Search Results for author:
Found 11 papers, 3 papers with code
Credal Learning Theory
Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learnt from a (single) training set, assumed to issue from an unknown probability distribution.
Second-Order Uncertainty Quantification: A Distance-Based Approach
In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order probability distributions, i. e., predictions in the form of distributions on probability distributions.
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning
Upon a new task, IBCL (1) updates a knowledge base in the form of a convex hull of model parameter distributions and (2) obtains particular models to address task trade-off preferences with zero-shot.
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems.
A Novel Bayes' Theorem for Upper Probabilities
In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise.
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?
Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence.
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning
Upon a new task, IBCL (1) updates a knowledge base in the form of a convex hull of model parameter distributions and (2) obtains particular models to address task trade-off preferences with zero-shot.
Using Semantic Information for Defining and Detecting OOD Inputs
This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information (e. g. training class labels).
Take Me Home: Reversing Distribution Shifts using Reinforcement Learning
To aid in our estimates of Wasserstein distance, we employ dimensionality reduction through orthonormal projection.
Credal Bayesian Deep Learning
We show that CBDL is better at quantifying and disentangling different types of uncertainties than single BNNs, ensemble of BNNs, and Bayesian Model Averaging.
Concentration inequalities and optimal number of layers for stochastic deep neural networks
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN.
