arXiv:2506.23396v2 Announce Type: replace Abstract: The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment — particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence…
arXiv:2509.16586v1 Announce Type: cross Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model. However, much less is known about the constrained average-reward MDP (CAMDP), where…
arXiv:2305.16189v5 Announce Type: replace-cross Abstract: Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is…
arXiv:2509.16959v1 Announce Type: cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby reducing the final model’s performance. To address this, we introduce a scheduler that computes gradient interference, constructs…
arXiv:2407.11678v3 Announce Type: replace-cross Abstract: In this paper, we focus on analyzing the excess risk of the unpaired data generation model, called CycleGAN. Unlike classical GANs, CycleGAN not only transforms data between two unpaired distributions but also ensures the mappings…
arXiv:2509.16974v1 Announce Type: cross Abstract: Wasserstein gradient flow (WGF) is a common method to perform optimization over the space of probability measures. While WGF is guaranteed to converge to a first-order stationary point, for nonconvex functionals the converged solution does…
arXiv:2509.17051v1 Announce Type: cross Abstract: Bayesian hyperparameter optimization relies heavily on Gaussian Process (GP) surrogates, due to robust distributional posteriors and strong performance on limited training samples. GPs however underperform in categorical hyperparameter environments or when assumptions of normality, heteroskedasticity…
arXiv:2411.05850v2 Announce Type: replace-cross Abstract: Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data,…
arXiv:2509.17154v1 Announce Type: cross Abstract: Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many scientific and engineering problems. In this work, we propose kernel-based methods for identifying and forecasting…
arXiv:2502.12013v3 Announce Type: replace-cross Abstract: Motivated by the burgeoning interest in cross-domain learning, we present a novel generative modeling challenge: generating counterfactual samples in a target domain based on factual observations from a source domain. Our approach operates within an…
