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Found 6 papers, 0 papers with code
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization
Solving large-scale multistage stochastic programming (MSP) problems poses a significant challenge as commonly used stagewise decomposition algorithms, including stochastic dual dynamic programming (SDDP), face growing time complexity as the subproblem size and problem count increase.
ICLN: Input Convex Loss Network for Decision Focused Learning
In this paper, we propose Input Convex Loss Network (ICLN), a novel global surrogate loss which can be implemented in a general DFL paradigm.
Encoding Temporal Statistical-space Priors via Augmented Representation
In response, we leverage a simple representation augmentation technique to overcome these challenges.
Provably Scalable Black-Box Variational Inference with Structured Variational Families
In fact, recent computational complexity results for BBVI have established that full-rank variational families scale poorly with the dimensionality of the problem compared to e. g. mean field families.
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series
Our findings reveal that curriculum learning should be considered a novel direction in improving control-task performance over complex time-series.
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation
In this regard, we propose a mean-variance efficient collaborative filtering (MVECF) model for stock recommendations that consider both aspects.
