All papers

Proposes a transformer solver that uses in-context learning trained on approximate solutions to predict partial differential equation fields.

Presents an open-source library that unifies training, evaluation, reinforcement learning, and distillation workflows for tool-using language model agents.

Identifies and evaluates temporal, semantic, and population preprocessing misalignments that cause data leakage and bias in clinical machine learning pipelines.

Proposes a reinforcement learning framework with verifiable rewards and error grounding to align multimodal emotion descriptions with human preferences.

Analyzes classifier-free guidance in diffusion models through an information bottleneck framework to characterize when conditioning information is most effective.

Introduces a conjugate gradient solver for large-scale kernel ridge regression using sketched block preconditioning to improve memory efficiency and convergence.

Presents a geospatial foundation model that creates structural place embeddings from sociodemographic and business data across hexagonal spatial units.

Develops a model-free Bayesian clustering method using martingale posteriors and Wasserstein summarization to bypass explicit likelihoods and MCMC sampling.

Analyzes theoretically and empirically why linear models often out-predict complex nonlinear models under finite samples and environment distribution shifts.

Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics.

Introduces an efficient linear bandit algorithm that maintains a small candidate set of actions to reduce per-round evaluation costs.

Provides a comprehensive tutorial on time series causal discovery methods, covering underlying principles, evaluation techniques, and practical applications.

Introduces a unified benchmark evaluating five public photoplethysmography foundation models across nine datasets and 26 clinical and wearable tasks.

Proves that risk estimates derived from preferent compression schemes achieve standard law-of-large-numbers rates without excess logarithmic factors.

Derives theoretical approximation rates for Sobolev functions on quantum state and gate spaces using harmonic analysis and finite-depth quantum circuits.

Introduces a learned primal-dual solver for nonlinear model predictive control that uses temporal decomposition to scale independently of the prediction horizon.

Proposes a federated optimization framework that preserves global aggregate objectives while applying minimal corrections to meet individual client performance constraints.

Analyzes the discrete-time dynamical system of transformer self-attention, classifying its equilibria and showing consensus alignment with the value matrix's principal eigenvector.

Simulates transformer training loss spikes using quadratic models with heavy-tailed inputs and derives an adaptive optimizer hyperparameter schedule to prevent them.

Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts.

Presents an online randomized TCP acknowledgment algorithm that provides simultaneous competitive ratio bounds and sublinear regret against the best expert.

Applies Monte Carlo Tree Search with generalized rollout policy adaptation to optimize out-of-sample feature selection for high-dimensional financial regression.

Combines hierarchical forecasting with conformal prediction under missing data to forecast electric vehicle charging demand with valid prediction intervals across evolving networks.

Investigates theoretical lower bounds for randomized linear minimization oracle optimization algorithms over strongly convex sets.

Proposes a measurement-grounded representation framework using tabular tokens to link multi-view echocardiograms to clinical diagnostic tasks.

Presents a cognitive architecture based on episodic memory hierarchies and context-dependent error updates to predict human choice behavior.

Proposes an unconstrained parameterization and equivariant flow matching model for generating valid correlation matrices.

Presents a cycle-consistent adversarial model enabling continuous sound pressure level adjustments for speech vocal intensity conversion.

Analyzes theoretical connections between generative diffusion models, flow matching, and entropic optimal transport formulations.

Develops a kernel-based maximum mean discrepancy framework to formalize and estimate the external validity and replicability of experimental studies.

Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines.

Develops a differentially private and distributionally robust ADMM framework for multiclass classification across distributed client data.

Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks.

Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models.

Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains.

Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency.

Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models.

Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy.

Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations.

Proposes an in-context score operator framework allowing diffusion models to adapt to unseen target distributions without retraining.

Develops a theoretical framework linking training objective losses to sampling accuracy and finite-time convergence in Generative Flow Networks.

Presents an exact machine unlearning method for federated socially fair k-means clustering using separate group-level local summaries.

Introduces a multi-mode urban mobility benchmark across Seoul to evaluate origin-destination travel demand forecasting models.

Introduces two synthetic mathematical task datasets to study how task diversity affects model learning during pretraining.

Empirically evaluates the Bayesian posterior approximation quality and amortization errors of prior-data fitted networks across synthetic prior tasks.

Introduces an information-theoretic framework using rate-distortion theory to establish computable lower bounds on feature reconstruction under DP-SGD.

Analyzes convergence rates of a multi-objective optimization method pairing norm-constrained linear minimization oracles with entropic task-weight updates.

Establishes stochastic first-order and zeroth-order algorithms achieving optimal rates for constrained nonsmooth nonconvex optimization.

Introduces randomized momentum transport to track stochastic gradients using probabilistically sampled transport corrections alongside cheap predictors.

Proposes a two-phase variance reduction method that adapts to average, standard, and noiseless smoothness settings without predefined hyperparameter constants.

Develops a sequential conditioning framework enabling Gaussian process linear partial differential equation solvers to enforce linear inequality constraints.

Evaluates combinatorial fusion analysis to build diverse ensembles combining classical models and variational quantum circuits for classification tasks.

Repurposes pretrained tabular foundation models into supervised dataset encoders using dataset-level readouts and contrastive post-training.

Presents Shared Canonical Component Removal to quantify predictive information retained by raw machine learning representations beyond predefined expert features.

Evaluates full sandbox snapshot exploration for coding agents, finding benefits primarily when recovery requires restoring runtime dependencies outside Git repositories.

Applies dynamic budget allocation algorithms at inference time to select scaffolds and epitopes during de novo nanobody generative design.

Derives finite-sample convergence guarantees for fully discrete conditional diffusion models with finitely many denoising steps under smooth density assumptions.

Introduces PISO, an optimization framework that combines partially available gradients with zeroth-order residual tracking to reduce variance from random probing.

Analyzes the dimensional requirements of exact recurrent memory states for cumulative learning under convex residual loss functions.

Establishes finite-time error bounds for single-trajectory asynchronous Q-learning that incorporate the hitting time of the underlying Markov decision process.