All papers

The paper models demand management using probabilistic service options and analyzes provider incentives under transparency constraints.

The authors analyze theoretical sample-complexity tradeoffs between questions and generations when ensembling multiple language models at test time.

The paper introduces a regularization technique that aligns internal representations across rollout origins to improve neural differential equation emulation.

The authors show how prequential regret from minimum description length bounds neural network generalization error without held-out data.

The paper introduces a benchmark to evaluate question answering systems on reasoning and predictive tasks over relational databases.

The paper proposes a graph neural network framework to exploit multiple spatiotemporal aggregation hierarchies for time-series forecasting.

This study audits how historical information and calendar controls affect trajectory forecasting within a university simulator.

The paper presents a predictive verifier that decides how many actions from a vision-language-action chunk to execute safely.

The authors propose an algorithm that assigns multi-reward reinforcement learning credit specifically to relevant response tokens.

The paper introduces a dual-encoder transformer that jointly transcribes piano performances to scores and tracks metrical beat frames.

This paper analyzes Kolmogorov-Arnold networks to show that favorable dimension scaling depends on target smoothness rather than the architecture alone.

The authors introduce a nonlinear phase kernel to expand the effective retrieval capacity of modern Hopfield networks.

The paper analyzes the mathematical approximation capabilities of neural networks when layer normalization or RMSNorm is placed directly adjacent to activations.

The paper develops a primal-dual reinforcement learning algorithm providing finite-time performance and constraint-satisfaction bounds under partial observability.

The paper presents a differentiable masking technique to learn sparse control phases for preparing bosonic qudit quantum states.

The paper applies quantum natural language processing circuits to content-based TV programme recommendations, comparing efficiency against classical models.

The paper derives mathematical bounds to evaluate the calibration of query-based probabilistic models using logical consistency without ground-truth labels.

The paper introduces a benchmark to evaluate proactive video assistants across perception, risk prediction, and timely real-world intervention tasks.

The paper establishes regret bounds and an algorithm for transfer learning in multi-armed bandits when historical data comes from unknown adaptive policies.

The paper applies game theory to analyze market incentives for AI safety investments across upstream foundation model providers and downstream firms.

The paper uses local temporal contrastive learning on human motion data to preserve multi-scale latent geometric trajectories in representations.

The paper proposes conditional mean flows to enable few-step posterior sampling and tractable density evaluation for simulation-based inference.

The paper analyzes the accuracy and efficiency trade-offs of derivative-free weight perturbation compared with standard backpropagation on MNIST.

The paper presents a lightweight classical latent variable method for generating synthetic mixed tabular data without complex deep architectures.

The paper proposes an interpretable, momentum-aware adaptive testing system that updates learner proficiency ratings dynamically during timed assessments.

The paper introduces a stochastic approximation expectation maximization algorithm for robust covariance estimation with coordinate-wise contaminated and missing data.

The paper analyzes direct pairwise area-under-curve training to explain why mini-batch stochastic gradient descent can fail on severe ranking errors.

The paper introduces a multi-horizon reinforcement learning framework that dynamically mixes multiple discount factors using policy confidence signals.

The paper investigates optimization dynamics in variational quantum circuits, showing single-qubit re-uploading circuits encounter phase-locking during first-order training.

The paper provides analytical formulas to characterize and explain the gradient of learning-rate transfer curves across different model scales.

The paper derives a closed-form geometric law describing how depth and width affect representation compression in deep linear networks.

The paper frames client local training in federated learning as an optimal stopping problem to balance local updates against drift.

This paper presents a benchmark and link inference attack framework to evaluate edge unlearning methods in graph neural networks.

This paper proposes a Dirichlet energy criterion on kernel similarity graphs for bivariate causal inference without regression fitting or independence testing.

This paper presents a self-calibrated robust PCA method for removing mixed noise and line stripes in hyperspectral images without preset parameters.

This paper formulates tilted sampling as an online learning problem for sampling from Gibbs distributions and establishes logarithmic regret bounds.

This paper analyzes the theoretical Lagrangian structure of cost-penalized heuristics in active feature acquisition under hard budget constraints.

This paper introduces a prior-data fitted network that performs simultaneous Bayesian variable selection and nonlinear prediction in a single forward pass.

This paper introduces a spatio-temporal spectral sheaf Laplacian and filter to jointly capture space and time dependencies on graphs.

This paper presents an uncertainty-aware federated learning method to manage power and computing allocation for extended reality devices under state uncertainties.

This paper combines Gaussian process references with targeted diffusion model corrections to infer scientific physical fields from sparse data.

This paper applies deep learning to classify topological equivalence classes of finite group actions on Riemann surfaces.

This paper introduces an interface-based graph representation and encoder for scalable weight-space learning across diverse neural network architectures.

This paper introduces a stateful testing method to detect bugs in deep learning compilers caused by retained state across invocations.

This paper investigates unlabeled data selection strategies for semi-supervised QSAR modeling using large chemical repositories.

This paper introduces usage-aware Bayesian experimental design to minimize risk when experimental data is processed by unknown or external decision makers.

This paper tests a convolutional neural network technosignature detector against synthetic injections in real radio data to reveal out-of-distribution failure modes.

This paper optimizes the predefined phase-length schedules of Successive Rejects algorithms to improve error exponents in fixed-budget best-arm identification.

This paper evaluates probabilistic AI weather forecasting models against operational physics-based ensembles on tropical cyclone tracking, intensity, and structure.

This paper categorizes failure modes when deploying neural networks in closed-loop control systems and proposes online adaptation policies to counter distribution shifts.

This paper presents a diffusion-based posterior inference framework to reconstruct physical scenes from ubiquitous wireless channel measurements.

This paper introduces a temporal difference active learning framework to adaptively learn experiment selection policies for budgeted perturbation discovery.

Reorganizes tensor operations in physics-informed DeepONet architectures to eliminate redundancy and reduce training costs for higher-dimensional problems.

Analyzes generalization and memorization dynamics in exponential Hopfield networks trained with softmax attention over noisy examples.

Derives theoretical high-probability generalization guarantees and early stopping bounds for randomized block coordinate descent on nonconvex problems.

Introduces a tabular foundation model pretrained on synthetic priors to predict survival curves in-context.

Introduces an algorithm to evaluate multiple target policies simultaneously in contextual linear bandits with shared sampling.

Applies topology-inspired loss functions to fine-tune super-resolution neural networks on scientific wind-field datasets.

Analyzes score centering and covariance estimands to maintain reward shift invariance under regularized importance sampling.

Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives.