We provide comments on the article "High-dimensional simultaneous inference with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
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48 results for “simultaneous inference”
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis o…
Proposes sparsified intervals for high-dimensional regression coefficients.
problem Challenges of high-dimensional regression coefficient inference.
method Sparsified simultaneous confidence intervals.
result Intervals can shrink some coefficients to zero, indicating unimportance.
Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional linear regression model in which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous pos…
A distributed bootstrap method for high-dimensional data reduces communication rounds efficiently.
problem Simultaneous inference on massive, high-dimensional data stored across many machines.
method Distributed bootstrap based on de-biased lasso with efficient cross-validation tuning.
result Theoretical lower bound on communication rounds for statistical accuracy and efficiency.
Efficiently estimates marginal posteriors for complex simulations.
problem Bayesian inference in high-dimensional, intractable likelihood scenarios.
method Simulates and estimates low-dimensional marginal posteriors, using truncated indicators.
result Simulator efficiency and robustness testing of inference results.
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneous…
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
problem Improving simultaneous inference in complex settings like arrays and graphs.
method Generalized empirical Bayes approach based on probabilistic symmetry.
result BEB outperforms existing methods in denoising arrays and spatial data.
Develops a method for estimating networks and covariate associations in compositional data.
problem Estimating network interactions and covariate associations for compositional data.
method Hierarchical Bayesian model with spike-and-slab priors for edge and covariate selection, variational EM for inference.
result The proposed method outperforms existing methods in network recovery accuracy.
We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator. Our results call into question common implicit assumptions that tighter ELBOs are better vari…
Develops methods to adjust prediction set coverage based on post-selection analysis.
problem Adjusting prediction set coverage after initial analysis to better fit specific needs.
method Post-selection conformal inference to adjust miscoverage levels.
result Allows for trade-off between coverage and prediction set quality.
The paper provides high-probability bounds on false discovery proportions in conformal inference.
problem Existing methods fail to provide high-probability bounds on the realized false discovery proportion.
method Constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution.
result Establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds.
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference network…
We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model -- an actively studied topic in statistics and machine learning. In the noi…
Paper introduces MGLasso for multiscale graph inference in clustering and network analysis.
problem Graphical models in high-dimensional data analysis need to handle clustering and sparsity simultaneously.
method MGLasso combines clustering and graph inference through a convex relaxation of k-means and hierarchical clustering. It uses CONESTA for regularization.
result MGLasso improves network interpretability by estimating graphs at multiple scales.
This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur representation, which significantly generalizes the traditional Bahadur representation in parametric models, that is, Bahadur [Ann. Inst. S…
M-flows learn data manifolds and densities, improving manifold learning and inference.
problem Representing datasets with manifold structure more faithfully.
method Combining normalizing flows, GANs, autoencoders, and energy-based models, with a new training algorithm.
result M-flows learn data manifolds better than standard flows and provide handles for dimensionality reduction.
Composing graphical models with neural networks for structured representations and fast inferencestat.ML
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencode…
The importance of interpretability of machine learning models has been increasing due to emerging enterprise predictive analytics, threat of data privacy, accountability of artificial intelligence in society, and so on. Piecewise linear models have been actively studied to achieve both accuracy and interpretability. Th…
This paper speeds up inference in large hierarchical models.
problem Inference in large hierarchical models is slow and difficult.
method Amortized variational inference with shared parameters.
result Amortized inference is as accurate as a full-rank Gaussian but much faster.
NCoRE learns counterfactual representations for combined treatments.
problem Estimating individual response to multiple simultaneous interventions.
method Neural conditional representation with modulators for cross-treatment interactions.
result NCoRE significantly outperforms existing methods in counterfactual treatment effect estimation.
SNVI combines likelihood estimation with variational inference for efficient Bayesian inference.
problem Bayesian inference in models with intractable likelihoods.
method Sequential Neural Variational Inference (SNVI) that combines likelihood-estimation with variational inference.
result SNVI is more computationally efficient than previous algorithms without sacrificing accuracy.
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods. ITs adaptively sample from hierarchica…
BSFP method reveals latent patterns in multi-omic data for predicting lung function in HIV-associated OLD.
problem Limited understanding of multi-omic molecular phenomena and clinical outcomes in obstructive lung disease.
method Bayesian Simultaneous Factorization and Prediction (BSFP) method for multi-omic data, accommodating imputation and full posterior inference.
result BSFP reveals distinct clusters of patients with OLD and multi-omic patterns related to lung function decline.
Private Causal Inferencestat.ML
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…
Unobserved confounding is a central barrier to drawing causal inferences from observational data. Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously. In this paper, we present two simple, analytical counterexam…
Model predicts future values and fills in missing data.
problem Missing data in time series data.
method End-to-end time series model with two neural networks.
result Framework performs well in imputation and forecasting.
The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards. In contrast, active inference, an emerging framework within cognitive and computational neuroscience, proposes that agents act to maximize the evidence for a biased generative model. Here, we illustrate how i…
The paper improves matrix completion with auxiliary covariates using LS estimation.
problem Matrix completion with noisy data and auxiliary covariates.
method Iterative least squares estimation with statistical properties derived.
result Asymptotic normal distributions of estimators for low-rank matrix and coefficient matrix.
Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that…
BOED improves SBI by optimizing experimental designs and inference functions.
problem Efficiently use experimental resources for better inference on complex models.
method Link mutual information bounds between SBI and BOED, optimizing both design and inference.
result BOED improves inference in real-world simulators in epidemiology and biology.
Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach, which, for fully observable systems is at times competitive with numerical integration. However, for many real-world applications, only sparse observations are avail…
Framework for joint learning of tasks on dementia data with missing values.
problem Lack of multi-task learning, handling time-dependent data, and missing values in dementia forecasting.
method Proposes SSHIBA model using Bayesian variational inference for imputation and combined information from different views.
result SSHIBA model outperforms baselines in predicting diagnosis, ventricle volume, and clinical scores in dementia.
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator. In order to solve such an issue, we provide a learning objec…
ELS framework improves safety alignment by dynamically steering LLMs towards helpful responses.
problem Over-Refusal in Aligned Large Language Models
method Fine-tuning free framework using an Energy-Based Model (EBM) to dynamically steer LLMs during inference.
result Extensive experiments show a significant reduction in false refusals (from 57.3% to 82.6%) while maintaining safety performance.
Bayesian model infers strengths from noisy tennis match outcomes.
problem Ranking tennis players from match outcomes.
method Bayesian approach to infer unobserved strengths and mapping function.
result Bayesian approach robust to different model specifications.
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with…
Improves probabilistic programming by analyzing program structure.
problem Inefficiency and limitations of single inference algorithms in probabilistic programming.
method Three novel techniques: static and dynamic analyses to adapt programs for more efficient inference.
result Improves probabilistic programming by making inference more efficient.
We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The data-generating process is defined by Simultaneously Transforming and Rounding (STAR) a continuous-valued process, which produces a flexible family of integer-valued distributions capable of modeli…
Paper develops an online EM algorithm for graph signal inference from streaming data.
problem Joint inference and clustering of graph signals with non-white excitation.
method Mixture model with low-rank plus sparse prior, online EM algorithm.
result Proposed online EM algorithm converges to MAP solution.
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiotemporal TMP dynamics. These models are controlled by high-dimensional …
Estimates price elasticity from autocorrelated time series using causal graphs.
problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.
The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networksstat.ML
RAF model explains neural networks' dual rule learning and fact memorization.
problem Understanding how neural networks learn rules and memorize facts simultaneously.
method Introduces the Rules-and-Facts (RAF) model to bridge generalization and memorization.
result Characterizes conditions for simultaneous rule learning and fact memorization in neural networks.
Paper addresses regret minimization and inference in high-dimensional online decision-making.
problem Regret minimization and statistical inference in high-dimensional online decision-making.
method Integrates ε-greedy bandit algorithm with hard thresholding for sparse bandit parameters and debiasing method for inference.
result Achieves either regret or -consistent inference, with trade-off between exploration and exploitation.
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy. Most current techniques optimize model size, model accuracy and inference time in different stages, resulting in subopt…
This work improves Gaussian process inference using mixtures of experts and nested SMC samplers.
problem High computational and memory costs of Gaussian processes.
method Mixtures of Gaussian process experts with nested SMC samplers.
result Significantly improved inference compared to importance sampling.
A new method for efficient inference and model selection in SBMs using OT.
problem Efficient inference and model selection in stochastic block models.
method Interpreting MLVI as srGW with entropic regularization, then unregularizing for sparse solutions, and adding a sparsity-promoting regularizer.
result The method consistently recovers SBM parameters and selects the number of clusters in finite samples.