2,086 research outputs found
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation
The generation of synthetic tabular data that preserves differential privacy
is a problem of growing importance. While traditional marginal-based methods
have achieved impressive results, recent work has shown that deep
learning-based approaches tend to lag behind. In this work, we present
Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a
transformer-based autoregressive model that maintains differential privacy and
achieves performance competitive with marginal-based methods on a wide variety
of datasets, capable of even outperforming state-of-the-art methods in certain
settings. We also provide a theoretical framework for understanding the
limitations of marginal-based approaches and where deep learning-based
approaches stand to contribute most. These results suggest that deep
learning-based techniques should be considered as a viable alternative to
marginal-based methods in the generation of differentially private synthetic
tabular data
Outperforming Game Theoretic Play with Opponent Modeling in Two Player Dominoes
Dominoes is a partially observable extensive form game with probability. The rules are simple; however, complexity and uncertainty of this game make it difficult to apply standard game theoretic methods to solve. This thesis applies strategy prediction opponent modeling to work with game theoretic search algorithms in the game of two player dominoes. This research also applies methods to compute the upper bound potential that predicting a strategy can provide towards specific strategy types. Furthermore, the actual values are computed according to the accuracy of a trained classifier. Empirical results show that there is a potential value gain over a Nash equilibrium player in score for fully and partially observable environments for specific strategy types. The actual value gained is positive for a fully observable environment for score and total wins and ties. Actual value gained over the Nash equilibrium player from the opponent model only exist for score, while the opponent modeler demonstrates a higher potential to win and/or tie in comparison to a pure game theoretic agent
Counterfactual Regret Minimization を用いたトレーディングカードゲームの戦略計算
学位の種別: 修士University of Tokyo(東京大学
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