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Ruizhi Deng

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4 papers

1 author row

TMLR Journal 2026 Journal Article

ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processes

Amartya Mukherjee
Ruizhi Deng
He Zhao
Yuzhen Mao
Leonid Sigal
Frederick Tung

This work introduces a diffusion model-based approach to modelling temporal point processes via an asynchronous noise schedule. Existing methods typically rely on parametric conditional intensity functions or autoregressive next-event prediction, which can limit distributional expressivity and make long-horizon forecasting computationally expensive. We address this limitation by using diffusion models to learn the joint distribution of event sequences in latent space without imposing restrictive parametric assumptions. At each step of the diffusion process, the noise schedule injects noise of varying scales into different parts of the data. With a careful design of the noise schedules, earlier events are generated faster than later ones, thus providing stronger conditioning for forecasting the more distant future. We derive an objective to effectively train these models for a general family of noise schedules based on conditional flow matching. Our method models the joint distribution of the latent representations of events in a sequence and achieves state-of-the-art results in predicting both the next inter-event time and event type on benchmark datasets. Additionally, it flexibly accommodates varying lengths of observation and prediction windows in different forecasting settings by adjusting the starting and ending points of the generation process. Finally, our method shows strong performance in long horizon prediction tasks, outperforming existing baseline methods.

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NeurIPS Conference 2023 Conference Paper

PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models

Jiacheng Chen
Ruizhi Deng
Yasutaka Furukawa

This paper presents \textit{PolyDiffuse}, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data. The task of structured reconstruction poses two fundamental challenges to DM: 1) A structured geometry is a ''set'' (e. g. , a set of polygons for a floorplan geometry), where a sample of $N$ elements has $N! $ different but equivalent representations, making the denoising highly ambiguous; and 2) A ''reconstruction'' task has a single solution, where an initial noise needs to be chosen carefully, while any initial noise works for a generation task. Our technical contribution is the introduction of a Guided Set Diffusion Model where 1) the forward diffusion process learns \textit{guidance networks} to control noise injection so that one representation of a sample remains distinct from its other permutation variants, thus resolving denoising ambiguity; and 2) the reverse denoising process reconstructs polygonal shapes, initialized and directed by the guidance networks, as a conditional generation process subject to the sensor data. We have evaluated our approach for reconstructing two types of polygonal shapes: floorplan as a set of polygons and HD map for autonomous cars as a set of polylines. Through extensive experiments on standard benchmarks, we demonstrate that PolyDiffuse significantly advances the current state of the art and enables broader practical applications. The code and data are available on our project page: https: //poly-diffuse. github. io.

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NeurIPS Conference 2021 Conference Paper

Continuous Latent Process Flows

Ruizhi Deng
Marcus A. Brubaker
Greg Mori
Andreas Lehrmann

Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynamics is not only promising at an intuitive level but also has practical benefits, including the ability to generate continuous trajectories and to perform inference on previously unseen time stamps. Despite exciting progress in this area, the existing models still face challenges in terms of their representational power and the quality of their variational approximations. We tackle these challenges with continuous latent process flows (CLPF), a principled architecture decoding continuous latent processes into continuous observable processes using a time-dependent normalizing flow driven by a stochastic differential equation. To optimize our model using maximum likelihood, we propose a novel piecewise construction of a variational posterior process and derive the corresponding variational lower bound using trajectory re-weighting. Our ablation studies demonstrate the effectiveness of our contributions in various inference tasks on irregular time grids. Comparisons to state-of-the-art baselines show our model's favourable performance on both synthetic and real-world time-series data.

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NeurIPS Conference 2020 Conference Paper

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

Ruizhi Deng
Bo Chang
Marcus A. Brubaker
Greg Mori
Andreas Lehrmann

Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the continuous-time Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.

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