We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the depend…
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48 results for “LOCO inference”
Cluster LOCO: A model-agnostic feature importance score for interpreting cluster outputs
problem Interpreting and auditing cluster outputs
method Cluster LOCO (Leave-One-Covariate-Out)
result More reliably recovers informative features than existing methods
We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which …
A new efficient test addresses limitations of knockoffs for conditional independence testing.
problem Testing conditional independence under model-X assumptions.
method Leave-One-Covariate-Out Conditional Randomization Test (LOCO-CRT)
result LOCO-CRT produces valid -values for familywise error rate control with minimal variability.
iLOCO measures feature interactions without assumptions, providing statistical inference.
problem Lack of methods to statistically infer feature interactions.
method iLOCO metric and LOCO inference for distribution-free, efficient computation.
result First inferential approach to detecting feature interactions.
New method disentangles high-order effects in feature importance.
problem Quantifying cooperative effects in feature importance.
method Adaptive Leave One Covariate Out (LOCO) method to decompose LOCO into two-body and higher-order components.
result Decomposes LOCO into two-body and higher-order components, highlighting synergistic and redundant effects.
A new method for feature importance inference without data splitting.
problem Feature importance inference for machine learning models.
method Minipatch ensembles for model-agnostic, distribution-free inference.
result Asymptotic validity of confidence intervals without data splitting.
The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.
problem Quantifying feature importance in the presence of feature correlation.
method LOCO and Shapley Values, critiquing their axioms and proposing new measures.
result Shapley values do not eliminate feature correlation, and a modified LOCO is recommended.
Paper compares feature selection methods using GCM and LOCO, showing GCM methods generally outperform LOCO.
problem Feature selection and importance estimation in model-agnostic settings.
method Comparison of feature selection methods related to GCM and LOCO under three model settings.
result GCM-related methods generally outperform LOCO under suitable regularity conditions, as shown by theoretical and empirical results.
The paper derives theoretical foundations for two common machine learning variable importance measures.
problem Understanding variable importance in machine learning problems.
method The paper derives closed-form expressions for Permute-and-Predict (PaP) and Leave-One-Covariate-Out (LOCO) methods.
result Theoretical derivations explain the behavior of PaP and LOCO under collinearity, linking them to coefficients and predictor variability.
Proposes a new method to estimate variable importance in black box models, mitigating correlation effects.
problem Correlation between covariates affects the interpretation of variable importance parameters.
method Develops a modified LOCO (Leave Out COvariates) method and uses semiparametric models for estimation.
result Shows how to estimate a modified LOCO method that mitigates correlation effects.
LoCo-RLHF models diverse human feedback with contextual information.
problem Heterogeneous human feedback from diverse contexts and preferences.
method Low-rank contextual preference model, PRS policy.
result LoCo-RLHF achieves tighter sub-optimality gap than existing methods.
Transformer model outperforms classical methods in childhood anemia prediction across diverse countries.
problem Generalizing childhood anemia prediction models across different countries and data scarcity.
method Transformer-based tabular foundation model compared to classical supervised methods using DHS data.
result Transformer model achieves lower Brier score and ECE in low-data settings, outperforming classical models.
New method R-LOCO improves local feature importance analysis.
problem Local attribution methods fail to accurately identify important features.
method R-LOCO segments input space into regions and applies global methods within.
result R-LOCO delivers more accurate local attributions.
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency …
LoCo learns local representations without end-to-end synchronization, improving performance on complex tasks.
problem Learning local representations without end-to-end synchronization constraints.
method Overlap local blocks to increase decoder depth and allow feedback from upper to lower layers.
result LoCo closes the performance gap between local learning and end-to-end contrastive learning.
This work investigates fundamental questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regu…
TabPFN models achieve state-of-the-art performance on tabular data tasks.
problem Lack of interpretability in TabPFN models.
method Adaptations of interpretability methods specifically designed for TabPFN, leveraging in-context learning and LOCO.
result Improved interpretability of TabPFN models through efficient computations and scalable data valuation methods.
MinShap improves feature selection accuracy and stability in complex models.
problem Challenging feature selection in unknown non-linear relationships.
method Combines Shapley values with a modified approach to feature selection.
result MinShap outperforms state-of-the-art algorithms in accuracy and stability.
Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computatio…
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate infe…
This work frames active inference through control as inference, offering robust control algorithms.
problem Active inference framework lacks practical sensorimotor control algorithms.
method Frame active inference through control as inference, presenting trajectory optimization as inference.
result AI may be framed as partially-observed CaI when the cost function is defined in observation states.
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
problem Efficient inference of neuronal ensembles from activity data.
method Modified MCMC algorithm with simulated annealing for hyperparameter control.
result Our method reduces computational cost while maintaining or improving inference accuracy.
Adding metadata abruptly changes network inference outcomes.
problem Understanding the impact of metadata on network inference.
method Investigated the effect of metadata on network inference problems.
result Metadata causes abrupt transitions in inference outcomes.
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.
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulationscs.LG
Paper introduces a diagnostic for approximate inference methods.
problem Estimating errors in probabilistic inference algorithms, especially for approximate methods.
method Repeatedly simulate datasets from the prior and perform inference on each, estimating a symmetric KL-divergence.
result A diagnostic for approximate inference methods can be estimated using symmetric KL-divergence.
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to-progr…
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…
A new method for safer statistical inference after predictions.
problem Statistical inference with pseudo-outcomes from machine learning predictions.
method Prediction De-Correlated Inference (PDC) framework.
result PDC consistently outperforms supervised methods and can adapt to any model.
Bayesian interpolants explain neural network inferences concisely.
problem Understanding neural network inferences.
method Adapting Craig interpolants for neural networks.
result Produces precise, understandable explanations.
Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable proba- bility density functions. This largely limits the design and implementation of v…
New comparison shows differences in how value is incorporated in AIF and CAI.
problem Clarifying the relationship between Active Inference and Control-as-Inference.
method Formal comparison of AIF and CAI frameworks.
result Primary difference is how value is incorporated into generative models.
Paper shows how to infer hidden states in neural networks analytically.
problem Intractability of Bayesian inference for neural networks.
method Leverage tractable approximate Gaussian inference (TAGI) for hidden states inference.
result Demonstrates inference of hidden states through constraints for various applications.
Variational Inference shows promise for Bayesian GARCH model estimation.
problem Bayesian estimation of GARCH-family models using Monte Carlo sampling.
method Variational Inference as an alternative to Monte Carlo sampling.
result Variational Inference is a reliable and competitive method for Bayesian learning in GARCH-like models.
Post-ADC inference corrects bias in statistical inference after active data collection.
problem Bias in inference after active data collection.
method Post-ADC inference framework that corrects bias from both ADC process and data-driven target construction.
result Valid inference for data collected by SMBO methods like GP-UCB and TPE.
A new particle algorithm improves mean-field variational inference.
problem Efficiently approximating nonparametric posterior distributions in machine learning.
method Introduces PArticle VI (PAVI), a novel particle-based algorithm for nonparametric mean-field approximation.
result Obtains non-asymptotic error bounds for PArticle VI, providing the first end-to-end guarantee for particle-based MFVI.
Meta-learn Bayesian inference for task-specific BNNs using amortised inference.
problem Efficiently learning Bayesian inference for small-scale probabilistic meta-learning.
method Replace global inducing points with actual data to create a set of approximate likelihoods, train a meta-model to learn these parameters across related datasets.
result Meta-learned inference can be applied to task-specific BNNs, improving efficiency and scalability.
Posterior inference in directed graphical models is commonly done using a probabilistic encoder (a.k.a inference model) conditioned on the input. Often this inference model is trained jointly with the probabilistic decoder (a.k.a generator model). If probabilistic encoder encounters complexities during training (e.g. s…
Bayesian method infers contextual bandit policies robustly.
problem Inference of contextual bandit policies in small sample sizes.
method Empirical likelihood for Bayesian inference.
result Accurate uncertainty measurements and policy comparison.
Exact selective inference with randomization for Gaussian regression models.
problem Exact selective inference in Gaussian regression models.
method Introduces a pivot for exact selective inference with randomization, reducing the problem to a bivariate truncated Gaussian distribution.
result Our pivot leads to exact inference and produces narrower confidence intervals than related methods.
Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying th…
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.
Prediction-Powered Inferencestat.ML
Valid inference from data and predictions.
problem Valid statistical inference with machine learning predictions.
method Framework for valid inference using machine learning predictions.
result Valid confidence intervals without assumptions on predictions.
EFI automates statistical inference for big data.
problem Statistical inference for model parameters based on observations.
method EFI uses stochastic gradient Markov chain Monte Carlo and sparse deep neural networks.
result EFI provides higher fidelity in parameter estimation and automates the inference process.
Derives time-averaged active inference from control principles.
problem Finite-horizon or discounted-surprise problems in active inference.
method Derives infinite-horizon, average-surprise active inference from optimal control principles.
result Unified objective functional for sensorimotor control.
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelsstat.ML
UA-SABI uses surrogates to speed up Bayesian inference for expensive models.
problem Inference for computationally expensive models is slow and uncertain.
method Combines surrogate modeling with Amortized Bayesian Inference (ABI) to propagate uncertainties.
result Reliable, fast, and repeated Bayesian inference for expensive models is achieved.
Deep learning enhances active inference for dynamic state spaces.
problem Limited applicability of active inference to continuous state spaces.
method Use of deep learning to approximate probability distributions for active inference.
result Active inference can be applied to continuous state spaces.