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Found 17 papers, 5 papers with code
Simple Transferability Estimation for Regression Tasks
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task.
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task.
When can we reconstruct the ancestral state? Beyond Brownian motion
Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology.
Posterior concentration and fast convergence rates for generalized Bayesian learning
We prove that under the multi-scale Bernstein's condition, the generalized posterior distribution concentrates around the set of optimal hypotheses and the generalized Bayes estimator can achieve fast learning rate.
When can we reconstruct the ancestral state? A unified theory
Notably, we show that for a sequence of nested trees with bounded heights, the necessary and sufficient conditions for the existence of a consistent ancestral state reconstruction method under discrete models, the Brownian motion model, and the threshold model are equivalent.
Searching for Minimal Optimal Neural Networks
Large neural network models have high predictive power but may suffer from overfitting if the training set is not large enough.
OASIS: An Active Framework for Set Inversion
In this work, we introduce a novel method for solving the set inversion problem by formulating it as a binary classification problem.
Convergence of maximum likelihood supertree reconstruction
Supertree methods are tree reconstruction techniques that combine several smaller gene trees (possibly on different sets of species) to build a larger species tree.
Consistent Feature Selection for Analytic Deep Neural Networks
One of the most important steps toward interpretability and explainability of neural network models is feature selection, which aims to identify the subset of relevant features.
Consistent feature selection for neural networks via Adaptive Group Lasso
In this work, we propose and establish a theoretical guarantee for the use of the adaptive group lasso for selecting important features of neural networks.
Bayesian Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Non-bifurcating phylogenetic tree inference via the adaptive LASSO
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system.
Bayesian Pool-based Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Probabilistic Path Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary.
Fast learning rates with heavy-tailed losses
We study fast learning rates when the losses are not necessarily bounded and may have a distribution with heavy tails.
Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution.
Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data
For the generalization of the algorithm, we prove a PAC-style bound on the training sample size for the expected $L_1$-loss to converge to the optimal loss when training data are V-geometrically ergodic Markov chains.
