Conditional Generative Modeling

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29 papers in the last four weeks, up 142% on the four weeks before. 0.3% of all new papers.

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OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Oct 7, 2026Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4Causal Effect Estimation Causal Inference
Novartis · Barcelona Supercomputing Center · Universitat Polit`ecnica de Catalunya +2
HuLiGen: Human LiDAR Generation from Parametric Body Models
Oct 7, 2026Salma Galaaoui, Nermin Samet, David PicardPoint Cloud Generation Conditional Generative Modeling
Valeo.ai, Paris, France · LIGM, CNRS, Univ Gustave Eiffel, ENPC, Institut Polytechnique de Paris, Marne-la-Vall´ee, France
Relational Abstractions for Spatial Reasoning with Diffusion Models
Oct 7, 2026Ana Ezquerro, Ozan ÖzdenizciSpatial Reasoning Diffusion Models
Institute of Machine Learning and Neural Computation Graz University of Technology, Austria
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Oct 5, 2026Perrine Chassat, Agathe GuillouxStochastic Differential Equations Synthetic Data Generation
Inria, Université Paris Cité, Inserm, HeKA F-75015 Paris, France
Conditional Flow Matching for Single-Neuron Electrophysiology: Capturing Multimodal Responses Across Stimuli
Oct 5, 2026Cameron Schofield, Luca Ghafourpour, Philip H. Wong +2Neural Surrogate Modeling Conditional Flow Matching
University of Cambridge · Cedars-Sinai Medical Center · California Institute of Technology +1
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Oct 5, 2026Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke +2Probabilistic Forecasting Stochastic Interpolants
Vrije Universiteit Amsterdam · University of Amsterdam · Delft University of Technology +1
Benchmarking Generative Trajectory Models for Active-Inference Control
Oct 5, 2026Yulin Li, Mohsen A. Jafari, Andrea MattaActive Inference Generative Modeling
Rutgers University–New Brunswick, Piscataway NJ 08854, USA · Politecnico di Milano, Via La Masa 1, 20156 Milano, Italy
Diffusion Transformers are Provably Optimal In-context Generators
Oct 4, 2026Guoji Fu, Tomoya Wakayama, Ryotaro Kawata +3Diffusion Transformer Diffusion Sampling
National University of Singapore · RIKEN AIP · The University of Tokyo +1
Generalized Engression Models
Oct 1, 2026Xinwei Shen, Zijian Guo, Francis BachConditional Distribution Estimation Conditional Generative Modeling
Department of Statistics, University of Washington · Center for Data Science, Zhejiang University · Inria, ´Ecole Normale Sup´erieure, PSL Research University
Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Oct 1, 2026Yunrui Guan, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanFlow Matching Causal Counterfactual Generation
Department of Applied Mathematics and Statistics, Johns Hopkins University. · Amazon · Department of Statistics, University of California, Davis.
Benchmarking Generative Models for Near-Surface Data Assimilation on Real Station Observations
Sep 30, 2026Ruizhe Huang, Qidong Yang, Jonathan Giezendanner +1Flow Matching Data Assimilation
Massachusetts Institute of Technology, Cambridge, MA, USA
Grand Canonical Generators
Sep 30, 2026Andreas Burger, Malte Franke, Luka Mucko +2Generative Modeling Molecular Generation
University of Toronto NVIDIA Vector Institute Toronto, Canada · ETH Zurich NCCR Catalysis Zurich, Switzerland · University of Toronto NVIDIA Vector Institute Acceleration Consortium CIFAR Toronto, Canada
Mixture of Decoders for Diverse Dialog Response Generation
Sep 30, 2026Wenchao DuSpoken Dialogue Systems Diverse Text Generation
Microsoft Corporation Redmond, WA 98052
Scaling Collider Event Generation with Residual-Quantized Tokens
Sep 30, 2026Dan Godi, Dmitrii Kobylianskii, Eilam GrossAutoregressive Generation Conditional Generative Modeling
Weizmann Institute of Science, Rehovot, Israel
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
Sep 30, 2026Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan +8Crystal Structure Prediction Crystal Structure Generation
Center for Soft Matter Research, Department of Physics, New York University, New York 10003, USA · Simons Center for Computational Physical Chemistry, Department of Chemistry, New York University, New York 10003, USA · Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA +7
Conditional Generation of Creative Chess Puzzles with Diffusion Models
Sep 29, 2026Aatu Selkee, Severi Rissanen, Xidong Feng +2Masked Diffusion Models Computational Creativity
Aalto University · Google DeepMind
Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
Sep 29, 2026Umer Gupta, Saku Peltonen, Martin RitzertGraph Generation Autoregressive Generation
Independent Researcher London, United Kingdom · ETH Zurich Zurich, Switzerland · Leipzig University Leipzig, Germany
Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models
Sep 29, 2026Zhiyuan Li, Wenyan Yang, Pekka Marttinen +1Motion Retargeting Generative Modeling
Department of Electrical Engineering and Automation, Aalto University, Finland · Department of Computer Science, Aalto University, Finland
DrawingsDreamer: A Unified Multi-View Engineering Drawings Generation Model
Sep 28, 2026Shurui Liu, Weide Chen, Changwang Yi +1Generative Modeling Autoregressive Generation
School of Computer Science and Engineering, Sun Yat-sen University, China · School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, China
Simulating Respondents, Not Single Questions: Coherent Survey Generation with Large Language Models
Sep 28, 2026Ji Huang, Mengfei Li, Shuai ShaoLarge Language Model-Based Social Simulation Conditional Generative Modeling
School of Computer Science, University of Science and Technology of China · School of Management, Fudan University
Livin' on a Prior: Likelihood Score Approximation for Inverse Problems
Sep 28, 2026Rostislav Makarov, Tal Peer, Danilo de Oliveira +1Conditional Generative Modeling Inverse Problems
Signal Processing University of Hamburg Hamburg, Germany
GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling
Sep 28, 2026Moshe Eliasof, Eldad HaberConditional Flow Matching Geospatial Reasoning
Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Israel · Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Canada
Pulseflow: PPG Counterfactual Generation Via Latent Transport
Sep 27, 2026Hung Manh Pham, Dong Ma, Bin Zhu +1Counterfactual Data Augmentation Photoplethysmography
University of Cambridge, United Kingdom
Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation
Sep 24, 2026Haojin Li, Anbang Zhang, Wai Ho Mow +3Flow Matching Synthetic Data Augmentation
University of Science and Technology Beijing, China · Department of ECE, The Hong Kong University of Science and Technology, Hong Kong · University of Exeter +1
Sufficiently Reduced Distributional Regression
Sep 24, 2026Alexander Henzi, Tiange Liu, Xinwei ShenConditional Distribution Estimation Inverse Dimensionality Reduction
Tsinghua University, Department of Statistics and Data Science · University of Washington
Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
Sep 23, 2026Gabriele Gianuzzo, Stefan Dasbach, Fleur Hendriks +2Neural Surrogate Modeling Surrogate Modeling
Eindhoven University of Technology · DIFFER
One-Step Generative Surrogate Models via Block-Triangular Joint Drifting
Sep 22, 2026Nicholas Geissler, Shreya Jha, Ricardo Baptista +1Surrogate Modeling One-Step Generative Modeling
Courant Institute of Mathematical Sciences, New York University · Statistical Sciences, University of Toronto · Institute of Mathematics, EPFL
ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation
Sep 21, 2026Subo Wei, Jianqi Gao, Mingyan Fan +3Time Series Generation Autoregressive Generation
School of Computer Engineering and Science, Shanghai University, Shanghai, China · Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression
Sep 20, 2026Wenxi Tan, Bing Li, Lingzhou XueConditional Distribution Estimation Inverse Dimensionality Reduction
Department of Statistics, The Pennsylvania State University
Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning
Sep 17, 2026Maximilian Negedly, Sebastian Falkner, Alessandro Coretti +1Conditional Generative Modeling
Faculty of Physics, University of Vienna, 1090 Vienna, Austria · Vienna Doctoral School in Physics, University of Vienna, 1090 Vienna, Austria · Institute of Physics, University of Augsburg, 86159 Augsburg, Germany

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OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments

Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess, Eduard Serrahima de Cambra+3

Novartis · Barcelona Supercomputing Center · Universitat Polit`ecnica de Catalunya+2

Causal Effect Estimation Causal Inference Doubly Robust Estimation Conditional Generative Modeling

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Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences). However, this task is challenging because of time-varying confounding, yet existing adjustment strategies for this task are limited. In this paper, we aim to learn CDPOs under time-varying treatments using flexible generative models. Our contributions are two-fold. (1) We introduce a tailored adjustment strategy for our setting, namely, generative recursive g-computation. Our adjustment strategy recursively propagates full conditional outcome distributions rather than conditional means, modeling the variables of interest directly rather than full trajectories. Building on our adjustment strategy, we formulate simple generative learners for CDPO estimation. However, these learners can be sensitive to nuisance estimation errors, which motivates an orthogonal learner. (2) We thus introduce OrthoGen, a Neyman-orthogonal and doubly robust generative learner. Importantly, we show that OrthoGen further achieves rate double robustness and quasi-oracle efficiency under suitable conditions. Our learners are flexible and can be instantiated with different generative backbones (e.g., normalizing flows and diffusion models). Across experiments with synthetic, semi-synthetic and real-world datasets, we find that OrthoGen is highly effective. To the best of our knowledge, we are the first to propose a generative orthogonal learner for estimating CDPOs under time-varying treatments.

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Latest papers 258