2315 papers
This paper proposes a novel fairness-aware data repair framework that utilizes a Bayesian nonparametric stopping rule to learn robust optimal transport operators, thereby overcoming representation bias in underrepresented subgroups and enabling effective repairs on out-of-sample archival data while balancing fairness against data distortion.
This paper proposes an unsupervised representation learning framework that factorizes latent variable transformations into sparse rotational and potential flow fields, enabling the model to learn disentangled representations based on independent transformation primitives while achieving state-of-the-art performance in data likelihood and equivariance on sequence data.
This paper proposes a data-driven framework combining control barrier functions and differentiable optimization to learn interpretable responsibility allocations, enabling autonomous agents to quantify and adjust their behaviors for safe, socially-aware multi-agent interactions.
This paper proposes a novel machine learning framework that combines gradient descent on the Grassmannian manifold with Donaldson's algorithm to efficiently compute Ricci-flat approximations of Calabi-Yau metrics, demonstrating its effectiveness and the emergence of nontrivial local minima on the Dwork family of threefolds.
This paper introduces Adaptive Importance Sampling and Stratified Subsampling estimators that achieve minimax-optimal rates for robust high-dimensional sparse regression under heavy-tailed noise, contamination, and temporal dependence, while also providing fully specified de-biasing procedures for valid confidence intervals and demonstrating superior empirical performance over uniform subsampling.
The paper introduces Scalable Message Passing Neural Networks (SMPNNs), a deep Graph Neural Network architecture that replaces computationally expensive attention mechanisms with standard convolutional message passing within a Pre-Layer Normalization Transformer-style block, achieving state-of-the-art performance on large graphs while theoretically addressing oversmoothing through the necessity of residual connections for universal approximation.
This paper proposes SPDIM, a parameter-efficient geometric deep learning framework that leverages information maximization on the symmetric positive definite (SPD) manifold to effectively address source-free unsupervised domain adaptation in EEG data, specifically overcoming the generalization limitations of prior methods when faced with label shifts.
This paper proposes an unsupervised prognostics framework that utilizes unlabeled run-to-failure data to simultaneously identify latent failure modes and select informative sensors, thereby enabling accurate remaining useful life prediction for autonomous deep-space habitats under multiple unknown failure conditions.
This paper introduces MS-HGNN, a morphological-symmetry-equivariant heterogeneous graph neural network that integrates robotic kinematic structures and symmetries as architectural constraints to achieve high generalizability and efficiency in learning dynamics for various multi-body systems, with its effectiveness validated through formal proofs and experiments on quadruped robots.
This paper introduces CuriousBot, a mobile exploration system that utilizes a 3D relational object graph to enable active interaction with diverse objects in complex environments, outperforming vision-language model-based approaches in both effectiveness and generalization.
This paper demonstrates that simple, local molecular fingerprints combined with LightGBM outperform complex graph neural networks and transformers across 132 peptide datasets, challenging the assumption that modeling long-range interactions is essential for accurate peptide function prediction.
This paper introduces Astromer 2, an enhanced foundational model for light curve analysis that leverages self-supervised pre-training on 1.5 million MACHO survey observations and weighted per-sample embeddings to significantly outperform its predecessor and prior models in classification tasks, particularly when trained with limited labeled data.
This paper proposes a unified framework to evaluate how personalization affects both prediction accuracy and explanation clarity in high-stakes domains, deriving statistical bounds to determine when such effects are detectable given specific dataset characteristics.
This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.
This paper presents an end-to-end Real2Sim2Real framework for deformable linear object manipulation that employs likelihood-free inference to estimate physical parameter distributions for domain-randomized reinforcement learning, enabling zero-shot deployment of visuomotor policies from simulation to the real world.
This paper introduces Feature Importance Rescaling (FIR), a theoretically grounded method that improves the reliability of cluster validity indices in noisy, high-dimensional Gaussian mixtures by attenuating irrelevant features, thereby strengthening the correlation between unsupervised evaluation metrics and ground truth.
This perspective proposes the concept of "functional units" as a critical bridge to reconcile traditional structure-property correlations with emerging data-driven AI paradigms, thereby advancing the understanding of material design and knowledge inheritance across diverse systems.
The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.
This paper presents mixed-integer linear programming formulations to construct globally optimal model trees with linear support vector machines at the leaves, demonstrating through extensive experiments that they achieve competitive accuracy with significantly smaller, more interpretable structures compared to greedy model trees and other standard machine learning algorithms.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.