Solves high-dimensional observation learning for control models.
problem Learning dynamics from high-dimensional images is challenging.
method Proposes a Beta DVBF approach to handle latent and observable space discrepancies.
result Demonstrates improved model learning from high-dimensional observations.
LOL-BO improves latent space Bayesian optimization over structured inputs.
problem Optimizing complex functions over high-dimensional, structured search spaces.
method Adapting trust regions from high-dimensional to structured settings, using a DAE to map inputs into a latent space.
result Achieves up to 20x improvement over state-of-the-art methods.
SAE improves VAE's latent space precision in high dimensions.
problem High-dimensional latent codes vs probabilistic inference in VAEs.
method Spherical Auto-Encoder (SAE) with spherical normalization on latent space.
result SAE improves latent code inference precision in high dimensions.
Meta-learning technique bypasses data limitations by optimizing latent space.
problem Challenges in gradient-based meta-learning with high-dimensional parameter spaces in low-data regimes.
method Learning a latent generative representation of model parameters and performing meta-learning in this low-dimensional space.
result Achieves state-of-the-art performance on few-shot classification tasks.
A new tree-Wasserstein distance for high-dimensional data with latent feature hierarchy.
problem Finding meaningful distances between high-dimensional data samples with latent feature hierarchy.
method Proposes a new tree-Wasserstein distance (TWD) for high-dimensional data with a latent feature hierarchy, using diffusion geometry and tree decoding.
result The proposed TWD effectively recovers the latent feature hierarchy and is efficient and scalable.
Method learns latent SDEs from high-dimensional time series.
problem Learning latent stochastic differential equations from time series data.
method Self-supervised learning with variational autoencoders and Euler-Maruyama approximation.
result Can recover SDE coefficients and latent variables up to isometry with infinite data.
New methods improve sampling from complex dynamical models.
problem Sampling from high-dimensional, non-linear latent dynamical models is computationally challenging.
method Introduce auxiliary MCMC and Particle Gibbs samplers with improved performance and parallelisation.
result Enhanced samplers maintain performance in high-dimensional latent spaces and support parallelisation.
A new method maps high-dimensional Bayesian inverse problems to lower dimensions.
problem High-dimensional Bayesian inverse problems with complex prior information.
method Data-driven VAE prior and KRnet map for posterior approximation in latent space.
result Efficiently reduces computational cost and approximates posterior distributions.
DeepMDP simplifies complex observations into continuous latent states.
problem Learning from high-dimensional observations in reinforcement learning.
method Trains a DeepMDP model that predicts rewards and next latent states.
result Optimization of DeepMDP objectives ensures quality of latent space and environment model.
VAELLS learns latent manifold structure to improve VAE model accuracy.
problem VAEs struggle with mismatched latent structure and global structure.
method Integrates learnable manifold model into latent space of VAE.
result Improves model accuracy by matching prior to data manifold structure.
This paper improves uncertainty characterization in neural networks by learning latent representations.
problem Challenges in quantifying uncertainty in high-dimensional neural network parameter spaces.
method Introduces a variational inference framework for Bayesian neural networks that encodes complex distributions in a low-dimensional latent space.
result Improves uncertainty characterization and model generalization compared to methods working directly in the parameter space.
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.
We consider the problem of extracting a low-dimensional, linear latent variable structure from high-dimensional random variables. Specifically, we show that under mild conditions and when this structure manifests itself as a linear space that spans the conditional means, it is possible to consistently recover the struc…
This paper tackles high-dimensional uncertainty quantification with semi-supervised learning.
problem High-dimensional uncertainty quantification due to the curse of dimensionality.
method Autoencoder for dimension reduction, DFN for mapping and reconstruction, GP for surrogate modeling, semi-supervised learning for accuracy.
result The framework effectively reduces uncertainty quantification and reliability analysis for high-dimensional problems.
Simple model explains manifold structure in high-dimensional data.
problem Understanding manifold structure in high-dimensional data.
method Latent Metric Model with latent variables, correlation, and stationarity.
result Establishes statistical explanation for manifold hypothesis.
Paper proposes a new method for learning latent representations for control problems.
problem Learning representations for control algorithms in high-dimensional observation spaces.
method Formulated a loss function (PCC) consisting of prediction, consistency, and curvature terms, derived an amortized variational bound.
result The new variational-PCC learning algorithm leads to superior control performance and more stable training.
This work learns latent representations to speed up exploration in complex environments.
problem Challenging exploration in high-dimensional state and action spaces with sparse rewards.
method Representation learning using prior experience to learn effective latent representations.
result Learned latent representations reduce the dimensionality of the search space for effective exploration.
New approach predicts under latent shifts using high-dimensional images.
problem Prediction under latent subgroup shifts with high-dimensional observations.
method Recognition-parametrised model (RPM) for identifying causal latent structure.
result Successfully adapts predictions for high-dimensional image data.
LD-EnSF speeds up data assimilation with sparse observations.
problem Efficiently assimilate sparse and noisy data into complex dynamical systems.
method LD-EnSF uses latent dynamics networks and history-aware LSTM encoders to process sparse observations without full-space simulations.
result Achieves significant speedups over existing methods while maintaining high accuracy.
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.
E&E uses contrastive learning to speed up SBI for high-dimensional systems.
problem Challenges in training high-dimensional emulators for complex systems.
method Contrastive learning for low-dimensional latent embedding and fast emulator.
result Superior performance in non-identifiable parameter estimation tasks.
SLAC learns latent representations for image-based RL tasks.
problem Challenges in learning policies from high-dimensional image observations.
method SLAC separates representation learning and task learning, using a latent variable model.
result SLAC outperforms model-free and model-based methods in image-based control tasks.
GCAE uses density estimation to achieve reliable disentanglement in latent space.
problem Disentangled learning representations suffer from reliability issues.
method GCAE uses Gaussian Channel Autoencoder with Dual Total Correlation (DTC) to avoid the curse of dimensionality.
result GCAE achieves highly competitive and reliable disentanglement scores.
Ensemble decoders to capture latent space topology in deep generative models.
problem Topological mismatch between latent space geometry and data manifolds.
method Using ensembles of decoders to compute geodesics on the expected manifold.
result Ensemble approach provides a simple and reliable way to capture model uncertainty in latent space.
Deep Sets approximates functions on sets with high-dimensional latent space.
problem Modeling functions of sets (permutation-invariant functions).
method Deep Sets, a method known to be a universal approximator for continuous set functions.
result Deep Sets' universal approximation property is only guaranteed with a sufficiently high-dimensional latent space.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
Modeling latent dynamics in high-dimensional event sequences without prior knowledge.
problem Modeling latent dynamics in high-dimensional event sequences with unknown marker relations.
method Adversarial imitation learning framework decomposed into latent structural intensity model, efficient random walk model, and seq2seq discriminator.
result Effective detection of hidden network among markers and decent prediction for future events.
Model learns latent dynamics of complex systems with closed transformation paths.
problem Euclidean latent space does not match data structure.
method Incorporates manifold model into autoencoder latent space.
result Generates transformation paths and classifies samples on same path.
A hybrid method for subspace learning reduces noise in high-dimensional data.
problem Challenges in high-dimensional data where p >> n.
method Hybrid dimensionality reduction technique mapping some features to a low-dimensional subspace.
result More accurate estimation of latent space and lower reconstruction error.
In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear(in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we i…
IAF improves variational inference by scaling to high-dimensional spaces.
problem Flexible variational inference of posteriors over latent variables.
method Inverse autoregressive flow (IAF) using invertible transformations based on autoregressive neural networks.
result IAF significantly improves upon diagonal Gaussian approximate posteriors.
ODE2VAE learns latent dynamics for sequential data.
problem Learning latent dynamics for high-dimensional sequential data.
method Deep generative second order ODE model with Bayesian neural networks.
result State-of-the-art performance in long-term motion prediction and imputation.
CausalEGM estimates causal effects by encoding confounders, improving performance in high-dimensional settings.
problem Challenges in estimating causal effects with high-dimensional confounders.
method CausalEGM framework using generative modeling to decouple confounders and estimate causal effects.
result CausalEGM outperforms existing methods in binary and continuous treatment settings, especially with large sample sizes and high-dimensional confounders.
New metrics and transformations improve understanding of generative models' latent spaces.
problem Understanding the structure of latent spaces in generative models.
method Developed new metrics and transformations to study latent space geometries.
result Improved meaningful distances and data visualizations in latent spaces.
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
problem Challenges in high-dimensional, nonlinear Bayesian filtering with sparse observations.
method A novel data assimilation method using latent representations and a coupled VAE for efficient state encoding and reconstruction.
result Latent-EnSF outperforms traditional methods in accuracy, convergence, and efficiency for complex systems.
Optimizes latent space of VAEs using decoder uncertainty to generate valid objects.
problem Lack of robustness in optimizing VAE latent space for black-box properties.
method Importance sampling-based estimator of decoder epistemic uncertainty to guide optimization.
result Improves trade-off between black-box objective and validity of generated samples.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
Moment Pooling reduces latent space dimensions in machine learning models.
problem High-dimensional latent spaces in machine learning models are hard to interpret.
method Moment Pooling extends Deep Sets networks to arbitrary multivariate moments.
result Latent dimensions as small as 1 can achieve similar performance to higher dimensions.
Confidence-based filtering reveals latent structure in diffusion models.
problem Unclear latent structure in diffusion models.
method Confidence scores from a classifier.
result Class-relevant latent structure emerges under confidence-based filtering.
New methods test correlation between network structure and node features.
problem Assessing correlation between network structure and node-level covariates.
method Four novel methods based on linear models and canonical correlation analysis.
result Theoretical guarantees and computational efficiency for testing network dependency.
ILDM combines diffusion and latent learning for generative modeling on unknown manifolds.
problem Diffusion models struggle with high-dimensional data and lack geometric structure.
method ILDM integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds.
result ILDM significantly improves generation quality compared to standard models.
Study improves interpretability in generative models by disentangling latent variables in scientific datasets.
problem Extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings.
method Introducing Aux-VAE, a novel architecture within the VAE framework, which disentangles latent variables by guiding them with auxiliary variables.
result Aux-VAE achieves disentanglement with minimal modifications to the standard VAE loss function, validated on multiple datasets.
Proposes a novel method for detecting novelty in multi-modal data.
problem Challenges in detecting novelty in high-dimensional, multi-modal data.
method Orthogonalized latent space for disentangling features and defining novelty score.
result Proposed method outperforms state-of-the-art algorithms in novelty detection.
Autoencoder optimizes data embedding for accurate PDF reproduction.
problem Inaccurate PDF reproduction in latent space of VAEs.
method Rate-Distortion Optimization guided autoencoder with isometric property.
result Our method achieves isometric data embedding and tractable PDF relations.
Proposes GPLFR for predicting high-dimensional outputs with few data.
problem Predicting high-dimensional outputs from limited data.
method GPLFR combines Gaussian process and linear-Gaussian decoding for high-dimensional prediction.
result GPLFR outperforms existing methods in predicting high-dimensional outputs.
Study improves Gaussian Process Latent Variable Model for noisy longitudinal data.
problem Noisy and incomplete longitudinal data makes learning representations difficult.
method Augment variational approximation with systematic samples of unseen observations.
result Demonstrates improved learning of Gaussian Process Dynamical Systems in noisy data.
Researchers analyze and improve latent space in NAR models.
problem Latent space resolution and range issues in NAR models.
method Detailed analysis of GNN latent space structure, proposing and testing solutions.
result Improvements in majority of algorithms on CLRS-30 benchmark.
FSGD uses latent factors to scale SGD for high-dimensional learning.
problem Scalable optimization in high-dimensional machine learning.
method Factor-Augmented SGD (FSGD) that operates on streaming data.
result Established theoretical framework for latent factor estimation error in SGD.