arXiv:2509.09802v1 Announce Type: cross Abstract: We propose and study Sparse Polyak, a variant of Polyak’s adaptive step size, designed to solve high-dimensional statistical estimation problems where the problem dimension is allowed to grow much faster than the sample size. In…
arXiv:2509.10385v1 Announce Type: new Abstract: In this work, we explore differentially private synthetic data generation in a decentralized-data setting by building on the recently proposed Differentially Private Class-Centric Data Aggregation (DP-CDA). DP-CDA synthesizes data in a centralized setting by mixing…
arXiv:2210.13533v4 Announce Type: replace-cross Abstract: Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning. While learning invariant features across environments is a popular approach, it often assumes that these features are…
arXiv:2509.10393v1 Announce Type: cross Abstract: Several emerging post-Bayesian methods target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility introduces a computational challenge, as one loses access to an explicit unnormalised density for the target.…
arXiv:2402.02734v2 Announce Type: replace-cross Abstract: Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not…
arXiv:2509.10439v1 Announce Type: cross Abstract: Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute hardware (like mobile and other edge devices or distributed data centers). Communication becomes a major bottleneck in such settings…
arXiv:2303.03084v3 Announce Type: replace Abstract: We establish a statistical learning theoretical framework aimed at extrapolation, or out-of-domain generalization, on the unobserved tails of covariates in continuous regression problems. Our strategy involves performing statistical regression on a subsample of observations with…
arXiv:2509.03853v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) enables Bayesian analysis when the likelihood is intractable but model simulations are available. Recent advances in statistics and machine learning, including Approximate Bayesian Computation and deep generative models, have expanded the applicability…
arXiv:2412.20724v2 Announce Type: replace Abstract: This chapter presents the new family of soft diamond synaptic regularizers based on thick-tailed symmetric alpha stable $S{alpha}S$ probability bell curves. These new parametrized weight priors improved deep-learning performance on image and language-translation test sets…
arXiv:2509.08926v2 Announce Type: replace-cross Abstract: Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation. We address this challenge by reframing Re-ID as a supervised image similarity task and adopting a Siamese network…
