New framework for multi-domain translation using autoencoders.
problem Learning probabilistic coupling between different domains.
method Learning multiple uncoupled autoencoders under shared latent distribution.
result New autoencoders can be added sequentially without retraining.
Study on reliability of latent reuse in diffusion models under distribution shift.
problem When can latent spaces from a source dataset be reused for a target dataset with different distributions?
method Considered a source-target setting with approximately low-dimensional datasets near different subspaces. Analyzed the target-domain score error due to principal-angle misalignment and target ambient noise.
result Latent reuse is reliable only if the source and target subspaces are close and the target ambient noise is not too amplified.
Unpaired multi-domain causal representation learning is possible with sufficient conditions.
problem Learning shared causal representation from unpaired data across domains.
method Identify sufficient conditions for joint distribution and shared causal graph recovery.
result Practical method to recover shared latent causal graph from marginal distributions.
N-VAE separates class-specific and shared factors in data.
problem Multimodal distribution of generative factors due to class distinction.
method N-VAE model with class-conditioned and shared latent spaces.
result Model effectively disentangles class-dependent and shared factors.
HCL learns shared and modality-specific latent representations for multimodal data.
problem Binary shared-private decomposition inadequately represents shared information across subsets of modalities.
method Hierarchical Contrastive Learning framework combining latent-variable formulation, structural sparsity, and contrastive objective.
result HCL accurately recovers hierarchical structure and improves predictive performance on multimodal data.
PAVI speeds up VI for large-scale studies by sharing parameterization across i.i.d. variables.
problem Challenges in Bayesian inference for large population studies with many latent parameters.
method Designing plate-amortized variational inference (PAVI) to share parameterization across i.i.d. variables.
result Significant speedup in training large-scale hierarchical variational distributions.
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Diricihlet alloc…
DGPs learn from multiple tasks using shared and private latent processes.
problem Improving learning performance and information transfer between tasks.
method Non-linear mixtures of latent processes with shared and task-specific components, using hard or soft sharing.
result DGPs outperform other multi-task learning models across various settings.
New model for multiplex networks learns shared structure.
problem Complex network structures not well modeled by existing methods.
method Latent space model for multiplex networks with shared structure.
result Model recovers latent positions with sufficient separation.
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …
We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model which is fast to train and which achieves state-of-the-art or better results on several standard benchmark datasets. Specifically, we use a…
Improved neural population modeling using shared features and ensemble detection.
problem Missing shared coding properties in neural latent variable models.
method Feature sharing across tuning curves and soft clustering of neurons.
result More interpretable and better-performing neural population models.
New method disentangles shared and private latent factors in multimodal data.
problem Challenges in disentangling shared and private latent factors in multimodal data.
method Proposes a modification to existing multimodal Variational Autoencoders (MMVAE) to better handle modality-specific variation.
result Demonstrates improved robustness of modified MMVAE to modality-specific variation.
Proposes a deep probabilistic multi-view model for multi-view learning.
problem Learning from multiple related views with shared latent structure.
method Probabilistic Canonical Correlation Analysis (CCA) in latent space, deep generative networks, variational inference.
result Efficient variational inference approximates posterior distributions of latent multi-view layer.
Improves autoencoder reconstruction quality by approximating latent space with adversarial learning.
problem Ambiguity in autoencoder reconstructions and difficulty in matching true distribution.
method Adversarially Approximated Autoencoder (AAAE) using GAN for flexible latent space approximation.
result Generates more faithful reconstructions and maintains latent manifold structure.
New model extracts shared brain activity patterns from fMRI data.
problem Challenges in aggregating multi-subject fMRI data due to variability.
method Shared Gaussian Process Factor Analysis (S-GPFA) incorporating temporal information.
result Model reveals ground truth latent structures and replicates experimental performance.
We propose an unsupervised object matching method for relational data, which finds matchings between objects in different relational datasets without correspondence information. For example, the proposed method matches documents in different languages in multi-lingual document-word networks without dictionaries nor ali…
SPLICE method disentangles shared and private latent variables from multi-view data.
problem Lack of methods to characterize nonlinear relationships and preserve geometric information in multi-view data.
method Neural network-based approach to infer disentangled, interpretable representations of shared and private latent variables.
result SPLICE yields more interpretable representations by preserving geometry and is more robust to incorrect latent dimensionality.
MetaVAE learns transferable latent representations across related distributions.
problem Generative models struggle to adapt to new distributions.
method Doubly-amortized variational inference sharing computation across related models.
result MetaVAE significantly outperforms baselines on image classification tasks.
New method identifies latent variables with sparse perturbations.
problem Identifying latent variables with minimal supervision.
method Weakly supervised representation learning with sparse perturbations.
result Identification of latent variables up to specified blocks.
Unified MTL framework for heterogeneous data integrates shared and task-specific encoders.
problem Efficiently sharing information across multiple tasks with heterogeneous data.
method Dual-encoder framework with task-shared and task-specific encoders.
result Unified algorithm alternates learning task-specific and shared encoders and coefficients.
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amoun…
We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previous…
A new method for conditional sampling using paired Wasserstein Autoencoders.
problem Conditional sampling from complex data distributions.
method Derive a novel loss function for Wasserstein Autoencoders to enable sampling from OT-type couplings.
result Learned cost-optimal transport maps and conditional sampling from an OT-type coupling.
Paper proposes VAE-BPTF for better tensor factorization of sparse, imbalanced count data.
problem Inference of Bayesian Poisson-Gamma models for sparse and imbalanced count data is challenging.
method Variational auto-encoder framework with multi-layer perceptron networks for complex update information sharing and reweighting.
result VAE-BPTF outperforms current models in reconstruction errors and latent factor coherence across real-world datasets.
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Emb…
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution…
This work explains how maximizing latent correlations across multiple data views helps in identifying shared and private components.
problem Understanding how to identify shared and private components in multiview data.
method An intuitive generative model of multiview data is adopted, and latent correlation maximization is shown to guarantee the extraction of shared components.
result Latent correlation maximization guarantees the extraction of shared components across views and disentangles private information.
Meta-learning improves OoD detection with minimal in-distribution data.
problem Efficient OoD detection with limited in-distribution data.
method Meta-learning in latent space with Gaussian mixture models.
result Meta-learning enhances OoD detection performance.
A new meta-learning method using shared variational inference.
problem Meta-learning with uncertainty over model parameters.
method Shared amortized variational inference network for conditional prior and posterior.
result Prevents collapse of conditional prior to Dirac delta function.
New MCMC methods improve efficiency for large network inference.
problem Efficiency of Metropolis within Gibbs for large networks.
method Combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo.
result New methods outperform Metropolis within Gibbs on synthetic and real networks.
A new model integrates covariates with grade of membership analysis for better latent structure recovery.
problem Improving latent structure recovery in multivariate categorical data analysis.
method Covariate-assisted grade of membership model exploiting shared low-rank simplex geometry.
result Auxiliary covariates can provably improve latent structure recovery, leading to faster convergence rates.
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…
CARE improves LLM aggregation by accounting for shared confounders.
problem LLM judges' correlated errors due to shared latent confounders.
method CARE explicitly models judges' scores as true quality and confounders.
result CARE reduces aggregation error by up to 26.8% across various benchmarks.
Identifies latent actions and dynamics from offline data with diverse demonstrators.
problem Recovering latent actions and environment dynamics from action-free trajectories.
method Assumes distinct policies for each demonstrator, identifies latent transitions and policies via matrix factorization.
result Identifies latent transitions and demonstrator policies up to permutation.
Proposes a flexible feature allocation model for sparse factor analysis.
problem Sparse data and rigid assumptions in traditional exploratory tools.
method Adaptive latent feature sharing with control over feature sparsity.
result Derives a novel adaptive Factor analysis (aFA) and aPPCA for flexible dimensionality reduction.
The paper introduces a DRM for causal inference, offering a flexible method to analyze counterfactual distributions.
problem Estimating mean causal effects is limited; a distributional perspective is needed for a more thorough understanding.
method The paper employs a semiparametric density ratio model (DRM) with an empirical likelihood (EL) approach to estimate counterfactual distribution functions.
result The DRM framework enables direct and transparent causal inference from a distributional perspective, validated by numerical studies.
Proposes MD-LiNA for multi-domain latent factor causal discovery.
problem Discovering causal structures among latent factors from multi-domain data.
method Multi-Domain Linear Non-Gaussian Acyclic Models (MD-LiNA) with an integrated two-phase algorithm.
result Locally consistent estimators of causal structure among shared latent factors.
DICCA maps multi-view data into a shared latent space with interpretable components.
problem Learning from multiple related but distinct data views.
method DICCA extends CCA to deep generative networks and uses sparsity-inducing priors for interpretability.
result DICCA effectively disentangles shared and view-specific variations in multi-view data.
ALCNN predicts bike demand patterns in new cities using multi-source geographic data.
problem Inferring fine-grained bike demands in new cities with limited data.
method Extract features from POI, road networks, and nighttime light; use coPCA for adaptation; apply DWT for daily patterns; use attention-based local CNN (ALCNN).
result ALCNN outperforms other methods in predicting bike demand patterns.
Method learns to map dynamics of different systems.
problem Mapping dynamics of different systems.
method Learned latent dynamical system for mapping.
result Learned correspondences enable imagined motions and bisimulation.
SCHA-VAE generates novel data from limited examples using hierarchical context aggregation.
problem Generating data from a novel distribution with limited examples.
method Hierarchical context aggregation with attention-based point to set-level aggregation.
result Hierarchical approach better captures intrinsic variability in small data.
Aggregates models from different datasets using shared latent structures.
problem Aggregating models from heterogeneous datasets with shared latent structures.
method Bayesian nonparametrics for identifying correspondences among local model parameterizations.
result Framework successfully aggregates various model types across different applications.
The paper evaluates variational auto-encoders using model criticism methods.
problem Evaluating the quality of variational auto-encoders (VAEs).
method Statistical model criticism, focusing on reproducing statistics of unknown data generating processes.
result The proposed framework offers possibilities for model selection beyond intrinsic metrics.
Owing to the recent advances in "Big Data" modeling and prediction tasks, variational Bayesian estimation has gained popularity due to their ability to provide exact solutions to approximate posteriors. One key technique for approximate inference is stochastic variational inference (SVI). SVI poses variational inferenc…
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Aut…
New results show contrastive learning can recover shared factors in multimodal data.
problem Understanding when contrastive learning can recover shared latent factors in multimodal data.
method New identifiability results for multimodal contrastive learning, distinguishing between multi-view and multimodal settings.
result Contrastive learning can block-identify shared latent factors in multimodal data, even with dependencies.
Temporal aggregation reveals latent default correlation from monthly data.
problem Understanding effective default correlation from monthly default data.
method Temporal coarse-graining of latent default-probability paths.
result Temporal coarse-graining improves identifiability and reduces over-allocation of long-horizon fluctuations.