The paper introduces a scalable approach to learning Determinantal Point Processes (DPPs) by modeling full‑rank interactions through a nonlinear kernel function and latent features. It proposes a surrogate function for maximum likelihood estimation and employs stochastic optimization with random features of the nonlinear kernel. Experiments on real‑world datasets show that this method improves the trade‑off between expressive power and computational scalability, achieving better generalization with fewer parameters.
By Takahiro Kawashima, Hideitsu Hino
arXiv:2607. 21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather than points in a real space.
By Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga, Peter Mueller
The paper presents a variational learning framework that simultaneously infers non‑parametric interaction kernels and environmental or intra‑agent forces in collective dynamics. It extends existing methods to handle both interaction and environmental components, validating the approach on benchmark models such as synchronization, alignment, and attraction‑repulsion systems. A model‑selection procedure is also introduced to identify the best explanatory framework from trajectory data, enabling direct recovery of mechanistic interaction mechanisms.
By Nipuni de Silva, Ming Zhong, James M. Greene
CausalBind introduces a causal modeling framework for protein‑molecule virtual screening that focuses on sparse cross‑modality interactions rather than dense holistic alignment. By formalizing a V‑structure causal model with Heckman‑style selection, the authors prove that latent interaction concepts are identifiable only under sparsity constraints and propose three implementation variants. Experiments on DUD‑E and LIT‑PCBA benchmarks demonstrate that all variants outperform strong retrieval baselines, especially in early enrichment and out‑of‑distribution generalization.
By Loka Li, Jin Tian, Kun Zhang
arXiv:2610.15571v2 Announce Type: replace
Abstract: In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feat...
By Yuling Jiao, Lican Kang, Yang Xiang, Jerry Zhijian Yang, Fusheng Zhou
arXiv:2603. 23746v2 Announce Type: replace Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges.
By Zhitong Xu, Qiwei Yuan, Yinghao Chen, Yan Sun, Bin Shen, Shandian Zhe
The paper introduces Transformed Samplers with Variance Reduction, a method that extends exact control variate solutions from simple reference densities to general target distributions by learning a bijective change of variables, such as a normalizing flow. By training the flow to map the target close to the reference in latent space, the authors show that Markov kernels and their Poisson solutions transform accordingly, enabling explicit control variates in the latent space. The resulting estimator is consistent under mild tail conditions, and the approach also applies to importance sampling, with experiments demonstrating competitive performance against state‑of‑the‑art samplers and control variates.
By Siran Liu, Michalis Tisias, Petros Dellaportas
The paper introduces Pref‑MH, an exact Metropolis‑Hastings sampler that uses only stochastic binary pairwise comparisons from judges to sample from distributions conditioned on desired semantic properties. By linking the MH density ratio to the preference odds of the Bradley‑Terry model, the authors devise an accept/reject rule that guarantees convergence to the target distribution. Experiments on text, molecular, and image generation with large‑language‑model and vision‑language‑model judges show Pref‑MH as a practical, flexible method for conditional sampling when comparative feedback is readily available.
By Ariel Smogorghevski, Nir Rosenfeld, Yaniv Romano
arXiv:2609.36911v1 Announce Type: new
Abstract: In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometricall...
By Oskar Kviman
arXiv:2606. 19410v1 Announce Type: cross Abstract: Signed pairwise interaction scores fundamentally conflate uniqueness (U), redundancy (R), and synergy (S).
By Potito Aghilar, Sabino Roccotelli, Stanislao Fidanza, Vito Walter Anelli, Sebastiano Stramaglia, Tommaso Di Noia
arXiv:2606. 27014v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input level.
By Jingyi Cui, Qi Zhang, Hongwei Wen, Yisen Wang
Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-entropy distributions such as isotropic Gaussians,...