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Found 13 papers, 4 papers with code
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty
Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations.
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
In particular, we ask "Given a large pool of prompts, can we automatically score the prompts and ensemble those that are most suitable for a particular downstream dataset, without needing access to labeled validation data?".
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data.
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles.
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e. g., tree ensembles, kernel methods, neural networks, etc).
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network.
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero.
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model.
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
(2) BNN's uncertainty quantification for variable importance is rigorous, in the sense that its 95% credible intervals for variable importance indeed covers the truth 95% of the time (i. e., the Bernstein-von Mises (BvM) phenomenon).
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty.
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
Furthermore, we show that CGP inherents the optimal theoretical properties of the Gaussian process, e. g. rates of posterior contraction, due to the fact that CGP is an Gaussian process with a more efficient model space.
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Ensemble learning is a mainstay in modern data science practice.
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
Utilizing the theory of reproducing kernels, we reduce this hypothesis to a simple one-sided score test for a scalar parameter, develop a testing procedure that is robust against the mis-specification of kernel functions, and also propose an ensemble-based estimator for the null model to guarantee test performance in small samples.
