All papers

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.

Introduces Double-Take to mitigate capacity and cross-position binding failures in long-context test-time training architectures.

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

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.

Formulates relative policy difference transfer as an implicit reward in KL-regularized optimization for flexible on-policy knowledge distillation.

Evaluates static Transformer block skipping in time-series foundation models to reduce inference latency with minimal forecasting degradation.

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

Replaces dense LoRA experts with block-diagonal factorizations to isolate gradients and prevent catastrophic forgetting during continual multimodal tuning.

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.

Investigates on-policy distillation for multi-turn agents by decoupling objective interaction states from internal student reasoning trajectories.

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.

Develops a retrieval-enhanced routing framework that dispatches multimodal queries to specialized vision-language models based on local failure patterns.

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.

Proposes stabilization modules to enable half-precision FP16 proximal policy optimization training on commodity GPUs without numerical divergence.

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.

Allocates precision across MoE experts by separately handling resident storage footprint and active batch decode traffic via dynamic optimization.

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

Analyzes Mamba-3's input-dependent state rotations as oscillatory dynamics to explain their utility for tracking states and long-context language modeling.

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.

Linearizes multilayer perceptrons using low-degree rational approximations of activation functions to reduce inference costs across 3D models and language models.

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.