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Found 20 papers, 9 papers with code
Fine-Grained Distribution-Dependent Learning Curves
We solve this problem in a principled manner, by introducing a combinatorial dimension called VCL that characterizes the best $d'$ for which $d'/n$ is a strong minimax lower bound.
A Generalized Lottery Ticket Hypothesis
We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters.
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vision.
Ranked #17 on
Image Classification
on OmniBenchmark
What Do Neural Networks Learn When Trained With Random Labels?
We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.
Predicting Neural Network Accuracy from Weights
Furthermore, the predictors are able to rank networks trained on different, unobserved datasets and with different architectures.
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled and unlabeled data.
Practical and Consistent Estimation of f-Divergences
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
GeNet: Deep Representations for Metagenomics
We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training.
Competitive Training of Mixtures of Independent Deep Generative Models
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure.
On the Latent Space of Wasserstein Auto-Encoders
We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs).
Wasserstein Auto-Encoders
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution.
Differentially Private Database Release via Kernel Mean Embeddings
First, releasing (an estimate of) the kernel mean embedding of the data generating random variable instead of the database itself still allows third-parties to construct consistent estimators of a wide class of population statistics.
Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified.
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.
AdaGAN: Boosting Generative Models
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images.
Consistent Kernel Mean Estimation for Functions of Random Variables
We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings.
Minimax Lower Bounds for Realizable Transductive Classification
Transductive learning considers a training set of $m$ labeled samples and a test set of $u$ unlabeled samples, with the goal of best labeling that particular test set.
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning
This paper introduces a new complexity measure for transductive learning called Permutational Rademacher Complexity (PRC) and studies its properties.
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probability distributions.
Localized Complexities for Transductive Learning
We show two novel concentration inequalities for suprema of empirical processes when sampling without replacement, which both take the variance of the functions into account.
