Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
Adaptive learning of SPDE solutions using score-based diffusion models.
problem Model errors and reduced accuracy in SPDE solutions due to incomplete physical knowledge and environmental variability.
method Score-based diffusion models with recursive Bayesian inference, incorporating simulation data and observational information.
result Accuracy and robustness of the proposed method demonstrated on benchmark SPDEs.
SCORE improves tree-based predictions with boosted residual extraTrees.
problem Improving tree-based prediction models with reduced errors.
method Inspired by representation learning, SCORE uses boosting, regularized regression, and variable selection.
result SCORE provides comparable or superior performance compared to other models.
The paper tackles fairness in scoring functions for binary classification.
problem Fairness in scoring functions for binary classification tasks.
method Introduces ROC-based fairness constraints and learning algorithms.
result Generalization bounds and practical learning algorithms for fair scoring functions.
New active learning methods use statistical leverage scores to select examples efficiently.
problem Efficiently selecting labeled examples for high model accuracy with limited labeled data.
method Proposes ALEVS and DBALEVS methods based on statistical leverage scores.
result DBALEVS selects diverse, representative examples efficiently.
A novel framework quantifies uncertainty using proper scores for various tasks.
problem Uncertainty quantification in machine learning for reliable applications.
method Proposes a general framework based on proper scores for epistemic, aleatoric uncertainty, and model calibration.
result Achieves state-of-the-art uncertainty estimation for large language models and generative models.
Enhances learning of structured distributions using nonlinear denoising score matching.
problem Learning structured distributions from noisy data.
method Latent Nonlinear Denoising Score Matching (LNDSM) integrating nonlinear dynamics with VAE-based latent score matching.
result LNDSM achieves superior sample quality and variability compared to structure-agnostic methods.
SSDMs generate quantum states directly, outperforming classical methods.
problem Generating pure-state quantum representations efficiently.
method Score-based generative model on complex projective manifold.
result SSDMs match target pure-state ensembles by orders of magnitude.
A new method combines scores of individual observations to efficiently approximate posterior distributions.
problem Handling posterior distributions conditioned on multiple observations with neural methods.
method Conditional score modeling to combine learned scores from individual observations.
result Sample-efficient method that can aggregate multiple observations at inference time.
Simplifies denoising score matching for manifold learning.
problem Learning distributions on manifolds is computationally intensive.
method Modifies denoising score matching to implicitly account for the manifold.
result Reduces computational burden while maintaining efficiency.
SBMs learn manifold-like structures by mixing samples with a non-conservative field.
problem How SBMs learn data distributions on low-dimensional manifolds.
method Investigating linear approximations and subspaces of local feature vectors during diffusion.
result SBMs mix samples by a non-conservative field within the manifold, maintaining manifold-like structure.
New analysis shows scores learn data manifolds better than distributions.
problem Learning the full distribution vs. just the data manifold.
method Novel analysis of scores in the small-σ regime.
result Scores learn data manifold information Θ ( σ − 2 ) Θ(σ^{-2}) Θ ( σ − 2 ) stronger than distribution information. Improved score-based models generate high-quality images up to 256x256.
problem Training score-based models for high-resolution images is unstable and limited.
method Theoretical analysis, exponential moving average of model weights.
result Score-based models can generate high-fidelity images up to 256x256.
Deep networks can approximate score functions in high-dimensional graphical models efficiently.
problem Approximation efficiency of score functions by deep neural networks in high-dimensional graphical models like Markov random fields.
method Variational inference denoising algorithms and efficient neural network representation.
result Efficient sample complexity bound for diffusion-based generative modeling when score functions are learned by deep neural networks.
The H-score can bias bicluster comparisons, but a correction is provided.
problem Bias in H-score for comparing biclusters.
method Analytical proof and simulation to demonstrate bias; correction method provided.
result The H-score can be biased towards small clusters, but a correction is possible.
New bounds improve pruning for BDeu score in BNSL.
problem Improving pruning for BDeu score in BNSL.
method Deriving new upper bounds for BDeu score.
result New bounds are tighter and computationally efficient.
FLOP algorithm speeds up causal structure learning for linear models.
problem Efficiently learning causal structures from discrete data.
method FLOP algorithm combines fast parent selection and iterative score updates.
result FLOP finds highly accurate causal structures with near-perfect recovery.
Paper tackles score following in full-page sheet music images.
problem Score following in unprocessed sheet music images.
method Directly predicts score positions from audio and image input.
result Outperforms state-of-the-art methods in alignment precision.
ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.
problem Improving debiased machine learning and policy effects estimation.
method Score matching and Riesz representer estimation.
result Estimates policy path for continuous treatments, improving interpretability.
Method solves Bayesian inverse problems in function space without assuming log-concavity.
problem Bayesian inverse problems in infinite-dimensional nonlinear settings.
method Score-based diffusion models as a prior, Langevin-type MCMC on function spaces.
result Provable convergence bound for posterior sampling, dependent on score approximation.
Paper bridges score estimation to parameter and density estimation in DDPMs.
problem Efficiently estimating scores for generative models.
method Introduces a framework linking score estimation to parameter and density estimation.
result Denoising score-matching in DDPMs is asymptotically efficient for parameter estimation.
New score-based methods identify causal structures with latent variables.
problem Identifying causal structures involving latent variables.
method Score-based methods with identifiability guarantees.
result Score equivalence and consistency for latent variable causal models.
We solve the paradox of score-based methods by minimizing path variance.
problem Score-based methods are path-dependent, leading to inaccurate and unstable estimators.
method Propose MVP Principle to minimize path variance, derive closed-form expression, and use flexible Kumaraswamy Mixture Model.
result Establishes new state-of-the-art results on challenging benchmarks.
New algorithm speeds up learning of graphical models.
problem Learning graphical models with sparse structure efficiently.
method Vertex-greedy score-based algorithm for learning DAGs.
result Polynomial runtime for learning DAG models.
New method for generating images with conditional probability models.
problem Generating images with specific conditions.
method Score-based diffusion models with theoretical analysis and new estimator.
result New estimator for conditional score performs similarly to state-of-the-art.
Framework scores DeFi users based on liquidity and trading behavior.
problem Distinguishing between liquidity provision and active trading in DeFi.
method Rule-based decomposition, deep residual neural network, pool-level context.
result Deep residual neural network improves user scoring and risk assessment.
New machine learning methods for inference from simulated data.
problem Modeling score and likelihood ratio functions from sampled data.
method InferoStatic Networks (ISN), Kernel Score Estimation (KSE), Kernel Likelihood Ratio Estimation (KLRE).
result Improved inference methods for complex models.
Score-based models improve diffuse optical tomography accuracy.
problem Improving accuracy in diffuse optical tomography with uncertainty quantification.
method Score-based diffusion models with a mixed score function to prevent overfitting.
result Data-driven prior distribution results in posterior samples with low variance and centred around the ground truth.
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
problem Uncertainty quantification for Transformer Hawkes process predictions.
method Score matching for learning the score function of event arrival times.
result SMURF-THP outperforms likelihood-based methods in confidence calibration.
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
New method for data assimilation using score-based models.
problem Bayesian inverse problem of identifying plausible state trajectories.
method Score-based data assimilation, learning a score-based generative model of state trajectories.
result Effective method for zero-shot observation scenarios.
Large learning rates prevent memorization in denoising score matching.
problem Memorization of training data in diffusion-based generative models.
method Investigating the role of large learning rates in the small-noise regime, proving that they prevent convergence to the empirical optimal score.
result Large learning rates prevent memorization by making it impossible for the learned score to be arbitrarily close to the empirical optimal score.
SMRL uses score matching for efficient RL with exponential family models.
problem Efficient reinforcement learning with unknown transition models.
method Score matching for parameter estimation of exponential family models.
result Achieves online regret of O ( d H 3 T ) O(d\sqrt{H^3T}) O ( d H 3 T ) . BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
problem Imbalanced and heterogeneous credit scoring datasets.
method Bagging Supervised Autoencoder Classifier (BSAC) that uses autoencoders and undersampling.
result BSAC improves classification of loan applicants, demonstrating robustness and effectiveness.
A new algorithm learns MAGs from data more efficiently using entropy.
problem Learning MAGs from data is unstable and computationally expensive.
method Uses entropy estimation and refined Markov property to score MAGs.
result Algorithm is polynomial in number of nodes and outperforms existing methods.
A new loss function ED simplifies training energy-based models without scores.
problem Training energy-based models is computationally expensive.
method Energy Discrepancy (ED) loss function that does not rely on scores or MCMC.
result ED effectively interpolates between score matching and negative log-likelihood.
Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maxim…
Engression uses neural networks to learn conditional distributions, analyzing its error components.
problem Analyzing the error in neural-network-based engression.
method Engression decomposes excess risk into approximation, stochastic, and Monte Carlo errors.
result Established convergence rates under compositional smoothness assumptions.
We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefin…
New method detects changes in high-dimensional Markov processes without explicit likelihood evaluation.
problem Quickest change detection in Markov processes with unknown transition kernels.
method Learn conditional score from sample pairs, develop score-based CUSUM procedure.
result Exponential lower bounds on mean time to false alarm and asymptotic upper bounds on detection delay.
WS diffusion models handle anisotropic Gaussian noise better than conventional methods.
problem Handling anisotropic Gaussian noise in imaging inverse problems.
method Whitened Score (WS) diffusion models based on stochastic differential equations.
result WS DMs outperform conventional DMs on anisotropic Gaussian noise.
We improve density-based distances using normalizing flows and score matching.
problem Inaccurate density estimates and poor convergence in graph-based methods for high-dimensional spaces.
method Learn densities with normalizing flows and refine geodesics with a score model.
result Improved density-based distances that scale to high dimensions and improve numerical stability.
MAFLA improves sampling from heavy-tailed distributions using MH-inspired corrections.
problem Sampling from heavy-tailed and multimodal distributions when neither target nor proposal densities can be evaluated.
method Metropolis-Adjusted Fractional Langevin Algorithm (MAFLA) with Score Balance Matching.
result MAFLA significantly improves finite-time sampling accuracy over unadjusted fractional Langevin dynamics.
A new method improves training of energy-based models.
problem Training energy-based models is challenging.
method Diffusive Classification (DiffCLF) objective.
result DiffCLF enables EBMs with higher fidelity and broader applicability.
Paper presents variational estimates for EBLVMs without structural assumptions.
problem Challenges in learning and evaluating EBLVMs due to intractable true posteriors and partition functions.
method Variational estimates of the score function and its gradient (VaES and VaGES) in a general EBLVM.
result The estimates can be applied to KSD and SM-based methods to learn EBLVMs and estimate Fisher divergence.
Improved generative models using critically-damped Langevin diffusion.
problem Current score-based generative models (SGMs) use overly simplistic diffusion processes, leading to complex denoising tasks and suboptimal performance.
method Proposed a novel critically-damped Langevin diffusion (CLD) and derived a score matching objective and sampling scheme.
result CLD-based SGMs achieve superior performance in synthesis quality compared to previous methods.
A new method for estimating complex models and high-dimensional data.
problem Difficulty in computing Hessian of log-density functions for complex models and high-dimensional data.
method Sliced score matching, which projects scores onto random vectors before comparison.
result Sliced score matching can learn deep energy-based models and produce accurate score estimates.
Improves robustness of propensity score estimators in challenging settings.
problem Limited overlap, small sample sizes, or unbalanced data.
method Extends calibration techniques for propensity score models, focusing on sample-splitting schemes.
result Calibration reduces variance and bias in inverse probability weighting and double/debiased machine learning frameworks.