@book{antognini2015adaptive,
  title={Adaptive designs for sequential treatment allocation},
  author={Antognini, Alessandro Baldi and Giovagnoli, Alessandra},
  volume={73},
  year={2015},
  publisher={CRC Press}
}

@article{sciamanna2021working,
  title={Working to Increase Stability through Exercise (WISE): screening, recruitment, and baseline characteristics},
  author={Sciamanna, Christopher N and Ballentine, Noel H and Bopp, Melissa and Chinchilli, Vernon M and Ciccolo, Joseph T and Delauter, Gabrielle and Fisher, Abigail and Fox, Edward J and Jan De Beur, Suzanne M and Kearcher, Kalen and others},
  journal={Trials},
  volume={22},
  pages={1--12},
  year={2021},
  publisher={Springer}
}

@book{Gelman2013BayesianAnalysis,
    title = {{Bayesian data analysis}},
    year = {2013},
    author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
    publisher = {CRC press},
    isbn = {1439840954}
}
@article{SilverSneakers_2023,
        author = {Liza Rovniak},
	title = {{Activating SilverSneakers Benefits for Seniors with Osteoarthritis: Proactive versus Usual Care}},
	journal = {PCORI - Broad Pragmatic Studies},
}

@misc{SilverSneakers,
	title = {{SilverSneakers Program}},
	url = {https://tools.silversneakers.com/},
	urldate = {2024-02-09},
}

@article{milkman2021megastudies,
  title={Megastudies improve the impact of applied behavioural science},
  author={Milkman, Katherine L and Gromet, Dena and Ho, Hung and Kay, Joseph S and Lee, Timothy W and Pandiloski, Pepi and Park, Yeji and Rai, Aneesh and Bazerman, Max and Beshears, John and others},
  journal={Nature},
  volume={600},
  number={7889},
  pages={478--483},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",
pages =        "891--921",
year =         "1905"}

@book{latexcompanion,
author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
title     = "The \LaTeX\ Companion",
year      = "1993",
publisher = "Addison-Wesley"}

@book{Friedman2015,
   author = {Lawrence M. Friedman and Curt D. Furberg and David L. DeMets and David M. Reboussin and Christopher B. Granger},
   city = {Cham},
   doi = {10.1007/978-3-319-18539-2},
   isbn = {978-3-319-18538-5},
   publisher = {Springer International Publishing},
   title = {Fundamentals of Clinical Trials},
   year = {2015},
}

@misc{ehealth,
   author = {eClinicalHealth},
   journal = {CLINPAL – Engaging Patients in Clinical Research},
   title = {Recruitment Infographic},
   url = {https://clinpal.com/recruitment-infographic/},
}

@article{Patsopoulos2011,
   author = {Nikolaos A Patsopoulos},
   doi = {10.31887/DCNS.2011.13.2/npatsopoulos},
   issn = {1294-8322},
   issue = {2},
   journal = {Dialogues in clinical neuroscience},
   pages = {217-24},
   pmid = {21842619},
   title = {A pragmatic view on pragmatic trials.},
   volume = {13},
   year = {2011},
}

@article{pcori,
   author = {Christopher N. Sciamanna},
   title = {Comparing Two Ways to Reduce Future Fall Injuries among Older Adults Who Have Fallen -- The WISE Study},
   journal = {PCORI},
   doi = {10.25302/10.2023.PCS.140618325},
}

@article{Sciamanna2018,
   author = {Christopher Sciamanna and Noel H. Ballentine and Melissa Bopp and Jennifer S. Brach and Vernon M. Chinchilli and Joseph T. Ciccolo and Molly B. Conroy and Abigail Fisher and Edward J. Fox and Susan L. Greenspan and Suzanne, M. Jan De Beur and Kalen Kearcher and Jennifer L. Kraschnewski and Kathleen M. McTigue and Edward McAuley and Natalia E. Morone and Anuradha Paranjape and Sol Rodriguez-Colon and Andrew Rosenzweig and Joshua M. Smyth and Kerry J. Stewart and Heather L. Stuckey},
   doi = {10.1016/j.cct.2018.09.006},
   issn = {15517144},
   journal = {Contemporary Clinical Trials},
   month = {11},
   pages = {1-10},
   title = {Working to Increase Stability through Exercise (WISE): Study protocol for a pragmatic randomized controlled trial of a coached exercise program to reduce serious fall-related injuries},
   volume = {74},
   year = {2018},
}

@misc{mosio,
   author = {MOSIO},
   title = {Pragmatic Clinical Trials Common Challenges & Best Practices},
   url = {https://www.mosio.com/},
}

@article{Weissler2021,
   author = {E. Hope Weissler and Tristan Naumann and Tomas Andersson and Rajesh Ranganath and Olivier Elemento and Yuan Luo and Daniel F. Freitag and James Benoit and Michael C. Hughes and Faisal Khan and Paul Slater and Khader Shameer and Matthew Roe and Emmette Hutchison and Scott H. Kollins and Uli Broedl and Zhaoling Meng and Jennifer L. Wong and Lesley Curtis and Erich Huang and Marzyeh Ghassemi},
   doi = {10.1186/s13063-021-05489-x},
   issn = {1745-6215},
   issue = {1},
   journal = {Trials},
   month = {12},
   pages = {537},
   title = {The role of machine learning in clinical research: transforming the future of evidence generation},
   volume = {22},
   year = {2021},
}
@article{Miller2023,
   author = {Matthew I. Miller and Ludy C. Shih and Vijaya B. Kolachalama},
   doi = {10.1007/s13311-023-01384-2},
   issn = {18787479},
   issue = {4},
   journal = {Neurotherapeutics},
   month = {7},
   pages = {1066-1080},
   title = {Machine Learning in Clinical Trials: A Primer with Applications to Neurology},
   volume = {20},
   year = {2023},
}

@article{Buchholz2020,
   author = {Susan W. Buchholz and JoEllen Wilbur and Shannon Halloway and Michael Schoeny and Tricia Johnson and Sachin Vispute and Spyros Kitsiou},
   doi = {10.1016/j.cct.2019.105921},
   issn = {15517144},
   journal = {Contemporary Clinical Trials},
   month = {2},
   pages = {105921},
   title = {Study protocol for a sequential multiple assignment randomized trial (SMART) to improve physical activity in employed women},
   volume = {89},
   year = {2020},
}
@article{Almirall2014,
   abstract = {The management of many health disorders often entails a sequential, individualized approach whereby treatment is adapted and readapted over time in response to the specific needs and evolving status of the individual. Adaptive interventions provide one way to operationalize the strategies (e.g., continue, augment, switch, step-down) leading to individualized sequences of treatment. Often, a wide variety of critical questions must be answered when developing a high-quality adaptive intervention. Yet, there is often insufficient empirical evidence or theoretical basis to address these questions. The Sequential Multiple Assignment Randomized Trial (SMART)-a type of research design-was developed explicitly for the purpose of building optimal adaptive interventions by providing answers to such questions. Despite increasing popularity, SMARTs remain relatively new to intervention scientists. This manuscript provides an introduction to adaptive interventions and SMARTs. We discuss SMART design considerations, including common primary and secondary aims. For illustration, we discuss the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight.},
   author = {Daniel Almirall and Inbal Nahum-Shani and Nancy E Sherwood and Susan A Murphy},
   doi = {10.1007/s13142-014-0265-0},
   issn = {1869-6716},
   issue = {3},
   journal = {Translational behavioral medicine},
   month = {9},
   pages = {260-74},
   pmid = {25264466},
   title = {Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research.},
   volume = {4},
   year = {2014},
}
@inproceedings{MNLBANDIT,
 author = {Oh, Min-hwan and Iyengar, Garud},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {NeurIPS},
 title = {Thompson Sampling for Multinomial Logit Contextual Bandits},
 volume = {32},
 year = {2019}
}

@inproceedings{xmeans,
author = {Pelleg, Dan and Moore, Andrew W.},
title = {X-means: Extending K-means with Efficient Estimation of the Number of Clusters},
year = {2000},
isbn = {1558607072},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
booktitle = {Proceedings of the Seventeenth International Conference on Machine Learning},
pages = {727–734},
numpages = {8},
series = {ICML '00}
}

@article{greet,
author = {Christine Zahren and Sonia Harvey and Leanne Weekes and Charlotte Bradshaw and Radhika Butala and John Andrews and Sally O’Callaghan},
title ={Clinical trials site recruitment optimisation: Guidance from Clinical Trials: Impact and Quality},

journal = {Clinical Trials},
volume = {18},
number = {5},
pages = {594-605},
year = {2021},
doi = {10.1177/17407745211015924},
    note ={PMID: 34041937},
eprint = { 
    
        https://doi.org/10.1177/17407745211015924
    
    

}
}
@article{twostageML,
title = {Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study},
journal = {Osteoarthritis and Cartilage Open},
volume = {5},
number = {4},
pages = {100406},
year = {2023},
issn = {2665-9131},
doi = {https://doi.org/10.1016/j.ocarto.2023.100406},
author = {Paweł Widera and Paco M.J. Welsing and Samuel O. Danso and Sjaak Peelen and Margreet Kloppenburg and Marieke Loef and Anne C. Marijnissen and Eefje M. {van Helvoort} and Francisco J. Blanco and Joana Magalhães and Francis Berenbaum and Ida K. Haugen and Anne-Christine Bay-Jensen and Ali Mobasheri and Christoph Ladel and John Loughlin and Floris P.J.G. Lafeber and Agnès Lalande and Jonathan Larkin and Harrie Weinans and Jaume Bacardit},
keywords = {Osteoarthritis, Disease progression prediction, Machine learning, Patient selection for clinical trials, Inclusion},
abstract = {Objectives
To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.
Design
We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression.
Results
From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57).
Conclusions
The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.}
}
@article{chandraumakantham2024optimizing,
  title={Optimizing Patient Recruitment for Clinical Trials: A Hybrid Classification Model and Game-Theoretic Approach for Strategic Interaction},
  author={ChandraUmakantham, OmKumar and Singh, Isshaan and Marappan, Suguna},
  journal={IEEE Access},
  year={2024},
  publisher={IEEE}
}

@inbook{Dietterich2000,
   author = {Thomas G. Dietterich},
   doi = {10.1007/3-540-45014-9_1},
   pages = {1-15},
   title = {Ensemble Methods in Machine Learning},
   year = {2000},
}
@inproceedings{Guzman2015,
   author = {Emitza Guzman and Muhammad El-Haliby and Bernd Bruegge},
   doi = {10.1109/ASE.2015.88},
   isbn = {978-1-5090-0025-8},
   journal = {2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
   month = {11},
   pages = {771-776},
   publisher = {IEEE},
   title = {Ensemble Methods for App Review Classification: An Approach for Software Evolution (N)},
   year = {2015},
}
@article{silva2014,
   author = {Nádia F.F. da Silva and Eduardo R. Hruschka and Estevam R. Hruschka},
   doi = {10.1016/j.dss.2014.07.003},
   issn = {01679236},
   journal = {Decision Support Systems},
   month = {10},
   pages = {170-179},
   title = {Tweet sentiment analysis with classifier ensembles},
   volume = {66},
   year = {2014},
}
@article{Hoerl2000,
   author = {Arthur E. Hoerl and Robert W. Kennard},
   doi = {10.2307/1271436},
   issn = {00401706},
   issue = {1},
   journal = {Technometrics},
   month = {2},
   pages = {80},
   title = {Ridge Regression: Biased Estimation for Nonorthogonal Problems},
   volume = {42},
   year = {2000},
}
@article{Friedman2001,
   author = {Jerome H. Friedman},
   doi = {10.1214/aos/1013203451},
   issn = {0090-5364},
   issue = {5},
   journal = {The Annals of Statistics},
   month = {10},
   title = {Greedy function approximation: A gradient boosting machine.},
   volume = {29},
   year = {2001},
}
@article{Tibshirani1996,
   abstract = {[We propose a new method for estimation in linear models. The `lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.]},
   author = {Robert Tibshirani},
   issn = {00359246},
   issue = {1},
   journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
   pages = {267-288},
   publisher = {[Royal Statistical Society, Wiley]},
   title = {Regression Shrinkage and Selection via the Lasso},
   volume = {58},
   year = {1996},
}
@inproceedings{Ho1995,
   author = {Tin Kam Ho},
   doi = {10.1109/ICDAR.1995.598994},
   isbn = {0-8186-7128-9},
   journal = {Proceedings of 3rd International Conference on Document Analysis and Recognition},
   pages = {278-282},
   publisher = {IEEE Comput. Soc. Press},
   title = {Random decision forests},
   year = {1995},
}
@article{Grossberg1988,
   abstract = {An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentieth-century scientific movements. The nonlinear, nonstationary, and nonlocal nature of behavioral and brain data are emphasized. Three sources of contemporary neural network research—the binary, linear, and continuous-nonlinear models—are noted. The remainder of the article describes results about continuous-nonlinear models: Many models of content-addressable memory are shown to be special cases of the Cohen-Grossberg model and global Liapunov function, including the additive, brain-state-in-a-box, McCulloch-Pitts, Boltzmann machine, Hartline-Ratliff-Miller, shunting, masking field, bidirectional associative memory, Volterra-Lotka, Gilpin-Ayala, and Eigen-Schuster models. A Liapunov functional method is described for proving global limit or oscillation theorems for nonlinear competitive systems when their decision schemes are globally consistent or inconsistent, respectively. The former case is illustrated by a model of a globally stable economic market, and the latter case is illustrated by a model of the voting paradox. Key properties of shunting competitive feedback networks are summarized, including the role of sigmoid signalling, automatic gain control, competitive choice and quantization, tunable filtering, total activity normalization, and noise suppression in pattern transformation and memory storage applications. Connections to models of competitive learning, vector quantization, and categorical perception are noted. Adaptive resonance theory (ART) models for self-stabilizing adaptive pattern recognition in response to complex real-time nonstationary input environments are compared with off-line models such as autoassociators, the Boltzmann machine, and back propagation. Special attention is paid to the stability and capacity of these models, and to the role of top-down expectations and attentional processing in the active regulation of both learning and fast information processing. Models whose performance and learning are regulated by internal gating and matching signals, or by external environmentally generated error signals, are contrasted with models whose learning is regulated by external teacher signals that have no analog in natural real-time environments. Examples from sensory-motor control of adaptive vector encoders, adaptive coordinate transformations, adaptive gain control by visual error signals, and automatic generation of synchronous multijoint movement trajectories illustrate the former model types. Internal matching processes are shown capable of discovering several different types of invariant environmental properties. These include ART mechanisms which discover recognition invariants, adaptive vector encoder mechanisms which discover movement invariants, and autoreceptive associative mechanisms which discover invariants of self-regulating target position maps.},
   author = {Stephen Grossberg},
   doi = {https://doi.org/10.1016/0893-6080(88)90021-4},
   issn = {0893-6080},
   issue = {1},
   journal = {Neural Networks},
   pages = {17-61},
   title = {Nonlinear neural networks: Principles, mechanisms, and architectures},
   volume = {1},
   year = {1988},
}
@article{Lloyd1982,
   author = {S. Lloyd},
   doi = {10.1109/TIT.1982.1056489},
   issn = {0018-9448},
   issue = {2},
   journal = {IEEE Transactions on Information Theory},
   month = {3},
   pages = {129-137},
   title = {Least squares quantization in PCM},
   volume = {28},
   year = {1982},
}
@article{Cox1958,
   abstract = {[A sequence of 0's and 1's is observed and it is suspected that the chance that a particular trial is a 1 depends on the value of one or more independent variables. Tests and estimates for such situations are considered, dealing first with problems in which the independent variable is preassigned and then with independent variables that are functions of the sequence. There is a considerable amount of earlier work, which is reviewed.]},
   author = {D R Cox},
   issn = {00359246},
   issue = {2},
   journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
   pages = {215-242},
   publisher = {[Royal Statistical Society, Wiley]},
   title = {The Regression Analysis of Binary Sequences},
   volume = {20},
   year = {1958},
}
@inproceedings{chen2016,
author = {Chen, Tianqi and Guestrin, Carlos},
title = {XGBoost: A Scalable Tree Boosting System},
year = {2016},
isbn = {9781450342322},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/2939672.2939785},
abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {785–794},
numpages = {10},
keywords = {large-scale machine learning},
location = {San Francisco, California, USA},
series = {KDD '16}
}
@inproceedings{MacQueen1967,
   author = {James MacQueen},
   issue = {14},
   journal = {Proceedings of the fifth Berkeley symposium on mathematical statistics and probability},
   pages = {281-297},
   title = {Some methods for classification and analysis of multivariate observations},
   volume = {1},
   year = {1967},
}
@article{Pocock1979,
   author = {S. J. Pocock},
   doi = {10.2307/2529944},
   issn = {0006341X},
   issue = {1},
   journal = {Biometrics},
   month = {3},
   pages = {183},
   title = {Allocation of Patients to Treatment in Clinical Trials},
   volume = {35},
   year = {1979},
}
@article{Williams2006,
   author = {Ishan Canty Williams and Giselle Corbie-Smith},
   doi = {10.1016/j.cct.2006.05.006},
   issn = {1551-7144},
   issue = {6},
   journal = {Contemporary clinical trials},
   month = {12},
   pages = {580-6},
   pmid = {16839822},
   title = {Investigator beliefs and reported success in recruiting minority participants.},
   volume = {27},
   year = {2006},
}
@article{Baker2017,
   author = {Timothy B. Baker and Stevens S. Smith and Daniel M. Bolt and Wei-Yin Loh and Robin Mermelstein and Michael C. Fiore and Megan E. Piper and Linda M. Collins},
   doi = {10.1016/j.beth.2016.12.005},
   issn = {00057894},
   issue = {4},
   journal = {Behavior Therapy},
   month = {7},
   pages = {567-580},
   title = {Implementing Clinical Research Using Factorial Designs: A Primer},
   volume = {48},
   year = {2017},
}
@article{Park2018,
   author = {Christina K Park and Raed Alhusayen},
   doi = {10.5070/D3242038108},
   issn = {1087-2108},
   issue = {2},
   journal = {Dermatology Online Journal},
   title = {Representation and treatment allocation of racial groups in dermatologic therapy trials: A 2-year review of the literature},
   volume = {24},
   year = {2018},
}
@article{PCORI-2,
   author = {Christopher Sciamanna},
   title = {Comparative Effectiveness of Brief Strength and Balance Exercises and Standard Home-Based Group Exercise for Primary Care Patients with Mobility Disability},
   journal = {PCORI - Healthy Aging: Optimizing Physical and Mental Functioning},
}

@book{Friedman2015,
   author = {Lawrence M. Friedman and Curt D. Furberg and David L. DeMets and David M. Reboussin and Christopher B. Granger},
   city = {Cham},
   doi = {10.1007/978-3-319-18539-2},
   isbn = {978-3-319-18538-5},
   publisher = {Springer International Publishing},
   title = {Fundamentals of Clinical Trials},
   year = {2015},
}
@article{Harrer2019,
   author = {Stefan Harrer and Pratik Shah and Bhavna Antony and Jianying Hu},
   doi = {10.1016/j.tips.2019.05.005},
   issn = {01656147},
   issue = {8},
   journal = {Trends in Pharmacological Sciences},
   month = {8},
   pages = {577-591},
   title = {Artificial Intelligence for Clinical Trial Design},
   volume = {40},
   year = {2019},
}
@article{Rousseeuw1987,
   author = {Peter J. Rousseeuw},
   doi = {10.1016/0377-0427(87)90125-7},
   issn = {03770427},
   journal = {Journal of Computational and Applied Mathematics},
   month = {11},
   pages = {53-65},
   title = {Silhouettes: A graphical aid to the interpretation and validation of cluster analysis},
   volume = {20},
   year = {1987},
}
@book{hu2006theory,
  title={The Theory of Response-Adaptive Randomization in Clinical Trials},
  author={Hu, F. and Rosenberger, W.F.},
  isbn={9780471653967},
  lccn={2006044048},
  series={Wiley Series in Probability and Statistics},
  url={https://books.google.com/books?id=2S6bkQEACAAJ},
  year={2006},
  publisher={Wiley}
}
@article{karrison2003group,
  title={A group sequential, response-adaptive design for randomized clinical trials},
  author={Karrison, Theodore G and Huo, Dezheng and Chappell, Rick},
  journal={Controlled Clinical Trials},
  volume={24},
  number={5},
  pages={506--522},
  year={2003},
  publisher={Elsevier}
}
@article{li1995adaptive,
  title={An adaptive design for maximization of a contingent binary response},
  author={Li, Zhengqing and Durham, Stephen D and Flournoy, Nancy},
  journal={Lecture Notes-Monograph Series},
  pages={179--196},
  year={1995},
  publisher={JSTOR}
}


@InProceedings{pmlr-v23-agrawal12,
  title = 	 {Analysis of Thompson Sampling for the Multi-armed Bandit Problem},
  author = 	 {Agrawal, Shipra and Goyal, Navin},
  booktitle = 	 {Proceedings of the 25th Annual Conference on Learning Theory},
  pages = 	 {39.1--39.26},
  year = 	 {2012},
  editor = 	 {Mannor, Shie and Srebro, Nathan and Williamson, Robert C.},
  volume = 	 {23},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Edinburgh, Scotland},
  month = 	 {25--27 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v23/agrawal12/agrawal12.pdf},
  url = 	 {https://proceedings.mlr.press/v23/agrawal12.html},
}
