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Found 6 papers, 1 papers with code
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
We find that current state-of-the-art empirical techniques offer sub-optimal performance on these practical, non-decomposable performance objectives.
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Using the proposed CSST framework, we obtain practical self-training methods (for both vision and NLP tasks) for optimizing different non-decomposable metrics using deep neural networks.
Causality-Aware Neighborhood Methods for Recommender Systems
In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations.
Approximation Theory Based Methods for RKHS Bandits
The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback.
Unbiased Learning for the Causal Effect of Recommendation
This paper proposes an unbiased learning framework for the causal effect of recommendation.
Submodular Bandit Problem Under Multiple Constraints
Thus, motivated by diversified retrieval considering budget constraints, we introduce a submodular bandit problem under the intersection of $l$ knapsacks and a $k$-system constraint.
