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47
The jackknife—a review.
 Biometrika
, 1974
"... The Light Beyond, By Raymond A. Moody, Jr. with Paul Perry. New York, NY: Bantam Books, 1988, 161 pp., $18.95 In his foreword to this book, Andrew Greeley, a prominent priest and sociologist, introduces his comments with the following statement: "Raymond Moody has achieved a rare feat in th ..."
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The Light Beyond, By Raymond A. Moody, Jr. with Paul Perry. New York, NY: Bantam Books, 1988, 161 pp., $18.95 In his foreword to this book, Andrew Greeley, a prominent priest and sociologist, introduces his comments with the following statement: "Raymond Moody has achieved a rare feat in the quest for human knowledge; he has created a paradigm." He then refers to Thomas Kuhn, who pointed out in The Structure of Scientific Revolutions that scientific revolutions occur when someone creates a new perspective, a new model, a new approach to reality. Although Greeley acknowledges that Moody did not discover the neardeath experience (NDE), he contends that because Moody put a name to it in his previous bestseller Life After Life (1975), he therefore deserves credit for the new para digm that has evolved. Greeley then refers to The Light Beyond as characterized by Moody's "openness, sensitivity and modesty." This he attributes to Moody's acknowledgement that the NDE does not repre sent proof of life after death; rather, it indicates only the existence and widespread prevalence of the NDE. I must question why Greeley does not comment more on the content of the book, and why Moody felt it was appropriate to be credited with creating a new paradigm. During the last fourteen years since Life
Knows What It Knows: A Framework For SelfAware Learning
"... We introduce a learning framework that combines elements of the wellknown PAC and mistakebound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is ..."
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We introduce a learning framework that combines elements of the wellknown PAC and mistakebound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcementlearning and activelearning problems. We catalog several KWIKlearnable classes and open problems. 1.
NearBayesian exploration in polynomial time (full version). Available at http://ai.stanford.edu/˜kolter
, 2009
"... We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to modelbased RL offers an elegant solution to this problem, by considering a distribution over possible models and acting to maximize expected reward; unfortunately, the Bayesian solution is intr ..."
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Cited by 70 (0 self)
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We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to modelbased RL offers an elegant solution to this problem, by considering a distribution over possible models and acting to maximize expected reward; unfortunately, the Bayesian solution is intractable for all but very restricted cases. In this paper we present a simple algorithm, and prove that with high probability it is able to perform ǫclose to the true (intractable) optimal Bayesian policy after some small (polynomial in quantities describing the system) number of time steps. The algorithm and analysis are motivated by the socalled PACMDP approach, and extend such results into the setting of Bayesian RL. In this setting, we show that we can achieve lower sample complexity bounds than existing algorithms, while using an exploration strategy that is much greedier than the (extremely cautious) exploration of PACMDP algorithms. 1.
Reinforcement Learning in Finite MDPs: PAC Analysis Reinforcement Learning in Finite MDPs: PAC Analysis
"... Editor: We study the problem of learning nearoptimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These “PACMDP ” algorithms include the wellknown E 3 and RMAX algorithms as well as the more recent Delayed Qlearning algorithm. We summarize the current ..."
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Cited by 50 (6 self)
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Editor: We study the problem of learning nearoptimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These “PACMDP ” algorithms include the wellknown E 3 and RMAX algorithms as well as the more recent Delayed Qlearning algorithm. We summarize the current stateoftheart by presenting bounds for the problem in a unified theoretical framework. We also present a more refined analysis that yields insight into the differences between the modelfree Delayed Qlearning and the modelbased RMAX. Finally, we conclude with open problems.
Robust Bounds for Classification via Selective Sampling
"... We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reason it can be efficiently run in any RKHS. Unlike previous marginbased semisupervised algorithms, our sampling condition ..."
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We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reason it can be efficiently run in any RKHS. Unlike previous marginbased semisupervised algorithms, our sampling condition hinges on a simultaneous upper bound on bias and variance of the RLS estimate under a simple linear label noise model. This fact allows us to prove performance bounds that hold for an arbitrary sequence of instances. In particular, we show that our sampling strategy approximates the margin of the Bayes optimal classifier to any desired accuracy ε by asking Õ ( d/ε2) queries (in the RKHS case d is replaced by a suitable spectral quantity). While these are the standard rates in the fully supervised i.i.d. case, the best previously known result in our harder setting was Õ ( d3 /ε4). Preliminary experiments show that some of our algorithms also exhibit a good practical performance. 1.
A unifying framework for computational reinforcement learning theory
, 2009
"... Computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervisedlearning algorithms such as their sample complexity. While existing models such as PAC (Probably Approximately Correct) have played an influential role in understand ..."
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Cited by 23 (7 self)
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Computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervisedlearning algorithms such as their sample complexity. While existing models such as PAC (Probably Approximately Correct) have played an influential role in understanding the nature of supervised learning, they have not been as successful in reinforcement learning (RL). Here, the fundamental barrier is the need for active exploration in sequential decision problems. An RL agent tries to maximize longterm utility by exploiting its knowledge about the problem, but this knowledge has to be acquired by the agent itself through exploring the problem that may reduce shortterm utility. The need for active exploration is common in many problems in daily life, engineering, and sciences. For example, a Backgammon program strives to take good moves to maximize the probability of winning a game, but sometimes it may try novel and possibly harmful moves to discover how the opponent reacts in the hope of discovering a better gameplaying strategy. It has been known since the early days of RL that a good tradeoff between exploration and exploitation is critical for the agent to learn fast (i.e., to reach nearoptimal strategies
Robust selective sampling from single and multiple teachers
, 2010
"... We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of our algorithm and on the number of labels it queries when faced with an adaptive adversarial strategy of generating the ins ..."
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Cited by 20 (1 self)
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We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of our algorithm and on the number of labels it queries when faced with an adaptive adversarial strategy of generating the instances. Our bounds both generalize and strictly improve over previous bounds in similar settings. Using a simple onlinetobatch conversion technique, our selective sampling algorithm can be converted into a statistical (poolbased) active learning algorithm. We extend our algorithm and analysis to the multipleteacher setting, where the algorithm can choose which subset of teachers to query for each label.
Multiclass classification with bandit feedback using adaptive regularization
 In ICML
, 2011
"... We present a new multiclass algorithm in the bandit framework, where after making a prediction, the learning algorithm receives only partial feedback, i.e., a single bit of rightorwrong, rather then the true label. Our algorithm is based on the 2ndorder Perceptron, and uses upperconfidence bounds ..."
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Cited by 20 (5 self)
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We present a new multiclass algorithm in the bandit framework, where after making a prediction, the learning algorithm receives only partial feedback, i.e., a single bit of rightorwrong, rather then the true label. Our algorithm is based on the 2ndorder Perceptron, and uses upperconfidence bounds to trade off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where instances are chosen adversarially, while the labels are chosen according to a linear probabilistic model, which is also chosen adversarially. We show a regret of O ( √ T logT), which improvesoverthe current best bounds of O(T 2/3) in the fully adversarial setting. We evaluate our algorithm on nine realworld text classification problems, obtaining stateoftheart results, even comparedwith nonbandit online algorithms, especially when label noise is introduced. 1.
Multiresolution Exploration in Continuous Spaces
"... The essence of exploration is acting to try to decrease uncertainty. We propose a new methodology for representing uncertainty in continuousstate control problems. Our approach, multiresolution exploration (MRE), uses a hierarchical mapping to identify regions of the state space that would benefit ..."
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Cited by 18 (3 self)
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The essence of exploration is acting to try to decrease uncertainty. We propose a new methodology for representing uncertainty in continuousstate control problems. Our approach, multiresolution exploration (MRE), uses a hierarchical mapping to identify regions of the state space that would benefit from additional samples. We demonstrate MRE’s broad utility by using it to speed up learning in a prototypical modelbased and valuebased reinforcementlearning method. Empirical results show that MRE improves upon stateoftheart exploration approaches. 1