Search Results for author:
Found 14 papers, 6 papers with code
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable.
Autoregressive flow-based causal discovery and inference
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Here, we propose a general framework for nonlinear ICA, which, as a special case, can make use of temporal structure.
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning.
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Nevertheless, we soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation
Then, based on the observation that conventional classification learning with neural networks is implicitly assuming an exponential family as a generative model, we introduce a method for clustering unlabeled data by estimating a finite mixture of distributions in an exponential family.
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables.
ParceLiNGAM: A causal ordering method robust against latent confounders
In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders.
Bridging Information Criteria and Parameter Shrinkage for Model Selection
Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied.
Neural Empirical Bayes
Kernel density is viewed symbolically as $X\rightharpoonup Y$ where the random variable $X$ is smoothed to $Y= X+N(0,\sigma^2 I_d)$, and empirical Bayes is the machinery to denoise in a least-squares sense, which we express as $X \leftharpoondown Y$.
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
We consider the problem of inferring causal relationships between two or more passively observed variables.
Information criteria for non-normalized models
However, model selection methods for general non-normalized models have not been proposed so far.
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called "disentanglement".
