AKO improves stability and power of Knockoff inference.
problem Instability of Knockoff-based inference.
method Aggregation of Multiple Knockoffs (AKO) method.
result AKO maintains FDR control while improving stability and power.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “inference stability”
Optimism stabilizes Thompson Sampling for adaptive inference in multi-armed bandits.
problem Subtle inferential properties of Thompson Sampling under adaptive data collection.
method Introduced optimism as a key mechanism to restore stability and validity of inference.
result Suitably implemented optimism stabilizes Thompson Sampling and enables asymptotically valid Wald inference.
Efficient inference for adaptive data with directional stability condition.
problem Efficient inference on scalar targets after adaptive data collection.
method Introduces directional stability, a weaker condition than i.i.d. data, and shows asymptotic normality and efficiency of estimators.
result Estimators remain asymptotically normal and semiparametrically efficient under directional stability.
New stability condition explains MAP inference success.
problem Understanding why MAP inference works well in practice.
method Introduced block stability condition and proved LP persistence.
result Real-world instances have large stable regions.
Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theoremstat.ML
Paper stabilizes bandit learning with regularization, improving inference under adaptive sampling.
problem Challenges in statistical inference with adaptive sampling.
method Refined stability condition for online algorithms, using regularized stochastic-mirror-descent-style methods.
result Derives precise regret bounds and asymptotic normality, showing necessity of regularization for valid inference.
Proposes stabilized weights for causal inference using isotonic calibration.
problem Stability and bias issues in inverse propensity weighting.
method Post-hoc isotonic calibration of inverse propensity weights.
result Improves performance of doubly robust estimators of average treatment effect.
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instancesstat.ML
New LP method recovers MAP solution from noisy stable instances.
problem MAP inference on noisy stable instances.
method Designing an algorithm to find nearby perturbation stable instances and using LP relaxation.
result LP approximately recovers the MAP solution from noisy stable instances.
Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In t…
The paper studies stability of mean-field variational inference for log-concave distributions.
problem Stability of mean-field variational inference for log-concave distributions.
method Novel approach via linearized optimal transport, lifting non-convex problem to convex optimization over transport maps.
result Dimension-free Lipschitz continuity of the MFVI optimizer with respect to the target distribution, measured in 2-Wasserstein distance.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
problem Determining causal directionality in quantum systems under MNAR observation.
method Integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
result Achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics.
A new algorithm reduces inference error in adaptive contextual bandits.
problem Challenges in statistical inference for adaptive contextual bandits.
method Proposes a regularized EXP4 algorithm that satisfies the Lai-Wei stability condition.
result Valid Wald-type confidence intervals for linear functionals can be achieved without the price of adaptivity.
The paper explains why approximate algorithms perform well on stable instances.
problem Structured prediction problems often find solutions close to optimal on real-world instances.
method The paper analyzes alpha-expansion and LP relaxation algorithms on MAP inference in Ferromagnetic Potts models.
result The paper provides stability conditions under which these algorithms provably recover the optimal MAP solution.
The paper analyzes the stability of parameter recovery in Linear Structural Equation Models.
problem Understanding the stability of parameter recovery in Linear Structural Equation Models (LSEM).
method Condition number analysis for bow-free LSEM and random input parameter analysis.
result For bow-free LSEM, parameter recovery is stable under certain conditions and with high probability.
Lyapunov's second theorem is an essential tool for stability analysis of differential equations. The paper provides an analog theorem for incremental stability analysis by lifting the Lyapunov function to the tangent bundle. The Lyapunov function endows the state-space with a Finsler structure. Incremental stability is…
Statistical inference for misspecified contextual bandits is challenging due to adaptivity issues.
problem Statistical inference for misspecified contextual bandits
method Inverse-probability-weighted Z-estimation framework
result Consistent and asymptotically normal estimator with sandwich variance estimator
New method uses subtractive mixture models for approximate inference.
problem How to effectively use subtractive mixture models for approximate inference.
method Design expectation estimators for IS and learning schemes for VI with SMMs.
result Empirical evaluation shows SMMs can approximate distributions effectively.
Stabilizes deep Bayesian neural networks with self-stabilizing priors.
problem Brittleness and difficulty in training deep Bayesian neural networks.
method Signal propagation theory, reformulated ELBO, self-stabilizing priors.
result Improved convergence and robustness in training deeper networks and noisier settings.
The paper explores how dynamic preconditioning affects the CLT in online averaging.
problem When does dynamic preconditioning preserve the Polyak-Ruppert CLT?
method The authors decompose the averaged error and identify a stabilization-rate threshold for the CLT to hold.
result The CLT holds if the dynamic remainder vanishes in and the stabilization rate exceeds a threshold.
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descentcs.LG
Paper proposes LSVGD to stabilize GAN training via Langevin Stein Variational Gradient Descent.
problem Mode collapse and performance deterioration in GAN training.
method Langevin Stein Variational Gradient Descent (LSVGD) incorporating noise to stabilize training.
result LSVGD improves performance and stability of various GAN models.
The study analyzes numerical stability in large language models using mixed-precision arithmetic.
problem Numerical stability of large language models using low-precision arithmetic.
method Developed a mixed-precision analysis of transformer inference, deriving bounds for condition numbers and forward error.
result Established that numerical stability is determined by the interplay between weight magnitude and the growth of the residual stream.
Transformers learn algorithms for in-context learning with bounds and stability analysis.
problem Understanding and formalizing in-context learning as an algorithm learning problem.
method Formalizing ICL as a multitask learning problem, deriving generalization bounds, and analyzing stability.
result Transformers can implement near-optimal algorithms for classical regression tasks with i.i.d. and dynamic data.
We assess cluster stability by trimming extreme points and tracking data range reduction.
problem Assessing stability of one-dimensional clusters.
method Probabilistic method using diameter-shrinkage ratio to track data range reduction.
result Our method achieves higher accuracy than classical tests in small or noisy samples.
Unified variational inference framework reveals GAN's limitations and proposes improvements.
problem Limitations of GAN training and lack of completeness in loss function.
method Reinterpretation of variational inference and revealing special cases of GAN, VAE, etc.
result Proposes a regularization term to improve GAN training stability.
New findings show many popular bandit algorithms are unstable, contradicting minimax optimality.
problem Challenges in statistical inference from bandit algorithms due to adaptive, non-i.i.d. nature.
method Analysis of stability properties of optimism-based bandit algorithms.
result Widely used minimax-optimal UCB-style algorithms are unstable.
Veridical Data Sciencestat.ML
Proposes PCS framework for veridical data science results.
problem Ensuring reliable, reproducible, and transparent data science results.
method PCS workflow with predictability, computability, and stability principles.
result PCS inference procedures demonstrate favorable performance in high-dimensional simulations.
Wasserstein variational inference uses optimal transport for stable likelihood-free training.
problem Approximate Bayesian inference with stability and flexibility for implicit distributions.
method Optimal transport theory, Sinkhorn iterations, and backpropagation.
result Stable likelihood-free training method for autoencoders and probabilistic programs.
AutoPC optimizes hyperparameters for the PC algorithm to improve its performance.
problem The unsupervised nature of the PC algorithm makes it difficult to tune the Type I level.
method AutoPC optimizes directly for a chosen metric and ensures stability through a second run.
result AutoPC consistently outperforms state-of-the-art methods across multiple metrics.
Develops a new approach for algorithmic recourse in AI systems.
problem Tackles the problem of providing recommendations for reversing negative AI decisions.
method Introduces a causal framework that models recourse as a process over pre- and post-intervention outcomes, allowing for partial stability and resampling of latent variables.
result Demonstrates the value of the proposed methods on real and semi-synthetic datasets.
Stabilized neural differential equations enforce constraints on dynamical systems.
problem Ensuring dynamical systems preserve known constraints like conservation laws.
method SNDEs with a stabilization term to enforce manifold constraints.
result SNDEs outperform existing methods and broaden constraint types.
Paper stabilizes persistent homology rank functions for statistical inference.
problem Stability issues in persistent homology rank functions.
method Derive stability results for rank functions under FDA metrics.
result Rank functions stabilize, improving statistical inference.
New framework limits testing algorithmic stability under computational constraints.
problem Testing algorithmic stability is computationally hard.
method Unified framework for quantifying stability hardness.
result Exhaustive search is the only universally valid mechanism for certifying stability.
Hida-Matérn Kernelstat.ML
New Hida-Matérn kernels enable flexible process priors and efficient GP inference.
problem Flexible modeling of stationary processes with oscillatory components.
method Introducing a new class of covariance functions (Hida-Matérn kernels) and their state space representations.
result Efficient Gaussian Process inference and improved numerical stability.
IPAD method enhances interpretability and stability in econometric forecasting.
problem Lack of stable interpretable forecasting in econometric settings.
method Model-X knockoffs framework with intertwined probabilistic factors decoupling (IPAD).
result Method controls the fraction of wrongly discovered features for enhanced interpretability.
Paper examines stability of Bayesian posterior measures using integral probability metrics.
problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.
New theory relaxes assumptions for optimal cooperative inference.
problem Achieving optimal cooperative inference under strong assumptions.
method Relaxing restrictive assumptions, demonstrating convergence, robustness, and stability.
result Generalized cooperative inference for any discrete joint distribution.
Paper proposes an unsupervised feature selection algorithm with stability guarantees.
problem Feature selection for dimension reduction and interpretability.
method Proposes a novel unsupervised feature selection algorithm with stability guarantees.
result The algorithm has superior generalization performance and stable selected features.
This paper studies Thompson sampling's arm-pull dynamics and inference, revealing key differences from UCB algorithms.
problem Understanding the precise arm-pull dynamics in Thompson sampling algorithms.
method Developed new approaches to analyze the arm-pull count process and noise processes, including inverse process and reparametrization methods.
result Arm-pull count is asymptotically deterministic only for suboptimal or unique optimal arms, revealing a unifying principle of stability.
C-Learner improves stability of plug-in estimators for causal inference.
problem Limited overlap between treatment and control groups leads to unstable estimates.
method Constrained learning framework that achieves stability and asymptotic properties.
result Constrained learning produces stable estimates with desirable asymptotic properties.
Develops methods for cooperative Bayesian inference.
problem Cooperation between learning agents.
method Sequential Bayesian inference approaches.
result Theoretical foundation for cooperative inference.
ConDiSim uses diffusion models to approximate complex system posteriors efficiently.
problem Simulation-based inference of systems with intractable likelihoods.
method Conditional diffusion model with forward and reverse processes.
result Effective posterior approximation across various benchmark and real-world problems.
Paper estimates manifold reach using convexity defect function.
problem Estimating the reach of submanifolds from point clouds.
method Relates reach to convexity defect function, uses stability properties, and combines with recent estimators.
result Uniform expected loss bound and minimax rate lower bounds for reach estimation are provided.
CADR estimator improves inference for contextual bandit data.
problem Valid inference on contextual bandit data.
method CADR estimator for policy value, addressing adaptive data collection challenges.
result CADR provides correct coverage of confidence intervals.
MAML++ improves MAML's stability and performance.
problem Stability and high generalization in few-shot learning.
method Various modifications to MAML to stabilize and improve performance.
result Significant improvements in stability, generalization, and computational efficiency.
mGPfusion predicts protein stability changes using a novel Gaussian process method.
problem Limited experimental data for predicting protein stability changes.
method Bayesian data fusion model combining experimental and molecular simulation data.
result mGPfusion outperforms state-of-the-art methods in predicting protein stability.
In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information abou…
New method stabilizes inputs to DNN for secure inference with LHE.
problem Incompatibility of LHE with nonlinear functions in DNN.
method Training with polynomial approximations and Min-Max normalization.
result Loss in prediction accuracy reduced to small values or eliminated.
An adaptive time-stepping controller improves stability and accuracy of ResNets.
problem Improving stability and performance of ResNets using adaptive time stepping.
method Developed an adaptive time-stepping controller based on Runge-Kutta-Fehlberg method.
result Demonstrated improved stability and accuracy of ResNets without additional overhead.
Sequential coordinate ascent is more robust in high-dimensional linear regression.
problem Behavior difference between sequential and parallel coordinate ascent in variational inference.
method Comparison of sequential and parallel coordinate ascent algorithms in high-dimensional linear regression.
result Sequential algorithm converges under more relaxed conditions than parallel algorithm.