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Found 20 papers, 8 papers with code
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation
We further provide algorithm-dependent generalization bounds for these two settings, where the generalization is characterized by the mutual information between the parameters and the data.
Generalization Properties of Decision Trees on Real-valued and Categorical Features
Furthermore, we show that the VC dimension of a binary tree structure with $L_T$ leaves on examples of $\ell$ real-valued features is in $O(L_T \log(L_T\ell))$.
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion
We significantly improve the generalization bounds for VC classes by using two main ideas.
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
In this work, instead of aiming at reducing the under-specification of model explanations, we fully embrace it and extract logical statements about feature attributions that are consistent across all models with good empirical performance (i. e. all models in the Rashomon Set).
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms.
Decision trees as partitioning machines to characterize their generalization properties
We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension.
MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING
Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.
Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.
Large scale modeling of antimicrobial resistance with interpretable classifiers
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide.
Efficient Learning of Ensembles with QuadBoost
We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set.
Domain-Adversarial Training of Neural Networks
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
Ranked #2 on
Domain Adaptation
on Synth Digits-to-SVHN
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers.
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
We propose an extensive analysis of the behavior of majority votes in binary classification.
Domain-Adversarial Neural Networks
We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification task, but uninformative as to the domain of the input.
On the String Kernel Pre-Image Problem with Applications in Drug Discovery
This work uses a recent result on combinatorial optimization of linear predictors based on string kernels to develop, for the pre-image, a low complexity upper bound valid for many string kernels.
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies.
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.
Sequential Model-Based Ensemble Optimization
One of the most tedious tasks in the application of machine learning is model selection, i. e. hyperparameter selection.
A Note on Improved Loss Bounds for Multiple Kernel Learning
In this paper, we correct an upper bound, presented in~\cite{hs-11}, on the generalisation error of classifiers learned through multiple kernel learning.
From PAC-Bayes Bounds to KL Regularization
We show that standard ell_p-regularized objective functions currently used, such as ridge regression and ell_p-regularized boosting, are obtained from a relaxation of the KL divergence between the quasi uniform posterior and the uniform prior.
