Optimizes ground metric on graphs for evolving density models.
problem Optimizing ground metric for evolving density models.
method Adaptive ground metric learning constrained to geodesic distances on graphs.
result Efficiently learned geodesic distances align with observed density evolution.
A new method learns meaningful distances between samples using optimal transport.
problem Learning meaningful distances between samples in datasets without labeled data.
method Computes OT distances between samples and features using singular vectors of a function mapping ground metrics to OT distances.
result Wasserstein Singular Vectors provide a scalable solution for unsupervised ground metric learning.
Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in…
Paper proposes metrics to evaluate AI explanations without ground truth.
problem Challenges in evaluating neural network explanations without ground truth.
method Designs four metrics to evaluate explanation results.
result New insights into neural network interpretation methods.
The paper identifies magnetic ground states and their role in determining the conformal class of a surface.
problem Understanding the magnetic ground states and their relation to the conformal class of a surface.
method Analyzing the magnetic Laplacian and its eigenvalues on a Riemannian surface.
result The ground state spectrum uniquely determines the volume and conformal class of the metric.
Wasserstein GANs use different p-metrics to improve model performance.
problem Improving stability and performance of Wasserstein GANs.
method Introduce (q,p)-Wasserstein GANs using various p-metrics. result Different p-metrics can notably improve GAN performance. Paper introduces metrics to evaluate missing data imputation without ground truth.
problem Handling missing data in time series without ground truth.
method Introduces Wasserstein distance (WD) and Jensen-Shannon divergence (JSD) as metrics to evaluate imputation quality.
result WD and JSD are effective metrics for assessing missing data imputation quality.
Unsupervised clustering can reproduce categorization systems if features and metrics are correctly selected.
problem Reproducing expert-provided categorization systems using unsupervised clustering.
method Investigated using toy datasets and real-world fund categorization. Used appropriate feature selection and a supervised Random Forest-based distance metric.
result Unsupervised clustering can reproduce ground truth classes if features and metrics are correctly selected.
Study measures malware detection metrics without ground truth.
problem Accurate measurement of security metrics in the absence of ground truth.
method Statistical estimation methods for five malware detection metrics.
result Characterized and improved statistical estimators for accuracy.
Enhances trading metrics with financially grounded loss functions.
problem Challenges in financial deep learning, especially interpretability.
method Introduces loss functions derived from finance metrics and turnover regularization.
result Proposed loss functions outperform traditional methods in trading metrics.
RealCause provides a realistic benchmark for causal inference.
problem Lack of a reliable benchmark for comparing causal effect estimators.
method Flexible generative models to create a benchmark that is both ground-truth and realistic.
result Evaluation of over 1500 causal estimators provides evidence for choosing hyperparameters using predictive metrics.
New method estimates model performance bounds without ground truth labels.
problem Evaluation of weakly supervised models without direct access to ground truth labels.
method Formulates model evaluation as a partial identification problem and uses Fréchet bounds for performance estimation.
result Derives accurate and computationally efficient bounds for key metrics like accuracy, precision, recall, and F1-score.
Novel distances between distributions using conditional ground distances.
problem Quantifying distances between statistical multivariate distributions.
method Optimal transport with entropic regularization and ground distance on conditionals.
result Upper bounds for jointly convex distances and improved GMM learning.
Study validates metrics for offline MBO using diffusion models.
problem Evaluate metrics for offline MBO without ground truth oracle.
method Propose and quantify validation metrics over datasets.
result Identify most effective validation metrics.
New method accounts for uncertainty in medical AI evaluations.
problem Uncertainty in ground truth affects AI model performance estimates.
method Statistical aggregation approach to infer probabilities of medical conditions.
result Performance estimates are significantly lower when uncertainty is accounted for.
The study categorizes reward errors in reinforcement learning, finding some can be beneficial.
problem Training language models with imperfect proxy rewards.
method Theoretical analysis of policy gradient optimization and categorization of reward errors.
result Reward errors can be benign or even beneficial, preventing policy from stalling.
CNNs improve InSAR image denoising and coherence estimation.
problem Noise in InSAR imagery corrupts ground movement estimates.
method Autoencoder CNN architectures for denoising and preprocessing.
result Proposed method outperforms four established methods.
Proper holomorphic isometries between Bergman domains are biholomorphisms.
problem Characterizing isometries between Bergman domains.
method New method from Information Geometry.
result Proper holomorphic local isometries are biholomorphisms.
Enhances NOAA's Geospace model with machine learning for predicting ground magnetic perturbations.
problem Predicting the probability of ground magnetic field changes.
method Combining physics-based model with machine learning (boosted ensemble of classification trees).
result The ML-enhanced model consistently improves probabilistic forecast metrics.
Ranking a set of objects involves establishing an order allowing for comparisons between any pair of objects in the set. Oftentimes, due to the unavailability of a ground truth of ranked orders, researchers resort to obtaining judgments from multiple annotators followed by inferring the ground truth based on the collec…
New metric improves latent dynamics inference from neural data.
problem Limitations of co-smoothing in predicting latent dynamics.
method Few-shot co-smoothing to assess latent dynamics.
result High co-smoothing models often have extraneous dynamics, which few-shot co-smoothing detects.
Proposes a method to choose thresholds for LLM evaluation metrics.
problem Ensuring reliable large language models (LLMs) with correct threshold selection.
method Identify risks, stakeholders' risk tolerance, and use ground-truth data to determine thresholds.
result Demonstrates a concrete example with the Faithfulness metric and HaluBench dataset.
Polymarket-v1 Database tracks 1.2B trades across 1.3M markets with 100% ground-truth direction.
problem Lack of ground-truth data in prediction markets archives.
method Ground-truth archive of 1.2B trades from Polymarket's CTF Exchange.
result Ground-truth data reveals systematic errors in microstructure metrics.
Proposes MCC-F1 curve for better binary classification evaluation.
problem Misleading performance evaluations with ROC and PR curves for imbalanced data.
method Introduces MCC-F1 curve combining MCC and F1 score.
result MCC-F1 curve provides clearer classifier differentiation.
Proposes BSF to evaluate structure learning algorithms without bias.
problem Evaluation bias in structure learning algorithms.
method Balanced Scoring Function (BSF) to adjust reward based on edge difficulty.
result Eliminates bias in favour of underfitted graphs.
Proposes a method to align language and image data.
problem Aligning language and image data for better understanding.
method Uses triplet loss to learn consistent embeddings of language and images.
result Outperforms four baselines across multiple metrics.
Evaluates change point detection algorithms on real-world data.
problem Insufficient evaluation of change point detection algorithms on real-world time series.
method Developed a data set of 37 time series from various domains, annotated by human experts, and evaluated 14 algorithms using consistency metrics.
result Demonstrates the need for better evaluation methods in change point detection.
Generative model synthesizes earthquake acceleration data.
problem Robust estimation of ground motions for engineering applications.
method Wasserstein GAN formulation for conditioning on physical variables.
result Trained model synthesizes realistic 3-component accelerograms.
Statistical analysis of Diffusion Tensor Imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies that have shown that a Rieman…
New framework to test neural network representation similarity measures.
problem Disagreements among dissimilarity measures in neural networks.
method Statistical testing framework to evaluate measures based on functional behavior.
result Current metrics have different weaknesses; a classical baseline performs surprisingly well.
TemperatureGAN generates hourly atmospheric temperature data with high fidelity.
problem Generating accurate hourly atmospheric temperature data for climate risk assessment.
method Generative Adversarial Network (GAN) conditioned on months, locations, and time periods.
result TemperatureGAN produces high-fidelity hourly atmospheric temperature data with good spatial and temporal consistency.
Abstract: New methods for finding optimal controls in geometric problems on Lie groups.
problem Finding optimal controls for geodesics on Lie groups.
method Pontryagin maximum principle, (co)adjoint representation, geodesic vector field.
result Developed methods to find normal geodesics and locally optimal controls.
Consider a sample of n points taken i.i.d from a submanifold Σ of Euclidean space. We show that there is a way to estimate the Ricci curvature of Σ with respect to the induced metric from the sample. Our method is grounded in the notions of Carré du Champ for diffusion semi-groups, the theory of Empirical process…
A nonnegative number d_infinity, called asymptotic dimension, is associated with any metric space. Such number detects the asymptotic properties of the space (being zero on bounded metric spaces), fulfills the properties of a dimension, and is invariant under rough isometries. It is then shown that for a class of open …
A new method matches measures across different spaces using cost-regularized optimal transport.
problem Matching measures in different spaces without aligned data.
method Cost-regularized optimal transport formulation to match measures across two Euclidean spaces.
result Demonstrated applicability to single-cell spatial transcriptomics/multiomics matching tasks.
Study evaluates DGMs' ability to recover non-stationary Gaussian fields.
problem Assessing DGMs' learning of non-stationary Gaussian random fields.
method Comprehensive evaluation of four DGMs (FM, DDPM, score-SDE, VAE) on a known non-stationary Gaussian random field.
result DDPM and score-SDE recover covariance structure reasonably well, while FM and VAE struggle.
Study on ground states of semilinear elliptic equations with various potential wells.
problem Characterizing ground states of semilinear elliptic equations with arbitrary potential wells.
method Analyzing solutions in convex domains and manifolds with non-negative Ricci curvature, using Morse theory and min-max methods.
result Ground states are mountain-pass type with Morse index 1 in convex domains and manifolds with non-negative Ricci curvature.
Ground-A-Video edits videos without training, preserving intended changes.
problem Complex multi-attribute video editing with omitted or wrong changes.
method Grounding-guided video-to-video translation with Cross-Frame Gated Attention.
result Zero-shot multi-attribute video editing with improved accuracy and frame consistency.
Metrics assess uncertainty structure and distribution for regression models.
problem Quantifying uncertainty in high-dimensional and nonlinear regression tasks.
method Two bounded comparison metrics for uncertainty structure and distribution.
result DNNs and DNOs provide encouraging uncertainty metric values in high dimensions.
Crowdsourcing infers ground truth from multiple annotators, verified for supervised learning.
problem Obtaining universally valid ground truth for supervised learning is challenging and costly.
method Gather multiple annotations from diverse individuals, verify and aggregate for training classifiers.
result Inferred ground truth improves classifier performance in sensitive tasks like mitosis detection.
Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similar…
New algorithms sample from complex path measures using neural networks.
problem Sampling from posterior path measures under a general prior process.
method Combines controlled equilibrium dynamics and optimization in infinite-dimensional probability space.
result The algorithms can be integrated with neural networks for learning target trajectory ensembles.
Paper formalizes anti-discrimination law in automated systems.
problem Algorithmic discrimination in legal contexts.
method Decision-theoretic framework grounded in UK anti-discrimination law.
result Introduced 'conditional estimation parity' metric for ML fairness.
CREAM models enable concept-grounded predictions and interpretability.
problem Designing models that can encode and extend prior knowledge about concept-concept and concept-task relationships.
method Proposes a flexible and efficient framework (CREAMs) that encodes arbitrary C−C and CoY relationships, incorporating a side-channel for incomplete concept sets. result CREAM models achieve competitive task performance while encouraging concept-grounded predictions, avoiding concept leakage and achieving black-box-level performance.
The paper evaluates and compares dimensionality reduction quality metrics without tuning.
problem Evaluating the quality of nonlinear dimensionality reduction visualizations is challenging.
method Comparison of dimensionality reduction quality metrics on datasets with known ground truth manifolds.
result A few methods consistently perform well, with one proposed as a benchmark.
This research detects and identifies human-made objects in 3D point clouds using novel methods.
problem Detect and identify human-made objects in 3D point clouds.
method Ground filtering, local information extraction, clustering using Marked Point Fields (MPFs) and Hessian matrix.
result The proposed method outperforms previous techniques in detecting human-made objects.
The paper defines metrics for evaluating disentangled representations in learning models.
problem Evaluating disentangled representations in learning models.
method Defining semantics and metrics for disentanglement learning.
result Proposed metrics correctly characterize representations learned by different methods.
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial…