All papers

Proposes Product Monte Carlo to compute expectations under products of densities using draws from individual densities via regression.

Presents a Monte Carlo simulation framework to quantify distributional response shifts and model risks across language model lifecycles.

Analyzes the optimal balance between spatial sensor window size and temporal history when forecasting partially observed physical fields.

Establishes minimax estimation rates and examines plug-in estimators for powered sliced Wasserstein functionals under moment bounds.

Interprets Transformer networks as learned approximations to loopy belief propagation and expectation-maximization in dynamic Markov random fields.

Introduces a dual-curriculum conditional diffusion framework for multivariate time-series anomaly detection across varying masking and noise levels.

Proposes an institutional provenance-grouped evaluation protocol to eliminate data leakage when benchmarking models for perovskite photovoltaics.

Applies multivariate Hawkes processes to sequence data of soccer passes and shots to distribute attacking credit across multiple players.

Presents a coordinate-free automatic differentiation formulation that expresses geometric differential operations within standard machine learning primitives.

Develops polyconvex neural network surrogate models using learned paired directional features for anisotropic hyperelastic material homogenization.

Investigates quantum-native byte-level language models, analyzing memory limits under learned dissipation and testing them on quantum hardware.

Analyzes the expressive power and size-uniform approximation limits of message-passing neural networks using continuous random node features.

Uses ensembles of conditional variational autoencoders with wavelet decoders to synthesize acceleration time series from shock response spectra.

Develops a tree-based exploration and hindsight credit assignment method to improve multi-turn reinforcement learning for language model agents.

Introduces a scalable training method for determinantal point processes using nonlinear kernels, latent features, and stochastic optimization.

Proposes a cross-behavior policy optimization framework unifying offline demonstrations and online rollouts via a shared regression objective.

Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data.

Introduces a two-way guidance framework that jointly learns sufficient summary statistics and posteriors for scalable simulation-based inference.

Establishes finite-sample bounds for self-normalized Kullback-Leibler divergence estimators and applies them to test-time alignment sampling techniques.

Analyzes Langevin sampling algorithms under state-dependent heavy-tailed gradient noise with applications to Bayesian fine-tuning of language models.

Proposes a weighted metric to quantify and predict performance variability across mixed-integer programming solver runs.

Introduces a hybrid attention-RWKV architecture to mitigate horizon degradation in non-autoregressive time series forecasting.

Presents a cooperative game theoretic semi-supervised regression framework for predicting reservoir properties from seismic data.

Develops a personalized federated preference optimization framework maintaining local LoRA adapters atop a globally synchronized model.

Introduces determinant-ratio point processes to jointly model attraction and repulsion with distinct symmetric positive-definite kernels.

Proposes averaging query losses over generator states to improve pretraining sample efficiency in tabular foundation models.

Applies continuous-time control theory to stabilize adversarial minimax optimization against rotational divergence.

Analyzes online binary logistic regression where features are observed noisily subject to variable acquisition costs.

Studies dictionary learning on probability measures via linearized optimal transport embeddings and sparse autoencoders.

Proposes an imbalance-aware deep nonparametric two-sample test that leverages majority observations through repeated testing evidence.

Develops a matching repair framework with theoretical performance guarantees for training data acquisition under delayed feedback.

Employs a conditional diffusion model to generate observation-aligned precipitation forecasts from frozen weather foundation models.

Introduces a nuclear-aware determinant ansatz for multi-geometry neural-network variational Monte Carlo calculations.

Proposes scan-parallel variational inference for continuous-time latent jump models with state-dependent event intensities.

Derives tighter regret bounds and algorithms for offline-to-online linear bandits using Gaussian concentration tools.

Introduces an evidential transformer using Dempster-Shafer fusion to quantify and handle epistemic and aleatoric multimodal emotion uncertainty.

Presents a policy optimization method that corrects state-distribution shift during long-horizon reinforcement learning with language models.

Introduces Kolmogorov-Arnold flow to learn physical operator generators using low-rank univariate function symbols.

Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals.

Evaluates deep learning optimizers under a self-tuning fixed-hardware benchmark across nine distinct workloads.

Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring.

Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively.

Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics.

Proposes a spectral representation framework to attribute agent failures to specific decisive steps using only trajectory-level outcome labels.

Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale.

Provides a probabilistic marginal likelihood derivation explaining why heavy-tailed weight spectral metrics correlate with deep network generalization quality.

Introduces fast gradient-based approximation schemes for continuous dynamic time warping to enable scalable, differentiable time series alignment.

Uses wavelet scattering autoencoders to detect malicious client data updates offline in federated learning without requiring attack labels.

Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels.

Proposes a blind and verifiable sampling protocol for securely delegating quantum generative machine learning inference to remote servers.

Applies tensor decomposition and clustering to fuse heterogeneous clinical and genomic datasets for unsupervised cancer risk prediction.

Introduces coordinate normalization and energy-constrained sampling to train Lagrangian neural networks on complex Schwarzschild geodesic dynamics.

Combines Fourier neural operators with local-integral resampling in a U-Net to model high-frequency ultrasonic waves in heterogeneous media.

Generalizes multi-head attention by learning data-adaptive, full cross-head Mahalanobis metric matrices to capture inter-head feature interactions.

Applies Lagrangian neural networks with targeted sampling to recover non-pairwise three-body dispersion interactions directly from trajectory data.

Presents a DAG structure learning method that jointly estimates graph structure and node-varying heavy-tailed disturbance parameters.

Introduces an online reinforcement learning framework optimizing multi-objective rewards across diverse target backbones for RNA inverse design.

Presents an edge-computing architecture using low-cost hardware and adaptive algorithms to enable autonomous data management in smart buildings.

Combines joint-embedding predictive architecture with a Markov variational autoencoder to predict wireless channel state information under noisy observations.

Introduces a Schrödinger bridge diffusion matching method to solve forward and inverse partial differential equation problems under sparse observations.