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Found 7 papers, 1 papers with code
Lower-Bounded Proper Losses for Weakly Supervised Classification
To this end, we derive a representation theorem for proper losses in supervised learning, which dualizes the Savage representation.
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
In this paper, we propose a novel parameter estimator for probabilistic models on discrete space.
Unified estimation framework for unnormalized models with statistical efficiency
The parameter estimation of unnormalized models is a challenging problem.
Zero-shot Domain Adaptation Based on Attribute Information
In this paper, we propose a novel domain adaptation method that can be applied without target data.
Robust contrastive learning and nonlinear ICA in the presence of outliers
We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.
Regret Minimization for Causal Inference on Large Treatment Space
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems.
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation
Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation.
