New methods learn from PU data with non-representative positives.
problem Learning from PU data with non-representative positive classes.
method Integrates negative-unlabeled and unlabeled-unlabeled learning, or uses a recursive risk estimator.
result Effective across various real-world datasets and forms of positive bias.
Paper tackles survival data analysis with positive and unlabeled observations.
problem Traditional survival analysis yields biased results with positive-unlabeled data.
method Developed parametric, nonparametric, and machine learning models for positive and unlabeled survival data.
result Proposed estimation method provides valid results for positive-unlabeled survival data.
In this short note, we prove positivity of Brown-York mass under quasi-positive boundary data which generalize some previous results by the authors. The corresponding rigidity result is obtained.
The Positive Mass Theorem for special singular initial data.
problem Proving the positive mass theorem for data with a codimension one singularity.
method Using asymptotically flat spin initial data sets with matching Bartnik data condition involving spacetime rotations.
result Established a spacetime positive mass theorem and rigidity statement.
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-c…
Paper proposes a new approach to stabilize GAN training by treating generated data as unlabeled.
problem Traditional GAN training treats generated data as negative, ignoring their potential quality.
method Defines positive and unlabeled classification for GANs, treating generated data as unlabeled.
result PUGAN achieves comparable or better performance than sophisticated discriminator stabilization methods.
Proves positive mass theorem for spin initial data sets with arbitrary ends and dominant energy shields.
problem Proving the positive mass theorem for spin initial data sets with various ends and energy shields.
method Modification of Witten's approach involving an additional independent timelike direction in the spinor bundle.
result Positive mass theorem for spin initial data sets with arbitrary ends and dominant energy shields.
New method for learning with non-Euclidean data using decomposable kernels.
problem Difficulty in using classical kernels for non-Euclidean data.
method Reproducing kernel Krein space (RKKS) methods for kernels that admit a positive decomposition.
result Invariant kernels can be used for learning in non-Euclidean spaces.
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contribu…
Assessing the performance of a learned model is a crucial part of machine learning. However, in some domains only positive and unlabeled examples are available, which prohibits the use of most standard evaluation metrics. We propose an approach to estimate any metric based on contingency tables, including ROC and PR cu…
Paper extends positive energy theorem to anti-de Sitter spacetimes.
problem Proving positive energy theorem for weighted anti-de Sitter spacetimes.
method Generalized positive energy theorem for 3D anti-de Sitter initial data sets.
result Positive energy theorem proved for weighted anti-de Sitter spacetimes.
Positive energy theorems for spin initial data with charge in higher dimensions.
problem Establishing positive energy theorems for spin initial data with charge in dimensions n≥4. method Using a dominant energy condition and asymptotically flat ends, extending classical theorems.
result Extending classical positive energy theorems to spin initial data with charge.
Explains how to prove positive mass theorem with boundary in dimensions less than 8.
problem Proving the positive mass theorem with boundary conditions.
method Uses established results to prove various versions of the theorem.
result Various versions of the positive mass theorem are proven for initial data sets with boundary in dimensions less than 8.
New model tackles PU data with better accuracy.
problem Addressing positive and unlabeled data challenges.
method Double Exponential Tilting Model (DETM)
result DETM effectively handles selected at random PU data.
A new PU classifier PUAL tackles trifurcate data issues.
problem Training classifiers on trifurcate data containing only labeled-positive instances and unlabeled instances.
method PUAL classifier with asymmetric loss and kernel-based algorithm.
result PUAL achieves satisfactory classification on trifurcate data.
Paper proposes a method to estimate true positive proportion without knowing it.
problem Bias in binary classifier performance due to different positive item proportions.
method Maximum likelihood estimator for true proportion of positives.
result Method accurately estimates true positive proportion in data sets.
Solves Jang's equation for hyperboloidal data in 4-7 dimensions, proving positive mass theorem.
problem Proving the positive mass theorem for asymptotically hyperbolic initial data sets in specific dimensions.
method Solves Jang's equation with hyperboloidal initial data in dimensions 4-7.
result Non-spinor proof of the positive mass theorem in 4-7 dimensions.
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic prediction is to capture spatial and temporal relations between future traffic flows and historical traffic due to highly dynamical patterns of h…
New method debiases selection bias in PU classification with exposure data.
problem Binary classification from positive and unlabeled data with selection bias.
method Automatic Debiased PUE (ADPUE) learning method.
result ADPUE outperforms traditional PU learning methods on various datasets.
A novel method for learning DAGs from positive-valued data.
problem Causal discovery from observational data of positive-valued variables.
method Hybrid Moment-Ratio Scoring (H-MRS) algorithm combining moment-based scoring and log-scale regression.
result H-MRS integrates log-scale Ridge regression for moment-ratio estimation with a greedy ordering procedure based on raw-scale moment ratios, followed by Elastic Net-based parent selection.
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literatur…
Proves spacetime positive mass theorem in all dimensions.
problem Proving the spacetime positive mass theorem in arbitrary dimensions.
method Using Brendle--Wang's Riemannian positive mass theorem approach.
result Proves the spacetime positive mass theorem for all dimensions.
Solves Jang equation for hyperboloidal data, proving positive mass theorem.
problem Proving the positive mass theorem in asymptotically hyperbolic 3D spacetimes.
method Solves Jang equation with hyperboloidal initial data, applies to positive mass theorem.
result Non-spinor proof of positive mass theorem in 3D asymptotically hyperbolic spacetimes.
Proves spacetime positive mass theorem for spin initial data sets with arbitrary ends.
problem Proving the spacetime positive mass theorem for specific spacetime configurations.
method Solving a mixed boundary value problem for the Dirac-Witten operator with a Callias potential.
result Established spacetime positive mass theorem for asymptotically flat spin initial data sets with arbitrary ends.
Proves density and mass theorems for specific initial data sets.
problem Initial data sets with boundary in spacetime.
method Harmonic asymptotics and dominant energy condition.
result Spacetime positive mass theorem for initial data sets with apparent horizon boundary.
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typi…
The paper proves a spacetime positive mass theorem for singular initial data sets.
problem Proving the positive mass theorem for initial data sets with corners.
method Extending Hirsch-Kazaras-Khuri's method to singular cases using Hirsch-Miao-Tsang ideas.
result Integral lower bound on spacetime mass and characterisation of zero mass.
A method to generate multi-label data from single positive annotations.
problem Generating multi-label datasets is costly and impractical.
method Single-to-multi-label (S2M) sampling using Markov chain Monte Carlo.
result S2M sampling enables high-quality multi-label data with minimal annotation cost.
W. Simon proved a conformal positive mass theorem, which was used to prove uniqueness of black holes later. In this note, we will generalize Simon's conformal positive mass theorem in two directions. First we will consider spacetime version of conformal positive mass theorems on asymptotically flat initial data set. Ne…
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Rigidity results for initial data sets related to the positive mass theorem.
problem Rigidity of initial data sets in general relativity.
method Establishing conditions for weak outermost marginally outer trapped surfaces and rigidity results for Riemannian manifolds.
result Marginally outer trapped surfaces are weakly outermost under certain conditions.
New method corrects skewed confidence for PbN classification.
problem Weakly supervised binary classification with biased negative data.
method Corrects skewed confidence in negative data to improve classifier.
result Reduces distortion in posterior probability for PbN classification.
Constructs positive energy representations from Toda equations Stokes data.
problem Creating positive energy representations of affine algebras.
method Using Stokes data of tt*-Toda equations to construct representations.
result Illustrates construction with examples in conformal field theory.
A new method for PU learning improves classification error on CIFAR-10.
problem Learning from positive and unlabeled data in practical applications.
method A simple yet effective data augmentation method based on consistency regularization.
result Achieves an averaged improvement of 3.40 points in classification error on CIFAR-10.
We propose an approach to reduce both computational complexity and data storage requirements for the online positioning stage of a fingerprinting-based indoor positioning system (FIPS) by introducing segmentation of the region of interest (RoI) into sub-regions, sub-region selection using a modified Jaccard index, and …
Improves PU learning for imbalanced data with practical AUL estimation and new training method.
problem Training binary classifiers on datasets with only positive and unlabeled samples.
method Asymptotic unbiased AUL estimation and ProbTagging for imbalanced data.
result ProbTagging increases AUC by up to 10% on industrial and artificial data sets.
The paper proves positive energy-momentum theorems for charged AdS initial data sets.
problem Proving positive energy-momentum theorems for charged asymptotically AdS initial data sets.
method Introducing a charged energy-momentum functional and establishing positive theorems under a dominant energy condition.
result The charged energy-momentum functional is non-negative on a natural real cone.
A new method ReCPE removes the need for a distributional assumption in PU learning.
problem Training binary classifiers with only positive and unlabeled data without negative data.
method Regrouping CPE (ReCPE) that constructs an auxiliary distribution to ensure positive data support is never in negative data support.
result ReCPE improves all state-of-the-art CPE methods on various datasets, indicating the need for the distributional assumption.
New method classifies manifold-valued data using Riemannian geometry.
problem Classifying data on curved Riemannian manifolds.
method Probabilistic Learning Vector Quantization on Symmetric Positive Definite Matrices.
result The method outperforms traditional Euclidean methods on manifold-valued data.
Proves positive mass theorem for hyperbolic manifolds with ends.
problem Establishing positive mass theorem for complex initial data sets.
method Used spectral PSC, Jang equation, and quantitative shielding theorem.
result Proved positive mass theorem for asymptotically hyperbolic manifolds.
Proves spacetime positive mass theorem with corners.
problem Proving a positive mass theorem for spacetime with corners.
method Deformation theorem with corner conditions, asymptotically flat initial data.
result Exterior end satisfies E≥∣P∣ in every dimension n≥3. Study introduces a new framework for policy learning without positivity assumption.
problem Learning optimal treatment assignment policies from observational data with constraints.
method Incremental propensity score policies and semiparametric efficiency theory.
result Validated framework's performance through numerical experiments.
In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting strategy for U data, so that a decent decision boundary can be found. However, given limited P data, the conventional PU models tend to suffe…
The visibility transformation embeds data position into signature features for efficient pattern recognition.
problem Embedding absolute position into signature features for efficient pattern recognition.
method The visibility transformation is put on a theoretical footing and used to embed absolute position into signature features efficiently.
result The generated feature set simplifies pattern recognition by accommodating nonlinear functions of absolute and relative values.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
The paper proposes methods to estimate positive examples and learn classifiers from mixed data.
problem Estimating the proportion of positive examples and learning classifiers from a mixture of positive and unlabeled data.
method Best Bin Estimation (BBE) for Mixture Proportion Estimation and Conditional Value Ignoring Risk (CVIR) for PU-learning.
result The proposed methods significantly improve both mixture proportion estimation and classifier learning.
We establish a type of positive energy theorem for asymptotically anti-de Sitter Einstein-Maxwell initial data sets by using Witten's spinoral techniques.
The paper extends the spacetime positive mass theorem to multiple time dimensions.
problem Proving the nonnegativity of mass in spacetimes with multiple time dimensions.
method Generalizing the spacetime positive mass theorem to include multiple time dimensions and showing mass nonnegativity through energy inequalities.
result Equality in the energy inequality implies a foliation by flat submanifolds.