Cost-Effective Incentive Allocation via Structured Counterfactual Inference
Authors: Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael Jordan, Yuan Qi, Le Song4997-5004
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets. 6 Experiments In our experiments, we consider only the case of a discrete action space. We compare our method CCPOv SIRE to a simple modification of Bandit Net... |
| Researcher Affiliation | Collaboration | 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley {romain lopez, jordan}@cs.berkeley.edu 2AI Department, Ant Financial Service Group {junwu.xjw, yuan.qi, le.song}@antfin.com 3Department of Computer Science, Shanghai Jiao Tong University lcc1992@sjtu.edu.cn, xyansjtu@163.com 4College of Computing, Georgia Institute of Technology |
| Pseudocode | No | The paper describes its algorithm components and steps but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link to a source-code repository for the methodology described. |
| Open Datasets | Yes | We construct a concrete example from the Image Net dataset (Deng et al. 2009) |
| Dataset Splits | Yes | The dataset has 4608 samples in total, randomly split into training, validation and testing with ratio 0.6: 0.2: 0.2. |
| Hardware Specification | Yes | We ran our experiments on a machine with a Intel Xeon E5-2697 CPU and a NVIDIA Ge Force GTX TITAN X GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | Yes | To train CCPOv SIRE, we used stochastic gradient descent as a first-order stochastic optimizer with a learning rate of 0.01 and a three-layer neural network with 512 neurons for each hidden layer. |