Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion

• URL: http://arxiv.org/abs/2312.13616v1
• Date: Thu, 21 Dec 2023 07:05:21 GMT
• Title: Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
• Abstract summary: We introduce Structured Counterfactual diffuser or SCD, the first plug-and-play framework leveraging diffusion for generating counterfactual explanations in structured data. Our experiments show that our counterfactuals not only exhibit high plausibility compared to the existing state-of-the-art but also show significantly better proximity and diversity.
• Score: 20.20945739504847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating counterfactual explanations is one of the most effective approaches for uncovering the inner workings of black-box neural network models and building user trust. While remarkable strides have been made in generative modeling using diffusion models in domains like vision, their utility in generating counterfactual explanations in structured modalities remains unexplored. In this paper, we introduce Structured Counterfactual Diffuser or SCD, the first plug-and-play framework leveraging diffusion for generating counterfactual explanations in structured data. SCD learns the underlying data distribution via a diffusion model which is then guided at test time to generate counterfactuals for any arbitrary black-box model, input, and desired prediction. Our experiments show that our counterfactuals not only exhibit high plausibility compared to the existing state-of-the-art but also show significantly better proximity and diversity.

Related papers

• Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech [6.877868518355945]
  Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data.<n>We study the properties of the structure of the latent space corresponding to discrete tokens expressed in terms of Kullback-Leibler divergence.<n>We find that FSQ tokenization scheme has the latent space structure with the properties that make it best suited for continuous diffusion for categorical data.
  arXiv  Detail & Related papers  (2026-06-08T14:41:24Z)
• CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data [13.773137893785984]
  CountsDiff is a diffusion framework designed to model distributions on the natural numbers.<n>We propose an initial instantiation of CountsDiff and validate it on natural image datasets.<n>We then highlight biological count assays as a natural use case, evaluating CountsDiff on single-cell RNA-seq imputation.
  arXiv  Detail & Related papers  (2026-04-04T16:06:14Z)
• Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction [54.95522167029998]
  This article is a self-contained primer on diffusion over general state spaces.<n>We develop the discrete-time view (forward noising via Markov kernels and learned reverse dynamics) alongside its continuous-time limits.<n>A common variational treatment yields the ELBO that underpins standard training losses.
  arXiv  Detail & Related papers  (2025-12-04T18:55:36Z)
• Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner [66.86440230599656]
  We argue that diffusion language models do not necessarily need to be in the discrete space.<n>In particular, we prove that continuous diffusion models have stronger expressivity than discrete diffusions and looped transformers.<n>We propose Coevolutionary Continuous Diffusion (CCDD), which defines a joint multimodal diffusion process on the union of a continuous representation space and a discrete token space.
  arXiv  Detail & Related papers  (2025-10-03T17:44:41Z)
• DS-Diffusion: Data Style-Guided Diffusion Model for Time-Series Generation [3.7098771725459336]
  We propose a data style-guided diffusion model (DS-Diffusion) for time series generation tasks.<n>The DS-Diffusion avoids retraining the entire framework to introduce conditional guidance.<n>The generated samples can clearly indicate the data style from which they originate.
  arXiv  Detail & Related papers  (2025-09-23T03:06:39Z)
• A Convergence Theory for Diffusion Language Models: An Information-Theoretic Perspective [8.15094483029656]
  Diffusion models enable parallel token sampling, leading to faster generation and eliminating left-to-right generation constraints.<n>We develop convergence guarantees for diffusion language models from an information-theoretic perspective.<n>These results offer novel theoretical insights into the practical effectiveness of diffusion language models.
  arXiv  Detail & Related papers  (2025-05-27T16:24:20Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes. Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering [11.161081261781659]
  We harness the power of a Diffusion Autoencoder to generate multiple distinct counterfactual explanations. By clustering in the latent space, we uncover the directions corresponding to the different modes within a class. We introduce a novel methodology, DifCluE, which consistently identifies these modes and produces more reliable counterfactual explanations.
  arXiv  Detail & Related papers  (2025-02-17T07:17:37Z)
• An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization [59.63880337156392]
  Diffusion models have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. Despite the significant empirical success, theory of diffusion models is very limited. This paper provides a well-rounded theoretical exposure for stimulating forward-looking theories and methods of diffusion models.
  arXiv  Detail & Related papers  (2024-04-11T14:07:25Z)
• Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement [58.9768112704998]
  Disentangled representation learning strives to extract the intrinsic factors within observed data. We introduce a new perspective and framework, demonstrating that diffusion models with cross-attention can serve as a powerful inductive bias. This is the first work to reveal the potent disentanglement capability of diffusion models with cross-attention, requiring no complex designs.
  arXiv  Detail & Related papers  (2024-02-15T05:07:54Z)
• Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors [56.82596340418697]
  We propose a simple yet effective framework comprising a pre-trained Stable Diffusion (SD) model containing rich generative priors, a unified head (U-head) capable of integrating hierarchical representations, and an adapted expert providing discriminative priors. Comprehensive investigations unveil potential characteristics of Vermouth, such as varying granularity of perception concealed in latent variables at distinct time steps and various U-net stages. The promising results demonstrate the potential of diffusion models as formidable learners, establishing their significance in furnishing informative and robust visual representations.
  arXiv  Detail & Related papers  (2024-01-29T10:36:57Z)
• Guided Diffusion from Self-Supervised Diffusion Features [49.78673164423208]
  Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or pretraining. We propose a framework to extract guidance from, and specifically for, diffusion models.
  arXiv  Detail & Related papers  (2023-12-14T11:19:11Z)
• A Comprehensive Survey on Generative Diffusion Models for Structured Data [0.0]
  generative diffusion models have achieved a rapid paradigm shift in deep generative models. Structured data has been received comparatively limited attention from the deep learning research community. This review serves as a catalyst for the research community, promoting developments in generative diffusion models for structured data.
  arXiv  Detail & Related papers  (2023-06-07T04:26:41Z)
• 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)
• Hierarchically branched diffusion models leverage dataset structure for class-conditional generation [0.6800113478497425]
  Branched diffusion models rely on the same diffusion process as traditional models, but learn reverse diffusion separately for each branch of a hierarchy. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets spanning many data modalities.
  arXiv  Detail & Related papers  (2022-12-21T05:27:23Z)
• A Survey on Generative Diffusion Model [75.93774014861978]
  Diffusion models are an emerging class of deep generative models. They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space. This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
  arXiv  Detail & Related papers  (2022-09-06T16:56:21Z)
• Let us Build Bridges: Understanding and Extending Diffusion Generative Models [19.517597928769042]
  Diffusion-based generative models have achieved promising results recently, but raise an array of open questions. This work tries to re-exam the overall framework in order to gain better theoretical understandings. We present 1) a first theoretical error analysis for learning diffusion generation models, and 2) a simple and unified approach to learning on data from different discrete and constrained domains.
  arXiv  Detail & Related papers  (2022-08-31T08:58:10Z)
• Diffusion Causal Models for Counterfactual Estimation [18.438307666925425]
  We consider the task of counterfactual estimation from observational imaging data given a known causal structure. We propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data.
  arXiv  Detail & Related papers  (2022-02-21T12:23:01Z)

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.