Search
Learning to Schedule Heuristics in Branch and Bound
While much of MIP research focuses on designing effective heuristics, the question of how to manage multiple MIP heuristics in a solver has not received equal attention.
A Neural Corpus Indexer for Document Retrieval Y ujing Wang
Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target.
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
Causal inference aims to draw a causal relationship between some treatment and target responses.
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Empirically, the low-variance baseline of POMO makes RL training fast and stable, and it is more resistant to local minima compared to previous approaches.
A Supplementary Material
Thus, Proposition 1 yields a smallest PI-explanation.Proposition 2. PI-explanations of an XLC can be enumerated with log-linear delay. This means that all PIexplanations will be found. Thus, all leaves correspond to PI-explanations. Figure 5: Percentage of important "hits" of explanations produced by Anchor and SHAP .