arXiv Machine Learning
Jun 9
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
Calibrating Generative Models to Feature Distributions with MMD Finetuning
arXiv:2606. 19496v1 Announce Type: new Abstract: Generative models can produce individually plausible samples while deviating substantially from a target set in the distribution of key features.
Related stories
arXiv Machine Learning
Oct 19
Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces
arXiv:2610.22763v1 Announce Type: new Abstract: Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps...
arXiv AI
Jun 10
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
arXiv AI
Oct 1
Fenchel Tilting: Weighted Correction for Efficient Finetuning of Generative Models
Fenchel Tilt Flow Control (FTFC) is a new method for fine‑tuning pretrained generative models to arbitrary preference functions. It decouples utility optimization from model fitting by first learning reward and density‑ratio weights on pretrained samples, then freezing these weights to adjust a diffusion or flow model in a single importance‑weighted stage. The approach supports general f‑divergence penalties, achieves exact duality for concave utilities, and demonstrates up to 20× efficiency gains while outperforming baselines on image and molecule generation tasks.
arXiv Machine Learning
Oct 15
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
arXiv:2610.19123v1 Announce Type: new Abstract: Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation,...
arXiv Machine Learning
Oct 9
Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning
The paper introduces RefineMix, a framework for training discrete diffusion models when data is scarce, common in scientific settings. RefineMix strategically incorporates out‑of‑distribution data at specific diffusion times to enhance generalization without distorting the sampling distribution. Experiments across five domain‑shift scenarios show that RefineMix matches or surpasses in‑domain fine‑tuning, and in protein sequence generation it nearly doubles the proportion of novel, foldable, in‑family proteins produced from just 197 in‑domain examples.
arXiv Machine Learning
Oct 28
Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel
The paper introduces the transport Neural Tangent Kernel (tNTK) to enable active learning for conditional generative models that produce distributional outputs. By decomposing the Wasserstein test risk into mean and shape components, the authors show that mean‑based active learning misses distributional shape, and propose tNTK as a tractable kernel that captures sensitivity of the predicted distribution to model parameters. Using tNTK as a Gaussian process covariance, they greedily select inputs that minimize posterior variance, and demonstrate that this distribution‑aware strategy outperforms mean‑based NTK baselines across several generative model types and datasets.
arXiv Machine Learning
Sep 29
Distribution-Conditioned Transport
The paper introduces Distribution‑Conditioned Transport (DCT), a framework that learns transport maps conditioned on embeddings of source and target distributions, allowing generalization to unseen distribution pairs. DCT supports semi‑supervised learning for distributional forecasting by leveraging distributions observed at only one condition. It is agnostic to the transport mechanism and is demonstrated on synthetic benchmarks and four biological applications, including batch effect transfer in single‑cell genomics and modeling T‑cell receptor sequence evolution.
arXiv Machine Learning
Aug 17
Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure
arXiv:2608. 14293v1 Announce Type: cross Abstract: High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible.
arXiv Machine Learning
Jul 21
GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures
arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?
arXiv Machine Learning
Jul 1
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
arXiv:2606. 31576v1 Announce Type: new Abstract: The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomolecule generation.
arXiv Machine Learning
Jun 2
Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.