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Found 10 papers, 3 papers with code
On training locally adaptive CP
We address the problem of making Conformal Prediction (CP) intervals locally adaptive.
Differentiable Architecture Pruning for Transfer Learning
We propose a new gradient-based approach for extracting sub-architectures from a given large model.
Adapting by Pruning: A Case Study on BERT
Adapting pre-trained neural models to downstream tasks has become the standard practice for obtaining high-quality models.
Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
To find the optimal weight-agnostic network, we use a novel and computationally efficient method that translates the hard architecture-search problem into a feasible optimization problem. More specifically, we look at the optimal task-specific architectures as the optimal configuration of binary networks with {0, 1}-valued weights, which can be found through an approximate gradient descent strategy.
Training conformal predictors
Efficiency criteria for conformal prediction, such as \emph{observed fuzziness} (i. e., the sum of p-values associated with false labels), are commonly used to \emph{evaluate} the performance of given conformal predictors.
Multiple Metric Learning for Structured Data
Main challenge is that metric constraints (especially positive-definiteness and sub-additivity), are not automatically respected if, for example, the coefficients of the linear combination are allowed to be negative.
Counterfactual Distribution Regression for Structured Inference
The inference problem is how information concerning perturbations, with particular covariates such as location and time, can be generalized to predict the effect of novel perturbations.
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs.
A posteriori error bounds for joint matrix decomposition problems
Joint matrix triangularization is often used for estimating the joint eigenstructure of a set M of matrices, with applications in signal processing and machine learning.
Approximate Joint Matrix Triangularization
The a priori bounds are theoretical inequalities that involve functions of the ground-truth matrices and noise matrices, whereas the a posteriori bounds are given in terms of observable quantities that can be computed from the input matrices.
