All papers

This paper introduces an uncertainty-aware property prediction workflow to guide out-of-distribution candidate selection for ionic liquid solvent discovery.

This paper presents a cleaned and enriched residential floor plan dataset with graph, geometry, and image representations for machine learning.

This paper introduces a coordinate encoder trained on open map data to produce multi-scale location embeddings without requiring imagery.

This paper proposes density-matrix reinforcement learning, representing policies as density matrices to capture relationships and coherences between actions.

This paper introduces a reinforcement learning method that calibrates test pass-rate rewards to create adaptive training curricula for software engineering agents.

This paper introduces an implicit solvation potential providing solvent-dependent corrections to gas-phase machine learning interatomic potentials without retraining.

This paper presents a discrete flow-matching generator that evaluates molecular identity probabilities on demand to accelerate high-fidelity molecular generation.

The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning.

The authors compare federated learning algorithms for hospital mortality prediction and report that evaluation protocol choices outweigh aggregation algorithm differences.

The authors propose a score-based diffusion framework that controls generation speed across different graph frequency bands via colored noise priors.

The authors demonstrate that a single autoregressive neuron using chain-of-thought generation can simulate any feedforward circuit of the same activation type.

The authors provide a theoretical criterion to decide whether neural network layers should receive sign-based or spectral optimization updates during training.

The authors introduce a reinforcement learning method that optimizes language model explanations specifically to improve human downstream decision accuracy.

The authors develop a geometric construction method to build two-hidden-layer neural networks that achieve exact classification on given training sets.

The authors propose a deep unrolling algorithm combining alternating projections with a learned denoiser for phase retrieval under changing measurement conditions.

The authors derive an online-to-offline reduction achieving near-optimal regret bounds for strongly monotone stochastic optimization under semi-bandit feedback.

The authors present an ensemble variational data assimilation algorithm that incorporates learned generative forecasting models to account for complex model errors.

The authors introduce a parameter-free conformal martingale method modeled as a universal portfolio to quickly detect distribution shifts at unknown times.

The authors propose a test-time learning method that trains a proposer to generate prompts, tools, and controls guiding scientific discovery agents.

The authors analyze robustly reliable learning for regression under data corruptions and propose an algorithm to compute optimal prediction intervals for Lipschitz predictors.

The authors introduce a masked autoencoder that pretrains wearable sensor representations across varied recording durations and sampling rates using cross-resolution views.

The authors define measure neural mappings and establish quantitative error bounds for self-attention transformers acting on probability measures in unbounded spaces.

The authors present a mathematical method to enforce Dirichlet, Neumann, and Robin boundary conditions exactly on curved triangular domains for scientific machine learning.

The authors argue that cooperative multi-agent reinforcement learning systems require test-time coordination mechanisms rather than relying solely on training-time centralized information.

The authors present a verification workflow combining reachability analysis with Bayesian optimization to close residual test bins in chip design verification.

The authors compare self-supervised and sleep-stage-supervised representations for identifying sleep-wake disorders across thousands of polysomnography recordings.

The authors analyze continual learning on a spherical parameter space to determine how many tasks a model can learn without catastrophic forgetting.

The authors introduce a selective hypothesis test for comparing cluster means under an unknown covariance matrix without inflating false-positive rates.

The paper proposes a hierarchical reinforcement learning framework to automate multi-phase spacecraft rendezvous and docking trajectories.

The paper introduces diagnostic scores to test whether neural network surrogates respect underlying physical conservation laws rather than numerical errors.

The paper evaluates online machine learning models within a time-synchronous framework to predict weekly student disengagement from activity logs.

The paper evaluates clustering methods on learning management system logs to identify learner participation profiles across unequal observation windows.

The paper introduces a multimodal auditory attention decoding framework designed to maintain reliance on neural signals alongside behavioural tracking.

The paper proposes an inference-time alignment method that optimizes expected costs while controlling tail risk in diffusion models.

The paper introduces a self-distillation framework where a vision-language model teaches itself language-conditioned robotic navigation policies without human annotations.

The paper introduces a personalized federated learning method that regularizes local model updates using low-rank difference structures.

The paper analyzes the theoretical detection limits and sum-test efficiency of textGrain watermarking under human text edits.

The paper introduces a global-to-local dynamic attention model to filter visual noise and improve multimodal sarcasm detection.

The paper proposes a concept-guided regularizer using sparse autoencoders to reduce catastrophic forgetting during parameter-efficient fine-tuning.

The paper develops a flow matching framework with structured priors to model partially observed time series without wasting capacity on observed entries.

The paper investigates the representation geometry and inductive biases of quantum classifiers when applied to classical visual benchmark datasets.

The paper proposes a Kolmogorov-Arnold Network architecture using parallel Bessel branches and quadratic interactions to reduce parameters and conditioning problems.

The paper provides an adversarial linear bandit algorithm achieving sublinear regret over general convex action sets using follow-the-perturbed-leader.

The paper presents a geometry-aware pretraining framework that aligns heterogeneous probe layouts for functional near-infrared spectroscopy representation learning.

The paper proposes a minimal positional encoding method using random Fourier features to feed persistence diagrams directly into standard Transformers.

The paper introduces an open-ended benchmark for evaluating agentic systems on discovering multiple data-supported hypotheses from tabular datasets.

The paper develops a neural network approach to learn effective mesoscopic field equations directly from microscopic trajectory data.

The paper presents a subspace construction method for continual low-rank adaptation to mitigate forgetting under differential privacy constraints.

The paper introduces a unified local training framework using norm-ball clipping to improve communication efficiency in distributed and federated learning.

The paper introduces a score-guided particle framework using Stein variational gradient descent to model multivariate return distributions in reinforcement learning.

This paper establishes accelerated parameter estimation rates when continuous data samples are accompanied by binary quality labels or pairwise preferences.

This paper proposes a conformal risk control layer that certifies miss rates and reduces alarm duty cycles in predictive cellular network operations.

This paper introduces a dimensional analysis framework and diagnostic metrics to maintain scale consistency across Transformer components.

This paper establishes minimax rates for estimating language model semantic entropy using verdicts from weak pairwise equivalence judges.

This paper presents an equivariant neural network functional to perform orbital-free density functional theory for periodic CHNO systems.

This paper introduces an exact method to compute Banzhaf and Shapley power indices for base classifiers in plurality-voting ensembles.

This paper proposes a counterfactual certification framework to bound performance loss during sequential machine unlearning requests.

This paper improves neural symbolic regression by using a tabular prior and reinforcement learning alignment to fit noisy data.

This paper presents an agentic framework and an evaluation rubric for generating explanations of risk labels in graph fraud detection.

This paper theoretically and empirically compares weighting-based rebalancing strategies to unweighted methods in imbalanced tabular classification tasks.