Method uses experimental data to correct hidden confounding in observational data.
problem Hidden confounding in observational data invalidates causal conclusions.
method Experimental grounding to correct hidden confounding in causal effect models.
result Proves conditions for consistent estimator of causal effect models.
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.
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 a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
New method falsifies causal discovery results without ground truth.
problem Evaluation of causal discovery algorithms without ground truth data.
method Detects incompatibilities between causal graphs learned on different subsets of variables.
result Detection of incompatibilities can falsify wrongly inferred causal relations.
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.
Introduces a new geometric method for optimal experimental design.
problem Restrictive invariance properties of traditional OED approaches based on probability densities.
method Mutual transport dependence (MTD) using optimal transport theory.
result Demonstrates high-quality designs and flexibility compared to standard methods.
Noise affects the effectiveness of interpolating models, especially those with strong inductive biases.
problem The impact of noise on interpolating models with strong inductive biases.
method Analyzing linear and classification models with sparse ground truths, proving fast rates for interpolators.
result Strong inductive biases can lead to faster but noisier interpolators, contrary to intuition.
Proposes a method for explaining tabular data using copulas.
problem Lack of ground truth for explainability in complex datasets.
method Uses copulas to specify statistical properties and build intuition.
result Demonstrates improved explainability on logistic regression and correlation use cases.
Deep learning tackles X-ray noise without clean data.
problem Lack of clean X-ray images for deep learning denoising.
method Uses Stein's Unbiased Risk Estimator (SURE) to train a deep neural network.
result SURE-based approach effectively denoises X-ray images.
Study proposes worst+gap measure for better DG evaluation.
problem Lack of comprehensive exploration of average measure in DG evaluation.
method Introduced worst+gap measure and compared it with average measure.
result Worst+gap measure provides a more accurate approximation of true DG performance.
New framework validates counterfactual estimations in network interference settings.
problem Challenges in causal effect estimation and validation in network interference settings.
method Introduces a distribution-preserving network bootstrap and counterfactual cross-validation procedure.
result Validates counterfactual estimations in diverse network interference settings.
Paper presents a machine learning method to improve significance tests for misspecified linear models.
problem Misspecification of linear assumptions in social science models leads to inaccurate significance levels.
method Apply machine learning to fit ground truth function, calculate linear approximation, and adjust the estimator.
result The method significantly outperforms linear regression for non-linear ground truth functions.
New algorithms improve efficiency in learning from personalized rewards.
problem Learning from personalized rewards in recommendation systems.
method Developed provably efficient algorithms with sublinear regret for context-dependent feedback.
result Introduced a Lipschitz reward estimator that improves generalization performance.
Paper proposes GANs for generating business process suffixes and remaining times.
problem Generating accurate suffixes and remaining times for business process models.
method Encoder-decoder GAN architecture with Gumbel-Softmax distribution for categorical sequences.
result The approach outperforms baselines in suffix and remaining time prediction accuracy.
A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like user-defined heuristics or knowledge bases, to label training data. Prior work has explored learning accuracies for these source…
Two methods estimate effect size for online experiments, improving accuracy and efficiency.
problem Determining the correct effect size for online experiment duration.
method Two approaches: hierarchical models and utility theory.
result Proposed methods outperform baseline approaches in accuracy and efficiency.
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn with…
Authors provide a fair comparison of GNNs for graph classification.
problem Lack of reproducibility and rigorousness in experimental procedures for GNNs.
method Controlled and uniform framework with over 47,000 experiments.
result GNNs do not fully exploit structural information on some datasets.
Algorithm learns two-layer residual units using ReLU activations from samples.
problem Learning two-layer residual units from samples.
method Design layer-wise objectives as functionals, formulate ERM as QP, solve using LP, prove statistical consistency.
result Strong statistical consistency and robustness of the algorithm.
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbea…
Mathematical framework for differential machine learning in finance.
problem Theoretical assumptions in financial models and their impact on machine learning algorithms.
method Rigorous mathematical framework for differential machine learning in finance.
result Theoretical grounding enhances the predictive capabilities of neural networks in financial applications.
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.
Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In the…
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.
Study shows auditing fairness of personalized interventions is impossible due to unknown ground truths.
problem Auditing fairness of personalized interventions in social services, education, and healthcare.
method Point-identification of quantities under monotone treatment response assumption, providing sensitivity analysis for violations.
result Proves impossibility of auditing fairness using standard metrics and provides methods for auditing using partially-identified ROC and xROC curves.
Smartphone data shows promise but accuracy issues remain.
problem Inaccurate travel surveys from smartphone data.
method Training algorithms on data quality and validating labels.
result Machine learning methods are limited by data quality.
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.
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.
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…
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.
Classification of ground state solutions to critical Dirac equation on spheres.
problem Classifying ground state solutions of the critical Dirac equation.
method Exploiting conformal covariance and relating to the Yamabe equation.
result Ground state solutions are given by Killing spinors up to conformal diffeomorphisms.
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 learning method for prosthetic arms without explicit rewards.
problem Learning a prosthetic arm to interact with users without explicit reward signals.
method Interaction-Grounded Learning, observing multidimensional context and feedback vectors, discovering latent reward signal.
result The algorithm can discover a latent reward signal and ground its policies for successful interaction.
New research highlights flaws in evaluating clustering algorithms using classification datasets.
problem Flaws in evaluating clustering algorithms using classification datasets.
method Advanced visualization and dimension reduction techniques to expose flaws.
result Current practice of evaluating clustering algorithms may produce misleading results.
PHIBP predicts infectious disease outbreaks in sparse data regions.
problem Predicting outbreaks in regions with no historical data.
method Poisson Hierarchical Indian Buffet Process (PHIBP) framework.
result PHIBP provides accurate outbreak predictions and meaningful insights in sparse data settings.
We provide some new results of the ground state of quantum layers.
New method uses kernel methods to approximate ground states of quantum Hamiltonians efficiently.
problem Approximating ground states of quantum Hamiltonians using neural networks is computationally expensive.
method Introduces a statistical learning approach using kernel methods to make optimization trivial.
result Ground state properties of arbitrary gapped quantum Hamiltonians can be reached with polynomial resources.
Active learning reduces simulation needs for high-fidelity mobility maps.
problem Efficiently training machine learning classifiers for high-fidelity mobility maps.
method Active learning based on PAC learning theory to reduce simulation needs.
result Our sampling algorithm trains neural networks with higher accuracy using less than half the number of simulations.
New method uses adiabatic principles to improve ground-state preparation in quantum computing.
problem Challenges in variational training of complex energy landscapes.
method Iterative Hamiltonian deformation complemented with adiabatic principles.
result Consistent convergence to target ground state through sequence of intermediate problems.
Enhanced VMC methods improve neural wavefunction training.
problem Efficiently training neural wavefunctions in VMC to converge to energy minimum.
method Rayleigh-Gauss-Newton (RGN) optimization and parallel tempering sampling.
result RGN method achieves superlinear convergence with reduced computational cost.
New method uses Multiple Choice Learning for speech separation.
problem Ambiguous task of assigning model predictions to ground truth signals.
method Uses Multiple Choice Learning (MCL) instead of Permutation Invariant Training (PIT).
result MCL matches PIT performance but is computationally advantageous.
Proposes a self-supervised method for generating spatial audio from monaural audio and video.
problem Generating spatial audio from monaural audio and video recordings is challenging and expensive.
method Uses a self-supervised network with an auxiliary classifier to classify video channels and generate spatial audio.
result The proposed method effectively generates spatial audio from monaural audio and video.
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
problem Generating reliable multi-step ahead prediction intervals for time series data.
method Adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) based on conformal prediction principles.
result AEnbMIMOCQR provides close to exact coverage and robustness to distribution shifts.
A neuro-inspired architecture learns without supervision using clustering and predictive coding.
problem Achieving continual learning without supervision.
method Neuro-inspired architecture based on online clustering and hierarchical predictive coding.
result The architecture achieves continual learning without supervision.
Tests for classifier independence without ground truth labels.
problem Validation of classifier independence without ground truth labels.
method Exact solution for independent binary classifiers using algebraic geometry.
result Self-consistent test for classifier independence without ground truth labels.
Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neuro…