Search Results for author:
Found 9 papers, 3 papers with code
Diverse Imitation Learning via Self-OrganizingGenerative Models
To address this problem, we introduce a novel generative model for behavior cloning, in a mode-separating manner.
Diverse Imitation Learning via Self-Organizing Generative Models
Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function.
De-Biasing Generative Models using Counterfactual Methods
Our proposed method combines a causal latent space VAE model with specific modification to emphasize causal fidelity, enabling finer control over the causal layer and the ability to learn a robust intervention framework.
Adaptive Second Order Coresets for Data-efficient Machine Learning
We propose AdaCore, a method that leverages the geometry of the data to extract subsets of the training examples for efficient machine learning.
Causal Structural Hypothesis Testing and Data Generation Models
We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes.
Improving Levenberg-Marquardt Algorithm for Neural Networks
We explore the usage of the Levenberg-Marquardt (LM) algorithm for regression (non-linear least squares) and classification (generalized Gauss-Newton methods) tasks in neural networks.
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality.
Towards Composable Distributions of Latent Space Augmentations
We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations.
Curvature-Informed SGD via General Purpose Lie-Group Preconditioners
We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm.
