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Found 14 papers, 12 papers with code
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
In this work we introduce a general bias-variance decomposition for proper scores, giving rise to the Bregman Information as the variance term.
Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity
Many real-world systems are described not only by data from a single source but via multiple data views.
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
With model trustworthiness being crucial for sensitive real-world applications, practitioners are putting more and more focus on improving the uncertainty calibration of deep neural networks.
Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables
Additionally, in settings where partial knowledge on the latent structure of the data is readily available, a statistically sound integration of prior information into current methods is challenging.
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems
Many common approaches to solve the collaborative filtering task are based on learning representations of users and items, including simple matrix factorization, Gaussian process latent variable models, and neural-network based embeddings.
Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration
We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS).
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization
We address the task of domain generalization, where the goal is to train a predictive model such that it is able to generalize to a new, previously unseen domain.
Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration
That is, it is crucial for predictive models to be uncertainty-aware and yield well-calibrated (and thus trustworthy) predictions for both in-domain samples as well as under domain shift.
Post-hoc Uncertainty Calibration for Domain Drift Scenarios
First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift.
TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells.
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems.
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
Document Informed Neural Autoregressive Topic Models
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
