A Theory for Conditional Generative Modeling on Multiple Data Sources

• URL: http://arxiv.org/abs/2502.14583v1
• Date: Thu, 20 Feb 2025 14:13:24 GMT
• Title: A Theory for Conditional Generative Modeling on Multiple Data Sources
• Authors: Rongzhen Wang, Yan Zhang, Chenyu Zheng, Chongxuan Li, Guoqiang Wu,
• Abstract summary: This paper takes the first step toward a rigorous analysis of multi-source training in conditional generative modeling. Our result shows that when source distributions share certain similarities and the model is expressive enough, multi-source training guarantees a sharper bound than single-source training.
• Score: 20.539424639329564
• License:
• Abstract: The success of large generative models has driven a paradigm shift, leveraging massive multi-source data to enhance model capabilities. However, the interaction among these sources remains theoretically underexplored. This paper takes the first step toward a rigorous analysis of multi-source training in conditional generative modeling, where each condition represents a distinct data source. Specifically, we establish a general distribution estimation error bound in average total variation distance for conditional maximum likelihood estimation based on the bracketing number. Our result shows that when source distributions share certain similarities and the model is expressive enough, multi-source training guarantees a sharper bound than single-source training. We further instantiate the general theory on conditional Gaussian estimation and deep generative models including autoregressive and flexible energy-based models, by characterizing their bracketing numbers. The results highlight that the number of sources and similarity among source distributions improve the advantage of multi-source training. Simulations and real-world experiments validate our theory. Code is available at: \url{https://github.com/ML-GSAI/Multi-Source-GM}.

Related papers

• Generative Distribution Prediction: A Unified Approach to Multimodal Learning [4.3108820946281945]
  We introduce Generative Distribution Prediction (GDP) to enhance predictive performance across structured and unstructured modalities. GDP is model-agnostic, compatible with any high-fidelity generative model, and supports transfer learning for domain adaptation. We empirically validate GDP on four supervised learning tasks-tabular data prediction, question answering, image captioning, and adaptive quantile regression-demonstrating its versatility and effectiveness across diverse domains.
  arXiv  Detail & Related papers  (2025-02-10T22:30:35Z)
• Bridging the inference gap in Mutimodal Variational Autoencoders [6.246098300155483]
  Multimodal Variational Autoencoders offer versatile and scalable methods for generating unobserved modalities from observed ones. Recent models using mixturesof-experts aggregation suffer from theoretically grounded limitations that restrict their generation quality on complex datasets. We propose a novel interpretable model able to learn both joint and conditional distributions without introducing mixture aggregation.
  arXiv  Detail & Related papers  (2025-02-06T10:43:55Z)
• Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory [87.00653989457834]
  Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. Despite the empirical success, theory of conditional diffusion models is largely missing. This paper bridges the gap by presenting a sharp statistical theory of distribution estimation using conditional diffusion models.
  arXiv  Detail & Related papers  (2024-03-18T17:08:24Z)
• CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions [42.293444710522294]
  Continual Multi-source Adaptation to Dynamic Distributions (CONTRAST) is a novel method that optimally combines multiple source models to adapt to the dynamic test data. We show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting.
  arXiv  Detail & Related papers  (2024-01-04T22:23:56Z)
• Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation [56.620403243640396]
  Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly when handling out-of-distribution (OoD) data. We develop a simple yet effective method called Generative Interpolation to fuse generative models trained from multiple domains for synthesizing diverse OoD samples.
  arXiv  Detail & Related papers  (2023-07-23T03:53:53Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• Bayesian predictive modeling of multi-source multi-way data [0.0]
  We consider molecular data from multiple 'omics sources as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence. We show that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients.
  arXiv  Detail & Related papers  (2022-08-05T21:58:23Z)
• Distributional Reinforcement Learning for Multi-Dimensional Reward Functions [91.88969237680669]
  We introduce Multi-Dimensional Distributional DQN (MD3QN) to model the joint return distribution from multiple reward sources. As a by-product of joint distribution modeling, MD3QN can capture the randomness in returns for each source of reward. In experiments, our method accurately models the joint return distribution in environments with richly correlated reward functions.
  arXiv  Detail & Related papers  (2021-10-26T11:24:23Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.