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- Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
In this work, we propose a novel estimator for off-policy learning and evaluation from the LBF dataset that outperforms existing estimators when dealing with estimated propensity scores and heavy-tailed or noisy weighted rewards
- Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
TL;DR: We propose a novel estimator for off-policy learning and evaluation under heavy-tailed assumption Off-policy learning and evaluation scenarios leverage logged bandit feedback datasets, which contain context, action, propensity score, and feedback for each data point
- [2506. 06873] Log-Sum-Exponential Estimator for Off-Policy Evaluation . . .
We address these issues by introducing a novel estimator based on the log-sum-exponential (LSE) operator, which outperforms traditional inverse propensity score estimators Our LSE estimator demonstrates variance reduction and robustness under heavy-tailed conditions
- Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
We address these issues by introducing a novel estimator based on the log-sum-exponential (LSE) operator, which outperforms traditional inverse propensity score estimators Our LSE
- Off-policy Evaluation and Learning - University of Washington
or: There are a number of estimators for off-policy evaluation, each with its corresponding MSE upper bound It is natural to ask how far we can low r the error, and what are fundamental lower bounds for the hardness of the probl
- #estimator #off #learning #spotlight #icml #novelty #lse #ope # . . .
We address these issues by introducing a novel estimator based on the #log -sum-exponential (LSE) operator, which outperforms traditional inverse propensity score estimators Our LSE
- Long-term Off-Policy Evaluation and Learning
We study the novel problem of future off-policy evaluation (F-OPE) and learning (F-OPL) for estimating and optimizing the future value of policies in non-stationary environments, where distributions vary over time
- GitHub Pages - Vincent Tan
May 2025: Three papers accepted to ICML 2025: "LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos", "BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms" and "Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning" (spotlight)
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