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Tag: robust

  • Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise

    Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise arXiv:2602.10530v1 Announce Type: new Abstract: Multiview datasets are common in scientific and engineering applications, yet existing fusion methods offer limited theoretical guarantees, particularly in the presence of heterogeneous and high-dimensional noise. We propose Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM), a new kernel-based diffusion…

  • Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation

    Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation arXiv:2601.15360v1 Announce Type: new Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it…

  • Distributionally Robust Online Markov Game with Linear Function Approximation

    Distributionally Robust Online Markov Game with Linear Function Approximation arXiv:2511.07831v1 Announce Type: new Abstract: The sim-to-real gap, where agents trained in a simulator face significant performance degradation during testing, is a fundamental challenge in reinforcement learning. Extansive works adopt the framework of distributionally robust RL, to learn a policy that acts robustly under worst case…

  • Robust Multi-Manifold Clustering via Simplex Paths

    Robust Multi-Manifold Clustering via Simplex Paths arXiv:2507.10710v1 Announce Type: new Abstract: This article introduces a novel, geometric approach for multi-manifold clustering (MMC), i.e. for clustering a collection of potentially intersecting, d-dimensional manifolds into the individual manifold components. We first compute a locality graph on d-simplices, using the dihedral angle in between adjacent simplices as the…

  • Wasserstein Distributionally Robust Nonparametric Regression

    Wasserstein Distributionally Robust Nonparametric Regression arXiv:2505.07967v1 Announce Type: new Abstract: Distributionally robust optimization has become a powerful tool for prediction and decision-making under model uncertainty. By focusing on the local worst-case risk, it enhances robustness by identifying the most unfavorable distribution within a predefined ambiguity set. While extensive research has been conducted in parametric settings,…

  • Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning

    Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning arXiv:2504.03784v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies…

  • Achievable distributional robustness when the robust risk is only partially identified

    Achievable distributional robustness when the robust risk is only partially identified arXiv:2502.02710v1 Announce Type: new Abstract: In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk. Invariance-based algorithms can provably take advantage of structural assumptions on the shifts when the training distributions are heterogeneous enough…

  • Median of Forests for Robust Density Estimation

    Median of Forests for Robust Density Estimation arXiv:2501.15157v1 Announce Type: new Abstract: Robust density estimation refers to the consistent estimation of the density function even when the data is contaminated by outliers. We find that existing forest density estimation at a certain point is inherently resistant to the outliers outside the cells containing the point,…

  • Robust Multi-Dimensional Scaling via Accelerated Alternating Projections

    Robust Multi-Dimensional Scaling via Accelerated Alternating Projections arXiv:2501.02208v1 Announce Type: new Abstract: We consider the robust multi-dimensional scaling (RMDS) problem in this paper. The goal is to localize point locations from pairwise distances that may be corrupted by outliers. Inspired by classic MDS theories, and nonconvex works for the robust principal component analysis (RPCA) problem,…

  • Robust random graph matching in dense graphs via vector approximate message passing

    Robust random graph matching in dense graphs via vector approximate message passing arXiv:2412.16457v1 Announce Type: new Abstract: In this paper, we focus on the matching recovery problem between a pair of correlated Gaussian Wigner matrices with a latent vertex correspondence. We are particularly interested in a robust version of this problem such that our observation…

  • Adversarially robust generalization theory via Jacobian regularization for deep neural networks

    Adversarially robust generalization theory via Jacobian regularization for deep neural networks arXiv:2412.12449v1 Announce Type: new Abstract: Powerful deep neural networks are vulnerable to adversarial attacks. To obtain adversarially robust models, researchers have separately developed adversarial training and Jacobian regularization techniques. There are abundant theoretical and empirical studies for adversarial training, but theoretical foundations for Jacobian…