All papers

The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems.

The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks.

The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems.

The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth.

The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems.

The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points.

The paper develops a theoretical Rényi-divergence framework to measure inherent differential privacy guarantees obtained directly from Bayesian posterior sampling.

The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes.

The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders.

The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations.

The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways.

The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization.

The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement.

The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions.

Evaluates whether dependency graphs improve automated mapping between source code files and system architectures.

Provides theoretical convergence bounds for score-based generative models driven by heavy-tailed stable processes.

Proposes a sequential string notation to represent and generate valid graphs using standard sequence models.

Formulates clinical population health stratification as a multi-treatment causal ranking problem to prioritize interventions.

Uses supervised advantage conditioning to train reinforcement learning policies with improved stability.

Applies positive-unlabeled learning to establish mathematically exact negative guidance in diffusion generative models.

Presents an efficient regression-based algorithm for contextual partial monitoring games with side information.

Studies statistical calibration techniques to bound risk when fast language models make automated decisions under uncertainty.

Analyzes layerwise inference across tabular foundation models to understand how depth and intermediate representations affect predictions.

Introduces continuous-time inter-variable attention inside differential equation solvers for long-term multivariate time-series forecasting.

Models reward overoptimization in reinforcement learning with human feedback as economic rent-seeking driven by proxy reward signals.

Applies large language models to filter anomalies in industrial sensor training data using unstructured operational logs.

Analyzes the optimization landscape of overparameterized weighted maximum mean discrepancy to provide finite-particle guarantees.

Mitigates catastrophic forgetting in continual learning through a hybrid approach combining parameter isolation and local regularization.

Proposes a one-step generative approach to model occupancy for simplified policy extraction in reinforcement learning.

Improves industrial sound anomaly detection by adapting unsupervised models with few fault samples to reduce false alarms.

Introduces a benchmark to evaluate how language model agents learn and self-evolve reusable skills when exposed to non-adversarial noise.

Predicts high-level electronic band structures of materials using orbital information combined with low-cost baseline calculations.

Establishes theoretical convergence bounds for Gaussian process Thompson sampling under noiseless conditions in global optimization.

Treats symbolic regression as a continuous optimization problem using neural units that morph dynamically into mathematical formulas.

The paper develops a minimax-optimal estimator for the pseudo-spectral gap of continuous-state Markov chains from a single observed trajectory.

The paper demonstrates that hyperbolic contrastive reinforcement learning achieves strong navigation performance with much shallower networks than Euclidean models.

The paper presents a multimodal benchmark for competition-level chemistry that grades reasoning step-by-step with partial credit.

The paper introduces a time-dependent control-cost weighting method to fine-tune diffusion and flow-matching models with reward guidance.

The paper proposes a staged physics-informed neural network to estimate electrical parameters of a direct-current buck power converter.

The paper develops twin support vector regression methods using granular ball computing to improve robustness against noise and outliers.

The paper uses random matrix theory to analyze the trade-off between noise reduction and bias in decentralized federated learning.

The paper evaluates autoregressive foundation models trained on simulated market order flows to analyze whether they learn underlying Bayesian dynamics.

The paper introduces an exhaustive dataset of univariate expression skeletons generated by a formal grammar to improve symbolic regression.

The paper presents an interactive, browser-based tool for visualizing and learning the mechanics of Kolmogorov-Arnold Networks.

The paper introduces a Bregman proximal framework for neural operators to interpret activation functions and improve the training of deeper models.

The paper proposes an amortized flow-matching method to generate diverse batches for parallel black-box optimization over sequential rounds.

The paper presents a mixed integer linear programming formulation to train sparse, interpretable Boolean rule networks with reduced decision variables.

The paper derives dimension-optimal dynamic regret bounds for online learning with exp-concave losses under time-varying comparator paths.

The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights.

The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability.

The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling.

The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks.

The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers.

The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers.

The paper introduces a rectified flow matching framework to separate overlapping radio-frequency signals in a single channel in one step.

The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry.

The paper introduces a lattice-based theoretical framework to reconcile differing hypotheses about how internal representations converge across neural networks.

The paper investigates how conditional flow models learn and potentially hallucinate non-additive interactions in scientific prediction tasks.

This paper explores extreme super-resolution of turbulent flow fields by training a deep neural network on a single flow realization.

This paper presents an entropy-regularized memory-guided policy optimization method for online test-time adaptation in robot locomotion.