All papers

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 presents an automated coding agent that tunes large model training configurations for throughput under user-defined compute budgets.

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 proposes mixed precision scaling laws to model the trade-offs of low-precision quantization during model pretraining.

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

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 an expressive and distributed attention kernel that compresses structured attention masks to enable efficient context parallelism on Hopper GPUs.

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

This paper presents an error-controlled compression codec for key-value caches using coordinate alignment and predictive coding.

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 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.

This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods.

This paper develops an empirical-likelihood bandit policy that provides asymptotic regret guarantees without knowledge of reward scales.

This paper proposes a score-matching approach for causal discovery in additive noise models under hard selection bias and truncation.

This paper introduces a framework combining persistent belief states with implicit world modeling to help long-horizon language agents recover from failures.

This paper demonstrates that standard optimizers are sensitive to parameter scaling in Transformers and proposes a symmetry-aware gradient adjustment.

This paper analyzes how layer ordering and selection affect performance across tasks in hybrid attention model architectures.

This paper introduces an attention-based neural network to predict code distances for bivariate bicycle quantum low-density parity-check codes.

This paper formulates a trace-minimization framework to design causal error-feedback coefficients for neural network weight quantization.

This paper analyzes the sample and mistake complexity of learning group elements from actions using Galois correspondences and multiclass dimensions.

This paper proposes a message-passing layer using randomized probing of operator diagonals to capture node self-interactions in graph neural networks.

This paper introduces a reinforcement learning navigation policy using shared spatial fields for large-scale lifelong multi-agent path finding.

This paper proposes a cross-image routing attention mechanism to predict millimeter-wave beam power from dual-endpoint vehicle-to-infrastructure camera views.

This paper presents an operator-valued kernel regression framework that enforces aggregation constraints directly in hierarchical time series forecasting.

This book chapter provides an overview and mathematical introduction to finite-parametric operator surrogates for approximating maps between infinite-dimensional spaces.

This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games.

This paper proposes Lookahead Self-Distillation, providing cross-instance token-level supervision to improve reinforcement learning with verifiable rewards for language models.

This paper develops an online algorithm achieving optimal regret bounds for stochastic contextual bandits with unknown context-lumpable cluster structures.

This paper introduces a physics-informed graph neural network for modeling advection-diffusion processes to forecast solar power production.

This paper derives finite-sample risk bounds for Kernel Mean Matching under dependent sampling using effective sample sizes.

This paper presents HE-CV-UCB, an algorithm for Bayesian optimization of Conditional Value-at-Risk under unknown Gaussian process hyperparameters.

This paper analyzes the geometric robustness of over-parameterized low-rank matrix sensing under the Burer-Monteiro factorization.

This paper provides optimal learning rate bounds for random feature kernel ridge regression under misspecified models and heavy-tailed noise.

This paper develops a theoretical framework characterizing when representations learned on a source task remain recoverable and reusable for future tasks.

This paper proposes a forecast-error distribution entropy framework to detect nonlinear predictive coupling across multiple time horizons.

This paper introduces LunaTP, a parallel execution framework that accelerates tensor product convolutions in equivariant machine learning interatomic potentials.

This paper benchmarks eighteen continual learning methods on a sequential radar waveform recognition task across task- and class-incremental settings.

This paper develops finite-sample bounds to guide algorithm design for Expected Improvement in continuous simulation optimization with heteroscedastic noise.

This paper presents FastEEP, a system that speeds up intra-instance elastic scaling for Mixture-of-Experts serving by reusing runtime state.

This paper develops graph metanetworks that respect the weight-space symmetries of transformer language models, including attention matrices and token embeddings.

This paper introduces ProTok, a unified discrete tokenizer for protein sequence, structure, and functional annotations using modular multi-codebook quantization.

This paper presents Lifting, a framework combining repetition encoding and neural decoding for mid-circuit quantum state discrimination on superconducting hardware.