10563 papers
This paper introduces GALACTIC, a unified framework that bridges local and global counterfactual explainability for unsupervised time-series clustering by generating minimal perturbations to cross cluster boundaries and employing a provably efficient submodular optimization algorithm to derive concise, non-redundant global summaries of these transitions.
The paper proposes FairFinGAN, a WGAN-based framework that integrates fairness constraints via a classifier to generate synthetic financial data that effectively mitigates bias against protected attributes while maintaining high utility for downstream predictive tasks.
This paper demonstrates that predictive inference is governed by four distinct, pairwise non-nested admissibility geometries—Blackwell risk dominance, anytime-valid supermartingales, marginal coverage, and Cesàro approachability—each offering a unique certificate of optimality and proving that admissibility is irreducibly relative to the chosen criterion rather than a universal property.
This paper establishes a comprehensive statistical theory for globally optimal empirical risk minimization decision trees by deriving sharp oracle inequalities and minimax optimal rates over a novel piecewise sparse heterogeneous anisotropic Besov space, thereby providing rigorous theoretical guarantees for their performance in high-dimensional regression and classification under both sub-Gaussian and heavy-tailed noise settings.
This paper proposes Geometric-Aware Quantization (GAQ), a framework that enables efficient, low-bit inference for SO(3)-equivariant Graph Neural Networks by decoupling magnitude and direction to rigorously preserve continuous symmetry, thereby achieving significant speedups and memory reductions on molecular simulation benchmarks without compromising physical consistency.
This paper proposes InfoFlow KV, an information-flow-aware method that uses attention-norm signals and global positional reordering to selectively recompute key-value caches, thereby improving the efficiency and accuracy of retrieval-augmented generation for long-context tasks.
This paper introduces TS-BOSS, a scalable, score-based algorithm for learning causal structures in multivariate time series that extends the Best Order Score Search framework with dynamic Bayesian networks and grow-shrink trees, demonstrating superior performance in high auto-correlation regimes compared to standard constraint-based methods.
This paper proposes a novel deep adversarial framework that explicitly integrates inter-subject variability to learn subject-invariant feature representations, thereby significantly improving generalization and classification performance in inertial sensor-based Human Activity Recognition across unseen individuals.
The paper introduces TopKGraphs, a robust and interpretable method for estimating node similarity that combines Jaccard-biased random walks with rank aggregation to outperform standard similarity measures and embedding-based approaches across diverse network types.
This paper challenges the necessity of learnable restriction maps in Sheaf Neural Networks by demonstrating that a simpler baseline with fixed identity maps achieves comparable performance on heterophilic graphs and does not suffer from the oversmoothing predicted by theoretical diffusion analysis.
This paper provides a comprehensive statistical review of synthetic data generated by modern AI models, outlining their benefits and limitations while offering principled frameworks and practical recommendations to ensure their valid and reliable use in scientific inference and prediction.
The paper introduces MobileFetalCLIP, a framework utilizing Selective Repulsive Knowledge Distillation to train a compact 11.4M parameter student model that outperforms its 304M parameter teacher in fetal ultrasound analysis while enabling real-time deployment on mobile devices.
This paper proposes an inherently interpretable, prototype-based neural network for medical tabular data that learns human-readable, discretized feature subsets to provide transparent, case-based clinical predictions while maintaining competitive accuracy.
This paper introduces a unified framework for ensembling diverse language models via -ensemble distributions and proposes a byte-level sequential Monte Carlo algorithm to sample from these distributions, effectively overcoming challenges like mismatched vocabularies and biased approximations to improve performance on structured text generation tasks.
The paper introduces OPSDC, an on-policy self-distillation method that trains reasoning models to generate more concise outputs by minimizing reverse KL divergence against their own "be concise" instructions, achieving significant token reduction and accuracy improvements on benchmarks like MATH-500 and AIME 2024 without requiring ground-truth answers or token budgets.
This paper introduces Latent Wasserstein Adversarial Imitation Learning (LWAIL), a novel framework that leverages a pre-trained, dynamics-aware latent space to achieve expert-level performance using only one or a few state-only expert demonstrations, thereby overcoming the data and action requirements of traditional imitation learning methods.
This paper proposes a physically constrained neural sequence learning framework that employs a Kraus-structured output layer to guarantee completely positive trace-preserving (CPTP) quantum state updates, demonstrating that a Kraus-LSTM architecture significantly outperforms unconstrained models and other backbones in reconstructing quantum trajectories under parameter drift.
This paper establishes a unified thermodynamic response framework for singular Bayesian models, demonstrating that posterior tempering induces a hierarchy of observables that naturally interpret complex learning-theoretic quantities like the real log canonical threshold and WAIC as free-energy derivatives, thereby revealing phase-transition-like structural reorganizations in models such as neural networks and Gaussian mixtures.
This paper introduces SurvHTE-Bench, the first comprehensive benchmark for evaluating heterogeneous treatment effect estimation in survival analysis, featuring a diverse suite of synthetic, semi-synthetic, and real-world datasets to enable rigorous, fair, and reproducible comparisons of causal survival methods.
This paper demonstrates that reasoning models often exhibit "performative chain-of-thought" by generating tokens without revealing their internal beliefs, yet activation probing can detect these hidden certainties early to enable significant token reduction while distinguishing genuine uncertainty in complex tasks.