML Conference 2018 Conference Paper

Deep Bayesian Nonparametric Tracking

• Aonan Zhang
• John Paisley

Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model. For example, information in social networks may show change-point-like bursts that then diffuse with smooth dynamics. Powerful models such as deep neural networks learn smooth functions from data, but are not as well-suited (in off-the-shelf form) for discovering and explaining sparse, discrete and bursty dynamic patterns. Bayesian models can do this well by encoding the appropriate probabilistic assumptions in the model prior. We propose an integration of Bayesian nonparametric methods within deep neural networks for modeling irregular patterns in time-series data. We use a Bayesian nonparametrics to model change-point behavior in time, and a deep neural network to model nonlinear latent space dynamics. We compare with a non-deep linear version of the model also proposed here. Empirical evaluations demonstrates improved performance and interpretable results when tracking stock prices and Twitter trends.

IJCAI Conference 2018 Conference Paper

MEnet: A Metric Expression Network for Salient Object Segmentation

• Shulian Cai
• Jiabin Huang
• Delu Zeng
• Xinghao Ding
• John Paisley

Recent CNN-based saliency models have achieved excellent performance on public datasets, but most are sensitive to distortions from noise or compression. In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to overcome this drawback. We construct a topological metric space where the implicit metric is determined by a deep network. In this latent space, we can group pixels within an observed image semantically into two regions, based on whether they are in a salient region or a non-salient region in the image. We carry out all feature extractions at the pixel level, which makes the output boundaries of the salient object finely-grained. Experimental results show that the proposed metric can generate robust salient maps that allow for object segmentation. By testing the method on several public benchmarks, we show that the performance of MEnet achieves excellent results. We also demonstrate that the proposed method outperforms previous CNN-based methods on distorted images.

NeurIPS Conference 2017 Conference Paper

Variational Inference via $\chi$ Upper Bound Minimization

• Adji Bousso Dieng
• Dustin Tran
• Rajesh Ranganath
• John Paisley
• David Blei

Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. It posits a family of approximating distributions $q$ and finds the closest member to the exact posterior $p$. Closeness is usually measured via a divergence $D(q || p)$ from $q$ to $p$. While successful, this approach also has problems. Notably, it typically leads to underestimation of the posterior variance. In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$. CHIVI minimizes an upper bound of the model evidence, which we term the $\chi$ upper bound (CUBO). Minimizing the CUBO leads to improved posterior uncertainty, and it can also be used with the classical VI lower bound (ELBO) to provide a sandwich estimate of the model evidence. We study CHIVI on three models: probit regression, Gaussian process classification, and a Cox process model of basketball plays. When compared to expectation propagation and classical VI, CHIVI produces better error rates and more accurate estimates of posterior variance.

ICML Conference 2016 Conference Paper

Markov Latent Feature Models

• Aonan Zhang
• John Paisley

We introduce Markov latent feature models (MLFM), a sparse latent feature model that arises naturally from a simple sequential construction. The key idea is to interpret each state of a sequential process as corresponding to a latent feature, and the set of states visited between two null-state visits as picking out features for an observation. We show that, given some natural constraints, we can represent this stochastic process as a mixture of recurrent Markov chains. In this way we can perform correlated latent feature modeling for the sparse coding problem. We demonstrate two cases in which we define finite and infinite latent feature models constructed from first-order Markov chains, and derive their associated scalable inference algorithms. We show empirical results on a genome analysis task and an image denoising task.

ICML Conference 2015 Conference Paper

Landmarking Manifolds with Gaussian Processes

• Dawen Liang
• John Paisley

We present an algorithm for finding landmarks along a manifold. These landmarks provide a small set of locations spaced out along the manifold such that they capture the low-dimensional non-linear structure of the data embedded in the high-dimensional space. The approach does not select points directly from the dataset, but instead we optimize each landmark by moving along the continuous manifold space (as approximated by the data) according to the gradient of an objective function. We borrow ideas from active learning with Gaussian processes to define the objective, which has the property that a new landmark is "repelled" by those currently selected, allowing for exploration of the manifold. We derive a stochastic algorithm for learning with large datasets and show results on several datasets, including the Million Song Dataset and articles from the New York Times.

ICML Conference 2015 Conference Paper

Markov Mixed Membership Models

• Aonan Zhang
• John Paisley

We present a Markov mixed membership model (Markov M3) for grouped data that learns a fully connected graph structure among mixing components. A key feature of Markov M3 is that it interprets the mixed membership assignment as a Markov random walk over this graph of nodes. This is in contrast to tree-structured models in which the assignment is done according to a tree structure on the mixing components. The Markov structure results in a simple parametric model that can learn a complex dependency structure between nodes, while still maintaining full conjugacy for closed-form stochastic variational inference. Empirical results demonstrate that Markov M3 performs well compared with tree structured topic models, and can learn meaningful dependency structure between topics.

JMLR Journal 2013 Journal Article

Stochastic Variational Inference

• Matthew D. Hoffman
• David M. Blei
• Chong Wang
• John Paisley

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

ICML Conference 2012 Conference Paper

Variational Bayesian Inference with Stochastic Search

• John Paisley
• David M. Blei
• Michael I. Jordan

ICML Conference 2011 Conference Paper

Variational Inference for Stick-Breaking Beta Process Priors

• John Paisley
• Lawrence Carin
• David M. Blei

ICML Conference 2010 Conference Paper

A Stick-Breaking Construction of the Beta Process

• John Paisley
• Aimee K. Zaas
• Christopher W. Woods
• Geoffrey S. Ginsburg
• Lawrence Carin

NeurIPS Conference 2009 Conference Paper

Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations

• Mingyuan Zhou
• Haojun Chen
• Lu Ren
• Guillermo Sapiro
• Lawrence Carin
• John Paisley

Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and compressive sensing (CS). The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size. The Dirichlet process and a probit stick-breaking process are also considered to exploit structure within an image. The proposed method can learn a sparse dictionary in situ; training images may be exploited if available, but they are not required. Further, the noise variance need not be known, and can be non-stationary. Another virtue of the proposed method is that sequential inference can be readily employed, thereby allowing scaling to large images. Several example results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches.

ICML Conference 2009 Conference Paper

Nonparametric factor analysis with beta process priors

• John Paisley
• Lawrence Carin

We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BP-FA) model allows for a dataset to be decomposed into a linear combination of a sparse set of factors, providing information on the underlying structure of the observations. As with the Dirichlet process, the beta process is a fully Bayesian conjugate prior, which allows for analytical posterior calculation and straightforward inference. We derive a varia-tional Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets.