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Found 14 papers, 5 papers with code
Conformal prediction under ambiguous ground truth
However, in many real-world scenarios, the labels $Y_1,..., Y_n$ are obtained by aggregating expert opinions using a voting procedure, resulting in a one-hot distribution $\mathbb{P}_{vote}^{Y|X}$.
Evaluating AI systems under uncertain ground truth: a case study in dermatology
In contrast, we propose a framework where aggregation is done using a statistical model.
Transformers Meet Directed Graphs
Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs.
Ranked #1 on
Graph Property Prediction
on ogbg-code2
Learning Optimal Conformal Classifiers
However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets.
Towards Fair Personalization by Avoiding Feedback Loops
Self-reinforcing feedback loops are both cause and effect of over and/or under-presentation of some content in interactive recommender systems.
Intermittent Demand Forecasting with Renewal Processes
Intermittency is a common and challenging problem in demand forecasting.
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs
We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.
A Bayesian Choice Model for Eliminating Feedback Loops
Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices.
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns
We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation.
Audio Source Separation Using Variational Autoencoders and Weak Class Supervision
In this paper, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources.
Differentially Private Dropout
Large data collections required for the training of neural networks often contain sensitive information such as the medical histories of patients, and the privacy of the training data must be preserved.
Differentially Private Variational Dropout
In this paper, we modify the recently proposed variational dropout technique which provided an elegant Bayesian interpretation to dropout, and show that the intrinsic noise in the variational dropout can be exploited to obtain a degree of differential privacy.
Clustering Words by Projection Entropy
We apply entropy agglomeration (EA), a recently introduced algorithm, to cluster the words of a literary text.
Summary Statistics for Partitionings and Feature Allocations
Experiments on various infinite mixture posteriors as well as a feature allocation dataset demonstrate that the proposed statistics are useful in practice.
