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Estimators computed from adaptively collected data do not behave like their non-adaptive brethren.
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Tze Leung Lai and Ching Zong Wei · 1982
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Tse—Leung Lai and David Siegmund · 1983
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Tze Leung Lai and Herbert Robbins · 1985
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Ngai H Chan and Ching-Zong Wei · 1987
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Testing for a unit root in time series regression
Peter CB Phillips and Pierre Perron · 1988
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Asymptotic properties of nonlinear least squares estimates in stochastic regression models
Tze Leung Lai · 1994
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Isoperimetry and Gaussian analysis , volume 1648
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Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs
Kani Chen, Inchi Hu, Zhiliang Ying, et al · 1999
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The role of the propensity score in estimating dose-response functions
Guido W Imbens · 2000
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Jared K Lunceford and Marie Davidian · 2004
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The concentration of measure phenomenon
Michel Ledoux · 2005
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Time series analysis and its applications: with R examples
Robert H Shumway and David S Stoffer · 2006
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Minimax bounds for active learning
Rui M Castro and Robert D Nowak · 2008
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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Applied econometric time series
Walter Enders · 2008
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Quasi-likelihood and its application: a general approach to optimal parameter estimation
Christopher C Heyde · 2008
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck, Nicolo Cesa-Bianchi, et al · 2012
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Linear bandits in high dimension and recommendation systems
Yash Deshpande and Andrea Montanari · 2012
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User-friendly tail bounds for sums of random matrices
Joel A Tropp · 2012
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Martingale limit theory and its application
Peter Hall and Christopher C Heyde · 2014
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lil’ucb: An optimal exploration algorithm for multi-armed bandits
Kevin Jamieson, Matthew Malloy, Robert Nowak, and Sébastien Bubeck · 2014
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On asymptotically optimal confidence regions and tests for high-dimensional models
Sara Van de Geer, Peter Bühlmann, Ya’acov Ritov, Ruben Dezeure, et al · 2014
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Jean-Yves Audibert and Sébastien Bubeck · 2009
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Implicit online learning
Brian Kulis and Peter L Bartlett · 2010
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
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Linearly parameterized bandits
Paat Rusmevichientong and John N Tsitsiklis · 2010
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Online least squares estimation with self-normalized processes: An application to bandit problems
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Incremental proximal methods for large scale convex optimization
Dimitri P Bertsekas · 2011
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Confidence intervals for low dimensional parameters in high dimensional linear models
Cun-Hui Zhang and Stephanie S Zhang · 2014
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Batch learning from logged bandit feedback through counterfactual risk minimization
Adith Swaminathan and Thorsten Joachims · 2015
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Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges
Sofia Villar, Jack Bowden, and James Wason · 2015
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Simple bayesian algorithms for best arm identification
Daniel Russo · 2016
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Susan Athey and Stefan Wager · 2017
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Estimation considerations in contextual bandits
Maria Dimakopoulou, Susan Athey, and Guido Imbens · 2017
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Balanced policy evaluation and learning
Nathan Kallus · 2017
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Why adaptively collected data have negative bias and how to correct for it
Xinkun Nie, Tian Xiaoying, Jonathan Taylor, and James Zou · 2017
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Accurate inference for adaptive linear models, 2018
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