AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
Unified framework for semi-supervised learning using DRMM.
problem Efficiently using both labeled and unlabeled data in semi-supervised learning.
method Developed an EM algorithm for DRMM to learn from labeled and unlabeled data.
result Reported state-of-the-art performance on MNIST and SVHN.
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sens…
A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while …
Unified framework for invariance to nuisance and bias factors in neural networks.
problem Inducing independence to nuisance and bias factors in neural networks without labeled data.
method Unified invariance framework using competitive training between prediction and reconstruction tasks, coupled with disentanglement and adversarial learning.
result Outperforms previous works at inducing invariance to nuisance factors and achieves state-of-the-art performance at learning independence to biasing factors.
ES-VAE models skeletal pose trajectories by removing nuisance factors.
problem Handling camera orientation, subject scale, viewpoint, and execution speed in skeletal data.
method ES-VAE uses TSRVF representation on Kendall's shape manifold to isolate shape dynamics.
result ES-VAE outperforms standard VAEs and sequence modeling baselines in gait cycle prediction and action recognition.
A new VAE model learns disentangled latent factors without supervision.
problem Learning disentangled latent representations in unsupervised settings.
method Relevance-Factor-VAE model using total correlation and relevance indicators.
result Demonstrates superior disentanglement performance across multiple datasets.
Combines VAE and adversarial censoring for invariant representations.
problem Learning invariant representations from data with nuisance variables.
method Adversarial censoring in conditional VAEs to prevent nuisance variable recovery.
result Achieves invariance while preserving model learning performance.
Joint PLDA models two factors for better speaker verification.
problem Improving speaker verification accuracy with a nuisance condition.
method Generalizes PLDA to model two sample-dependent factors, modifies training procedure.
result Significant performance gains in multilingual speaker-verification tasks.
BGM-IV uses AI to estimate causal effects in complex data.
problem Estimating causal effects in high-dimensional, nonlinear settings with endogeneity.
method Structured latent generative modeling for posterior inference in a causally structured latent space.
result BGM-IV outperforms existing methods in high-dimensional covariate regimes.
Extends robust methods for causal inference, improving estimator performance.
problem Estimating causal effects in the presence of latent confounders.
method Minimax kernel machine learning for doubly robust functionals.
result Proposed method leads to robust and high-performance estimators.
Proposes a supervised VAE to reveal model invariances for interpretability.
problem Understanding and interpreting complex supervised models.
method Supervised variational auto-encoders (VAEs) with latent space invariances.
result Reveals model invariances through sampling nuisance dimensions.
Bayes-Factor-VAE models improve disentanglement of latent factors in data.
problem Disentangling latent factors in data using standard Gaussian priors is suboptimal.
method Introduced hierarchical Bayesian deep auto-encoder models with hyper-priors on latent variances.
result Bayes-Factor-VAEs outperform existing methods in latent disentanglement.
New method corrects biased predictions and uncertainty estimates in classification with nuisance parameters.
problem Tackles biased predictions and invalid uncertainty estimates in classification with nuisance parameters.
method Proposes a method that estimates ROC across the entire nuisance parameter space to devise invariant cutoffs.
result Demonstrates effective domain adaptation and valid prediction sets with high power.
Proposes a method to enforce structural constraints in auto-encoding variational Bayes.
problem Difficulty in imposing structural constraints on approximate posterior of generative models.
method Uses kernel-based measures of independence (dHSIC) to enforce independence between latent representations and nuisance factors.
result Shows superior performance in learning interpretable representations, especially in scRNA-seq.
Corrects nuisance variation in cell image embeddings using Wasserstein distance.
problem Separating biological signal from domain-specific nuisance variation in cell images.
method Minimizing distances between marginal distributions (Wasserstein distance) to adjust embeddings.
result Transformed embeddings carry improved biological signal and less domain-specific information.
A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.
problem Statistical inference for low-dimensional parameters in high-dimensional linear regression models.
method Mean-field variational Bayes approach, focusing on nuisance parameters and conditional distributions.
result Competitive numerical performance and theoretical guarantees for estimation and uncertainty quantification.
New method for clustering tasks with heterogeneous data.
problem Clustered multitask learning with semiparametric and heterogeneous nuisances.
method Adaptive fused orthogonal estimator with Neyman-orthogonal losses and data-driven fusion penalties.
result Achieves exact clustering recovery and pooled parametric convergence rates.
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, …
A new distribution fixes a common error in VAEs, improving image quality.
problem Using a Bernoulli likelihood for pixel data in VAEs.
method Introducing the continuous Bernoulli distribution.
result The continuous Bernoulli improves image quality across various metrics and datasets.
Paper introduces a method to predict molecule properties from diverse data sources.
problem Limited ability to accommodate scarce or fragmented training data.
method Adaptive Invariance using invariant risk minimization to generalize beyond heterogeneous data.
result Predictor outperforms state-of-the-art transfer learning methods by significant margin.
NURD improves model performance by distilling representations independent of nuisance variables.
problem Models trained under spurious correlations may fail on data with different nuisance-label relationships.
method Developed Nuisance-Randomized Distillation (NURD) to find representations independent of nuisance variables.
result NURD finds representations that perform better regardless of nuisance-label relationships.
Improved disentangled representation learning using a non-parametric latent density model.
problem Limited disentanglement in VAE due to constraints on latent density independence and complexity.
method Utilized the Indian Buffet Process (IBP) as a non-parametric latent density model to allow richer modeling capacity.
result IBP-VAE outperformed state-of-the-art VAEs in disentangling latent factors across various datasets.
Enhances PLDA scoring for multiple nuisance conditions.
problem Scoring PLDA models for multiple nuisance conditions.
method Developed likelihood ratios for scoring with multiple nuisance conditions.
result Improved PLDA model for joint analysis of multiple conditions.
Paper models graph edge dependencies using latent variables for community detection.
problem Graphs' edge dependencies not fully explained by community membership.
method Introduces auxiliary latent variables to model edge dependencies and analyzes conditions for exact recovery.
result Exact recovery possible by semidefinite programming down to maximum likelihood threshold.
This guide simplifies high-probability regret bounds in empirical risk minimization.
problem High-probability regret bounds in empirical risk minimization.
method Modular presentation, three-step recipe, localized Rademacher complexity, local maximal inequalities, metric-entropy integrals.
result Recover familiar rates for various function classes and derive regret bounds for nuisance components.
Framework learns to separate predictive from nuisance factors for robust machine learning.
problem Supervised models associate irrelevant factors with prediction targets, hurting generalization.
method Information-theoretic formulation for discovering and separating predictive and nuisance factors.
result State-of-the-art performance achieved without requiring nuisance annotations.
Optimally estimates a functional using nuisance function tuning and sample splitting.
problem Estimating optimal rates for a doubly robust functional.
method Combines nuisance function tuning and sample splitting strategies.
result Shows optimal rates of convergence for various estimators.
Variational autoencoders learn deep latent models.
problem Learning deep latent-variable models.
method Principled framework using variational inference.
result Introduction to variational autoencoders and extensions.
The paper provides guarantees for statistical learning with a nuisance parameter.
problem Statistical learning with an unknown nuisance parameter.
method Two-stage sample splitting meta-algorithm for target and nuisance parameters.
result Nuisance estimation error impacts excess risk bound of second order under Neyman orthogonality.
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
problem Computational cost and limited applicability of current SBI methods for realistic analyses.
method Simulation-Based Inference with Factorizable Normalizing Flows to model systematic variations.
result Efficient profiling of nuisance parameters and multivariate DoI in complex analyses.
A new method reduces variance in training discrete latent variable models.
problem High variance in stochastic gradient estimators for discrete latent variable models.
method Double control variates for score function estimators using Taylor expansions.
result Our method can have lower variance compared to other estimators.
ICA accurately estimates treatment effects even with confounders.
problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).
Paper introduces metrics to assess and control nuisance factors in sentiment analysis.
problem Challenges in learning invariant representations for sentiment analysis due to entangled nuisance factors.
method Developed two generalization metrics and a data filtering approach to control nuisance factors.
result Simple text classification baseline can be badly affected by product ID in sentiment analysis.
Approach for selecting features by discarding nuisance and correlated ones.
problem Large datasets with correlated and nuisance features.
method Laplacian score criterion, autoencoder architecture, concrete layer.
result Outperforms similar approaches in clustering performance.
A new probabilistic framework improves deep learning performance.
problem Improving deep learning models and understanding their limitations.
method Developed a probabilistic framework based on DRMM, a generative model capturing latent variables.
result DRMM outperforms DCNs in classification tasks, achieving state-of-the-art results.
Machine learning in high-energy physics faces challenges from nuisance parameters, which are reviewed and techniques to mitigate their impact are discussed.
problem Impact of nuisance parameters on machine learning performance in high-energy physics.
method Review and discussion of techniques including nuisance-parameterized models, modified or adversary losses, semi-supervised learning, and inference-aware techniques.
result Various methods to reduce the impact of nuisance parameters and improve model performance in high-energy physics.
Variational autoencoders often collapse, showing latent variables are non-identifiable.
problem Posterior collapse in variational autoencoders due to non-identifiable latent variables.
method Proves latent variable non-identifiability causes posterior collapse. Proposes latent-identifiable models using Brenier maps and input convex neural networks.
result Latent-identifiable models resolve posterior collapse and provide meaningful representations.
A new method for CI construction from nuisance parameter estimators.
problem Building confidence intervals from nuisance parameter estimators.
method Collaborative TMLE (C-TMLE) for inference.
result The C-TMLE yields a CI under certain conditions.
A new approach predicts next observations without explicit decoding for better control.
problem High-dimensional observations and unknown dynamics in real-world control tasks.
method Proposes a novel information-theoretic LCE approach using predictive coding to develop a decoder-free model.
result The model reliably learns a controllable latent space leading to superior performance.
Adversarial transfer learning improves stress assessment across users.
problem Transfer learning challenges in physiological biosignals.
method Disentangled nuisance-robust representations using adversarial networks.
result Adversarial framework enhances cross-subjects stress assessment.
DDVI uses diffusion models for variational inference, improving latent variable model performance.
problem Improving variational inference in latent variable models.
method Introduces diffusion-based variational posteriors trained with a regularized ELBO.
result Outperforms alternative variational posteriors on various benchmarks and a biology task.
Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.
problem Bayesian active learning with nuisance parameters leads to bias and dilemmas.
method Characterizes and mitigates negative interference by accurately estimating nuisance parameters.
result The extent of negative interference can be extremely large, and accurate estimation of nuisance parameters is critical.
New method for inference on strongly identified functionals even when nuisance functions are weakly identified.
problem Inference on continuous linear functionals of weakly identified nuisance functions defined by conditional moment restrictions.
method Proposes penalized minimax estimators for both the primary and debiasing nuisance functions, which can converge to fixed limits regardless of nuisance identifiability.
result Proves the asymptotic normality of a debiased estimator for the functional of interest, leading to asymptotically valid confidence intervals.
DSVNP uses global and local latent variables for improved neural process predictions.
problem Limited expressiveness of vanilla neural processes in capturing target-specific local variation.
method Introduces DSVNP combining global and local latent variables for prediction.
result Competitive prediction performance in multi-output regression and uncertainty estimation.
New method estimates uncertainty in knowledge graph embeddings using neural variational inference.
problem Estimating uncertainty in knowledge graph embeddings.
method Constructs an inference network conditioned on symbolic representations of entities and relation types in a Knowledge Graph.
result Improved predictive uncertainty estimates during link prediction.
tvGP-VAE models tensor-valued latent variables with Gaussian processes for better data structure representation.
problem Agnostic latent variables in VAEs ignore data structure correlations.
method Proposes tensor-variate Gaussian process prior for variational autoencoder.
result Explicitly modeling correlation structures improves model performance in reconstruction.
DML addresses biases in machine learning by estimating nuisance functions.
problem Bias in machine learning models due to nuisance functions.
method Double/Debiased Machine Learning (DML) approach to reduce biases.
result DML allows flexible estimation of nuisance functions without auxiliary assumptions.