Search Results for author:
Found 21 papers, 6 papers with code
Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model
We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance and provide analysis of how the covariance between species changes when shifts in variance occur along the path.
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.
A Generalization Bound of Deep Neural Networks for Dependent Data
Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid).
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics
The most popular tools for modelling and predicting infectious disease epidemics are compartmental models.
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.
Evolutionary shift detection with ensemble variable selection
PhylogeneticEM is even more conservative with small signal sizes and falls between l1ou + pBIC and Ensemble method + BIC with large signal sizes.
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.
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data
In this paper, we propose an adaptive group Lasso deep neural network for high-dimensional function approximation where input data are generated from a dynamical system and the target function depends on few active variables or few linear combinations of variables.
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.
Recovery guarantees for polynomial approximation from dependent data with outliers
In this work, we study the problem of learning nonlinear functions from corrupted and dependent data.
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.
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.
