Generic groups can't move spaces but have rich actions.
problem Generic torsion-free groups and their actions.
method Model theoretic forcing
result Generic countable torsion-free groups do not admit nontrivial locally moving actions.
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48 results for “Rubin actions”
We give general classification and structure theorems for actions of groups of homeomorphisms and diffeomorphisms on manifolds, reminiscent of classical results for actions of (locally) compact groups. This gives a negative answer to Ghys' "extension problem" for diffeomorphisms of manifolds with boundary, as well as a…
This paper addresses clustering with missing data using Rubin's rules.
problem How to pool partitions and assess instability when data are incomplete after multiple imputation.
method Consensus clustering and bootstrap theory are used to address the problem.
result New rules for pooling partitions and assessing instability are proposed and validated.
Bayes-UCBVI tackles reinforcement learning with a new upper confidence bound method.
problem Optimizing exploration in reinforcement learning without bonuses.
method Bayes-UCBVI uses a quantile of a Q-value function posterior as an upper confidence bound.
result Proves a regret bound of order for tabular reinforcement learning.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
Paper reconciles RCM and SCM frameworks for causal inference.
problem Clarifying the relationship between RCM and SCM frameworks.
method Neutral logical perspective, previous work, and abstract representation.
result Every RCM emerges as an abstraction of some representable RCM.
Homogeneous M2 dualshep-th
Motivated by the search for new gravity duals to M2 branes with supersymmetry --- equivalently, M-theory backgrounds with Killing superalgebra for --- we classify homogeneous M-theory backgrounds with symmetry Lie algebra for . We f…
The paper proves rigidity results for certain supergravity backgrounds in 11 dimensions.
problem Supersymmetry gap problem for supergravity backgrounds in 11 dimensions.
method Study of curvature restrictions and use of bijective correspondence between filtered deformations of Lie superalgebras and highly supersymmetric backgrounds.
result Rigidity results for specific supergravity backgrounds with low rank 4-form and high Killing spinor dimension.
Paper presents a method to align unpaired samples across different modalities.
problem Challenges in collecting paired samples for multimodal representation learning.
method Uses propensity score alignment based on Rubin's framework to estimate a common space for unpaired samples.
result Optimal transport matching significantly improves alignment in real-world data.
Estimates counterfactual outcomes linking observed and unobserved data.
problem Estimating expected counterfactual outcomes for individuals.
method Introduces retrospective counterfactual estimators and prediction intervals linking observed and unobserved outcomes.
result Retrospective counterfactual estimators and prediction intervals asymptotically satisfy valid coverage under standard causal assumptions.
New method refines prediction intervals for individual treatment effects using cross-world correlation.
problem Uncertainty in individual treatment effects for high-stakes decisions.
method Introduces cross-world correlation parameter ρ to refine prediction intervals for individual treatment effects.
result Achieves more stable and accurate coverage of prediction intervals for individual treatment effects.
It is common practice in using regression type models for inferring causal effects, that inferring the correct causal relationship requires extra covariates are included or ``adjusted for''. Without performing this adjustment erroneous causal effects can be inferred. Given this phenomenon it is common practice to inclu…
Starting from the recent classification of quotients of Freund--Rubin backgrounds in string theory of the type AdS_{p+1} x S^q by one-parameter subgroups of isometries, we investigate the physical interpretation of the associated quotients by discrete cyclic subgroups. We establish which quotients have well-behaved cau…
Boundary Estimates for the Monge-Ampère Equation in the Polygons with Guillemin Boundary Conditionsmath.AP
Extends boundary estimates for Monge-Ampère equations in polygonal domains.
problem Boundary regularity for Monge-Ampère equations on convex polytopes with specific boundary conditions.
method Schauder-type techniques, inspired by Donaldson's work on the Abreu equation.
result Establishes boundary regularity result for Hölder continuous right-hand sides.
Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e.g. Gelman, Carlin, Stern, and Rubin [2004, p. 165]. This paper presents a method for latent variable models by pulling back the data into the space of latent variables, and c…
We classify Freund-Rubin backgrounds of eleven-dimensional supergravity of the form AdS_4 x X^7 which are at least half BPS; equivalently, smooth quotients of the round 7-sphere by finite subgroups of SO(8) which admit an (N>3)-dimensional subspace of Killing spinors. The classification is given in terms of pairs consi…
The volumes, spectra and geodesics of a recently constructed infinite family of five-dimensional inhomogeneous Einstein metrics on the two bundles over are examined. The metrics are in general of cohomogeneity one but they contain the infinite family of homogeneous metrics . The geodesic flow is sh…
We study the eleven dimensional supergravity equations which describe a low energy approximation to string theories and are related to M-theory under the AdS/CFT correspondence. These equations take the form of a non-linear differential system, on with the characteristic degeneracy at t…
Framework improves PV forecasting by accounting for missing data uncertainty.
problem Uncertainty from missing data in PV power data.
method Combines stochastic multiple imputation with Rubin's rule.
result Improves prediction interval calibration without sacrificing point prediction accuracy.
ProPublica's analysis of recidivism predictions produced by Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software tool for the task, has shown that the predictions were racially biased against African American defendants. We analyze the COMPAS data using a causal reformulation of the un…
New geometric flow equations describe how space-time dimensions change.
problem Understanding how the number of space-time dimensions affects geometry.
method Developed D-flow equations to model the variation of space-time geometries.
result Solutions for D-flow equations on D-dimensional spheres and Freund-Rubin Compactification.
Locally approximating groups of homeomorphisms reveal manifold properties.
problem Understanding the structure and properties of homeomorphism groups on manifolds.
method Analyzing dense subgroups in Euclidean charts and interpreting first-order arithmetic.
result Locally approximating groups of homeomorphisms uniquely determine manifold properties.
CNN identifies AGN host galaxies from Sloan Digital Sky Survey data.
problem Identifying AGN host galaxies using traditional methods is time-consuming.
method Trained a convolutional neural network on 210,000 galaxies.
result CNN can distinguish AGN host galaxies from non-active galaxies.
Finding active galactic nuclei through Finkastro-ph.IM
Fink AGN classifier achieves high accuracy in classifying active galactic nuclei.
problem Classifying active galactic nuclei from astronomical data.
method Built features from photometric points and color estimation. Used active learning for optimized training sample. Applied traditional machine learning algorithms.
result Achieved 98.0% accuracy in classifying real alerts from ZTF.
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical m…
Smooth Freund-Rubin backgrounds of eleven-dimensional supergravity of the form AdS_4 x X^7 and preserving at least half of the supersymmetry have been recently classified. Requiring that amount of supersymmetry forces X to be a spherical space form, whence isometric to the quotient of the round 7-sphere by a freely-act…
New framework infers causal shifts in event sequences under out-of-domain interventions.
problem Inferring causal relationships in event sequences without considering out-of-domain interventions.
method Proposes a new causal framework to define ATE, designs an unbiased ATE estimator, and uses a Transformer-based neural network model.
result Demonstrates superior performance in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.
This paper improves credit line impact analysis by considering spending as a distribution.
problem Previous studies on credit lines' impact on spending have overlooked the distributional nature of spending.
method Developed a distribution-valued estimator framework to extend existing real-valued estimators.
result Credit lines positively influence spending across all quantiles, but more towards luxuries as they increase.
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether th…
New taxonomy for structured missingness in large-scale databases.
problem Handling missing values in structured data.
method Introducing a new taxonomy for Structured Missingness (SM) and embedding it within existing mechanisms.
result Demonstrated the impact of Structured Missingness on inference and prediction.
New method speeds up galaxy analysis from hours to seconds.
problem Infeasibility of state-of-the-art SED analyses for large surveys.
method Amortized Neural Posterior Estimation (ANPE) for scalable Bayesian inference.
result Posterior distributions of 12 model parameters estimated in seconds per galaxy.
We prove the following Theorem: Let X be a nonempty compact metrizable space, let be a sequence of natural numbers, and let be a sequence of nonempty closed subspaces of X such that for each k in N, . Then there exists a compact metriz…
Randomization is minimax-optimal for variance in experimental design, even with structure.
problem Designing optimal randomized experiments for variance minimization.
method Analyzing permutation symmetric and non-symmetric sets of outcomes, proposing inference-constrained MSOD.
result Randomization is minimax-optimal for variance, even with structure, and requires uniformity constraints for Fisher's exact test.
We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data, this is a central problem in various application domains, including healthcare, social sciences, and online advertising. Within the Neyman Rubin potential outcomes model, we use the Kullback Leibler…
New complete panel dataset for LMICs helps analyze innovation and development.
problem Lack of complete data for empirical analyses in LMICs.
method Predictive Mean Matching multiple imputation technique.
result Created a large dataset of 47 variables for 82 LMICs from 2005-2019.
New method improves causal inference by estimating complex treatment effects with active learning.
problem Traditional causal inference frameworks ignore interference and assume independent treatment effects.
method Active Learning in Causal Inference with Interference (ACI) using Gaussian process and genetic algorithms.
result ACI achieves accurate effects estimation with reduced data requirements in complex interference scenarios.
Calibrated Prediction-Powered Inference improves semisupervised mean estimation by calibrating prediction scores.
problem Semisupervised mean estimation with a small labeled sample and a large unlabeled sample, and miscalibrated prediction models.
method Calibrated Prediction-Powered Inference (Calibeating) post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation.
result Calibrated Prediction-Powered Inference can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference.
Estimates classification rules from partially classified data.
problem Estimating Bayes' rule for unclassified observations in partially classified data.
method Fitting a g-component mixture model by maximum likelihood (ML) via the EM algorithm.
result Asymptotic relative efficiency (ARE) of Bayes' rule estimated from partially classified samples.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
problem Estimating the heterogeneous causal effects of crashes on highway traffic.
method Neyman-Rubin Causal Model, Conditional Shapley Value Index, Structural Causal Model, Doubly Robust Learning.
result Different types of crashes have varying impacts on traffic, with rear-end crashes causing the most severe congestion.
Homologically trivial symplectic cyclic actions need not extend to Hamiltonian circle actionsmath.SG
We give examples of symplectic actions of a cyclic group, inducing a trivial action on homology, on four-manifolds that admit Hamiltonian circle actions, and show that they do not extend to Hamiltonian circle actions. Our work applies holomorphic methods to extend combinatorial tools developed for circle actions to stu…
Reduces proper actions to simpler core actions for analysis.
problem Understanding properties of proper actions on manifolds.
method Extending Skjelbred and Straume's construction to non-compact groups, focusing on core of actions.
result Properties of proper actions are determined by simpler core actions.
Introduces Conditional Action Trees to simplify RL action spaces.
problem Challenges in RL with large, complex action spaces.
method Structures action spaces and reduces complexity through Conditional Action Trees.
result Demonstrates effectiveness in reducing action space and improving decision making.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencodercs.LG
One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous studies relied on human…
Study polar actions on Damek-Ricci spaces, proving existence and finding examples.
problem Characterize polar actions on Damek-Ricci spaces.
method Prove criteria for isometric actions to be polar, find examples, and classify actions.
result Non-trivial polar actions exist on all Damek-Ricci spaces.
Reduction principles for proper actions on smooth manifolds.
problem Proper actions on smooth manifolds and their properties.
method Exhibit constructions and prove reduction principles for proper actions.
result Reduction principles hold for proper actions, polar actions, and copolarity.
Sections of polar actionsmath.DG
Totally geodesic sections found in polar actions.
problem Understanding sections of polar actions on Riemannian manifolds.
method Elementary proof of a folklore result.
result Sections of polar actions are totally geodesic.
Simplifies large action space bandits by selecting representative actions.
problem Efficiently managing large action spaces with correlated outcomes.
method Random sampling and solving of bandit instances to identify representative actions.
result The algorithm selects a smaller set of representative actions that perform nearly as well as the full action space.
We study isometric actions on Riemannian symmetric spaces of noncompact type which are induced by reductive algebraic subgroups of the isometry group. We show that for such an action there exists a corresponding isometric action on a dual compact symmetric space, which reflects many properties of the original action. F…