ConferenceProceedings of Machine Learning Research · January 1, 2024
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mos ...
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Journal ArticleAdvances in neural information processing systems · December 2023
The Rashomon set is the set of models that perform approximately equally well on a given dataset, and the Rashomon ratio is the fraction of all models in a given hypothesis space that are in the Rashomon set. Rashomon ratios are often large for tabular dat ...
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ConferenceACM International Conference Proceeding Series · June 21, 2022
It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with integer coefficients, decision sets, rule lists, decision trees) is generally ...
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Journal Article35th AAAI Conference on Artificial Intelligence, AAAI 2021 · January 1, 2021
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-b ...
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ConferenceProceedings of Machine Learning Research · January 1, 2021
Transfer in reinforcement learning is based on the idea that it is possible to use what is learned in one task to improve the learning process in another task. For transfer between tasks which share transition dynamics but differ in reward function, succes ...
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Conference36th International Conference on Machine Learning, ICML 2019 · January 1, 2019
The impact of softmax on the value function itself in reinforcement learning (RL) is often viewed as problematic because it leads to sub-optimal value (or Q) functions and interferes with the contraction properties of the Bellman operator. Surprisingly, de ...
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ConferenceProceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 · January 1, 2012
Significant progress has been made recently in the following two lines of research in the intersection of AI and game theory: (1) the computation of optimal strategies to commit to (Stackelberg strategies), and (2) the computation of correlated equilibria ...
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ConferenceProceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 · August 11, 2011
The Approximate Linear Programming (ALP) approach to value function approximation for MDPs is a parametric value function approximation method, in that it represents the value function as a linear combination of features which are chosen a priori. Choosing ...
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ConferenceProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010 · July 15, 2010
Recently, algorithms for computing game-theoretic solutions have been deployed in real-world security applications, such as the placement of checkpoints and canine units at Los Angeles International Airport. These algorithms assume that the defender (secur ...
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ConferenceAdvances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 · January 1, 2010
Recent work in reinforcement learning has emphasized the power of L 1 regularization to perform feature selection and prevent overfitting. We propose formulating the L1 regularized linear fixed point problemas a linear complementarity problem (LCP). This f ...
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ConferenceNIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems · January 1, 2002
We present a new method for learning good strategies in zero-sum Markov games in which each side is composed of multiple agents collaborating against an opposing team of agents. Our method requires full observability and communication during learning, but ...
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ConferenceProceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000 · January 1, 2000
Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the ut ...
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ConferenceProceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000 · January 1, 2000
This paper addresses the problem of tracking and diagnosing complex systems with mixtures of discrete and continuous variables. This problem is a difficult one, particularly when the system dynamics are nondeterministic, not all aspects of the system are d ...
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ConferenceIJCAI International Joint Conference on Artificial Intelligence · January 1, 1995
The problem of making optimaJ decisions in uncertain conditions is central to Artificial Intelligence If the state of the world is known at all times, the world can be modeled as a Markov Decision Pro cess (MDP) MDPs have been studied extensively and many ...
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