All papers

The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows.

The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels.

The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations.

The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates.

The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells.

The paper introduces a standardized 25-task benchmark and evaluation protocol for docking-based molecular optimization in drug design.

The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts.

The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games.

The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions.

The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs.

The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token.

The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits.

The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions.

The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models.

The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives.

The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters.

The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models.

The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents.

The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options.

The paper extends undirected graphical models to non-Gaussian data using copula densities and provides an estimation algorithm.

The paper introduces an online tokenization algorithm that processes text streams in linear time with minimal algorithmic delay.

The paper shows how graph databases can execute graph neural network sampling, retrieval, and inference directly without separate in-memory pipelines.

The paper examines teacher interventions along student generation paths to identify how teacher guidance drives performance gains in on-policy distillation.

The paper analyzes packet routing throughput when selfish queue agents use no-regret learning algorithms to select buffered servers.

The paper proposes a Bayesian full waveform inversion framework that uses learned diffusion models as regularizers for geological reconstruction.

The paper proposes a directed graph neural network approach to forecast twelve-hour wildfire spread using spatial and environmental attributes.

The paper introduces a method to convert dense models into mixture-of-experts by dynamically allocating different numbers of active experts across layers.

The paper develops a continuous forecasting method using speech representations to anticipate stuttering events shortly before they occur.

The paper demonstrates that language models use modular abstract representations to generalize code generation across different programming languages and strategies.

The paper benchmarks language models on extracting and categorizing double-entry accounting relationships from corporate financial disclosure filings.

The paper introduces a neural solver architecture to accurately compute solutions and sensitivities for parametric stochastic volatility partial differential equations.

The paper presents an outcome-routed distillation and reinforcement learning method tailored for training mixture-of-experts diffusion language models.

The paper derives theoretical scaling laws for prediction regret in a capacity-constrained bigram model with power-law token frequencies.

The paper proposes an acquisition-time residual adaptation method that uses historical features to improve hit rates in sequential screening.

The paper examines why molecular graph transformers avoid attention sink patterns seen in language models and highlights attention design impacts.

The paper introduces a framework to translate and decompose clinical measurement records across heterogeneous assessment systems within predefined error tolerances.

The paper presents a dynamic graph framework to jointly forecast evolving node and relation representations from historical graph contexts.

The paper shows that one-step prediction accuracy in joint-embedding world models is an unreliable indicator of downstream planning performance.

The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons.

The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks.

The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors.

The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation.

The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes.

The paper applies transformer neural operators to model interference channels and cancel digital self-interference in full-duplex radio systems.

The paper presents a mathematical framework unifying symplectic geometry, kernel methods, and physical conservation laws for physics-constrained learning.

The paper introduces a heterogeneous graph variational autoencoder to model design and state variables jointly for analog circuit analysis.

The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems.

The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks.

The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems.

The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems.

The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points.

The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes.

The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders.

The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations.

The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways.

The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization.

The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement.

The paper introduces a two-stage low-rank fine-tuning method that uses residual gradients to add complementary adaptation directions without expanding the rank.

The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions.

Introduces an adaptive spectral gradient clipping method to stabilize and accelerate large language model pretraining.