Fetching the paper…
Reading the bibliography…
Questions of `how best to acquire data' are essential to modeling and prediction in the natural and social sciences, engineering applications, and beyond.
C. Feng and Y. M. Marzouk (2019), A layered multiple importance sampling scheme for focused optimal Bayesian experimental design · 1903
Earlier work this paper cites.
H. Rahimian and S. Mehrotra (2019), Distributionally robust optimization: A review · 1908
Earlier work this paper cites.
F. Yates (1933), The principles of orthogonality and confounding in replicated experiments, The Journal of Agricultural Science
1933
Earlier work this paper cites.
C. C. Craig and R. A. Fisher (1936), The design of experiments, The American Mathematical Monthly
1936
Earlier work this paper cites.
R. A. Fisher (1936), Design of experiments, British Medical Journal
1936
Earlier work this paper cites.
Technical Communication no. 35, Imperial Bureau of Soil Science
F. Yates (1937), The design and analysis of factorial experiments · 1937
Earlier work this paper cites.
R. C. Bose (1939), On the construction of balanced incomplete block designs, Annals of Eugenics
1939
Earlier work this paper cites.
R. C. Bose and K. R. Nair (1939), Partially balanced incomplete block designs, Sankhyā
1939
Earlier work this paper cites.
F. Yates (1940), Lattice squares, The Journal of Agricultural Science
1940
Earlier work this paper cites.
A. Wald (1943), On the efficient design of statistical investigations, The Annals of Mathematical Statistics
1943
Earlier work this paper cites.
R. T. Cox (1946), Probability, frequency and reasonable expectation, American Journal of Physics
1946
Earlier work this paper cites.
K. Karhunen (1947), Uber lineare Methoden in der Wahrscheinlichkeitsrechnung, Am. Acad. Sci. Fennicade, Ser. A, I
1947
Earlier work this paper cites.
M. Loève (1948), Fonctions aléatoires du second ordre, in Processus Stochastique et Mouvement Brownien
1948
Earlier work this paper cites.
D. M. Kotelyanskiĭ (1950), On the theory of nonnegative and oscillating matrices, Ukrains’kyi Matematychnyi Zhurnal
1950
Earlier work this paper cites.
D. Blackwell (1951), Comparison of experiments, in Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probability
1951
Earlier work this paper cites.
H. Robbins and S. Monro (1951), A stochastic approximation method, The Annals of Mathematical Statistics
1951
Earlier work this paper cites.
G. Elfving (1952), Optimum allocation in linear regression theory, The Annals of Mathematical Statistics
1952
Earlier work this paper cites.
J. Kiefer and J. Wolfowitz (1952), Stochastic estimation of the maximum of a regression function, The Annals of Mathematical Statistics
1952
Earlier work this paper cites.
H. Markowitz (1952), Portfolio selection, The Journal of Finance
1952
Earlier work this paper cites.
M. Rosenblatt (1952), Remarks on a multivariate transformation, The Annals of Mathematical Statistics
1952
Earlier work this paper cites.
D. Blackwell (1953), Equivalent comparisons of experiments, The Annals of Mathematical Statistics
1953
Earlier work this paper cites.
J. R. Blum (1954), Multidimensional stochastic approximation methods, The Annals of Mathematical Statistics
1954
Earlier work this paper cites.
D. V. Lindley (1956), On a measure of the information provided by an experiment, The Annals of Mathematical Statistics
1956
Earlier work this paper cites.
H. Knothe (1957), Contributions to the theory of convex bodies, Michigan Mathematical Journal
1957
Earlier work this paper cites.
J. Kiefer (1958), On the nonrandomized optimality and randomized nonoptimality of symmetrical designs, The Annals of Mathematical Statistics
1958
Earlier work this paper cites.
J. Kiefer (1959), Optimum experimental designs, Journal of the Royal Statistical Society: Series B (Methodological)
1959
Earlier work this paper cites.
J. Kiefer and J. Wolfowitz (1959), Optimum designs in regression problems, The Annals of Mathematical Statistics
1959
Earlier work this paper cites.
M. Stone (1959), Application of a measure of information to the design and comparison of regression experiments, The Annals of Mathematical Statistics
1959
Earlier work this paper cites.
F. R. Gantmacher and M. G. Kreĭn (1960), Oszillationsmatrizen, Oszillationskerne und Kleine Schwingungen Mechanischer Systeme
1960
Earlier work this paper cites.
J. Kiefer and J. Wolfowitz (1960), The equivalence of two extremum problems, Canadian Journal of Mathematics
1960
Earlier work this paper cites.
J. Kiefer (1961 a
1961
Earlier work this paper cites.
J. Kiefer (1961 b
1961
Earlier work this paper cites.
H. Raiffa and R. Schlaifer (1961), Applied Statistical Decision Theory
1961
Earlier work this paper cites.
L. Le Cam (1964), Sufficiency and approximate sufficiency, The Annals of Mathematical Statistics
1964
Earlier work this paper cites.
J. A. Nelder and R. Mead (1965), A simplex method for function minimization, The Computer Journal
1965
Earlier work this paper cites.
K. Fan (1967), Subadditive functions on a distributive lattice and an extension of Szász’s inequality, Journal of Mathematical Analysis and Applications
1967
Earlier work this paper cites.
K. Fan (1968), An inequality for subadditive functions on a distributive lattice, with application to determinantal inequalities, Linear Algebra and its Applications
1968
Earlier work this paper cites.
C. L. Atwood (1969), Optimal and efficient designs of experiments, The Annals of Mathematical Statistics
1969
Earlier work this paper cites.
T. E. Duncan (1970), On the calculation of mutual information, SIAM Journal on Applied Mathematics
1970
Earlier work this paper cites.
V. V. Fedorov (1972), Theory of Optimal Experiments
1972
Earlier work this paper cites.
H. P. Wynn (1972), Results in the theory and construction of D-optimum experimental designs, Journal of the Royal Statistical Society: Series B (Methodological)
1972
Earlier work this paper cites.
P. Whittle (1973), Some general points in the theory of optimal experimental design, Journal of the Royal Statistical Society: Series B (Methodological)
1973
Earlier work this paper cites.
J. Kiefer (1974), General equivalence theory for optimum designs (approximate theory), The Annals of Statistics
1974
Earlier work this paper cites.
J. Močkus (1975), On Bayesian methods for seeking the extremum, in Optimization Techniques IFIP Technical Conference
1975
Earlier work this paper cites.
M. L. Fisher, G. L. Nemhauser and L. A. Wolsey (1978), An analysis of approximations for maximizing submodular set functions—II, Mathematical Programming
1978
Earlier work this paper cites.
G. L. Nemhauser and L. A. Wolsey (1978), Best algorithms for approximating the maximum of a submodular set function, Mathematics of Operations Research
1978
Earlier work this paper cites.
G. L. Nemhauser, L. A. Wolsey and M. L. Fisher (1978), An analysis of approximations for maximizing submodular set functions—I, Mathematical Programming
1978
Earlier work this paper cites.
J. M. Bernardo (1979), Expected information as expected utility, The Annals of Statistics
1979
Earlier work this paper cites.
M. D. McKay, R. J. Beckman and W. J. Conover (1979), A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics
1979
Earlier work this paper cites.
R. D. Cook and C. J. Nachtsheim (1980), A comparison of algorithms for constructing exact D-optimal designs, Technometrics
1980
Earlier work this paper cites.
A. Hedayat (1981), Study of optimality criteria in design of experiments, in Statistics and Related Topics: International Symposium Proceedings
1981
Earlier work this paper cites.
L. Lovász (1983), Submodular functions and convexity, in Mathematical Programming The State of the Art: Bonn 1982
1982
Earlier work this paper cites.
M. D. Donsker and S. R. S. Varadhan (1983), Asymptotic evaluation of certain Markov process expectations for large time. IV, Communications on Pure and Applied Mathematics
1983
Earlier work this paper cites.
M. E. Johnson and C. J. Nachtsheim (1983), Some guidelines for constructing exact D-optimal designs on convex design spaces, Technometrics
1983
Earlier work this paper cites.
A. K. Kelmans and B. N. Kimelfeld (1983), Multiplicative submodularity of a matrix’s principal minor as a function of the set of its rows and some combinatorial applications, Discrete Mathematics
1983
Earlier work this paper cites.
Y. Nesterov (1983), A method of solving a convex programming problem with convergence rate 𝒪 ( 1 / k 2 ) \mathcal{O}\left(1/k^{2}\right) , Soviet Mathematics Doklady
1983
Earlier work this paper cites.
W. F. Caselton and J. V. Zidek (1984), Optimal monitoring network designs, Statistics & Probability Letters
1984
Earlier work this paper cites.
K. Chaloner (1984), Optimal Bayesian experimental design for linear models, The Annals of Statistics
1984
Earlier work this paper cites.
M. Conforti and G. Cornuéjols (1984), Submodular set functions, matroids and the greedy algorithm: Tight worst-case bounds and some generalizations of the Rado–Edmonds theorem, Discrete Applied Mathematics
1984
Earlier work this paper cites.
D. M. Steinberg and W. G. Hunter (1984), Experimental design: Review and comment, Technometrics
1984
Earlier work this paper cites.
H. P. Wynn (1984), Jack Kiefer’s contributions to experimental design, The Annals of Statistics
1984
Earlier work this paper cites.
J. O. Berger (1985), Statistical Decision Theory and Bayesian Analysis
1985
Earlier work this paper cites.
C. R. Johnson and W. W. Barrett (1985), Spanning-tree extensions of the Hadamard–Fischer inequalities, Linear Algebra and its Applications
1985
Earlier work this paper cites.
L. Pronzato and E. Walter (1985), Robust experiment design via stochastic approximation, Mathematical Biosciences
1985
Earlier work this paper cites.
L. F. Kozachenko and N. N. Leonenko (1987), A statistical estimate for the entropy of a random vector, Problems of Information Transmission
1987
Earlier work this paper cites.
M. C. Shewry and H. P. Wynn (1987), Maximum entropy sampling, Journal of Applied Statistics
1987
Earlier work this paper cites.
Available at http://ecommons.cornell.edu/ bitstream/handle/1813/8664/TR000781.pdf?sequence=1
D. Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro process, Technical report, Cornell University · 1988
Earlier work this paper cites.
K. Chaloner and K. Larntz (1989), Optimal Bayesian design applied to logistic regression experiments, Journal of Statistical Planning and Inference
1989
Earlier work this paper cites.
I. Ford, D. M. Titterington and C. P. Kitsos (1989), Recent advances in nonlinear experimental design, Technometrics
1989
Earlier work this paper cites.
T. Robertazzi and S. Schwartz (1989), An accelerated sequential algorithm for producing D-optimal designs, SIAM Journal on Scientific and Statistical Computing
1989
Earlier work this paper cites.
J. Sacks, W. J. Welch, T. J. Mitchell and H. P. Wynn (1989), Design and analysis of computer experiments, Statistical Science
1989
Earlier work this paper cites.
K. R. Shah and B. K. Sinha (1989), Theory of Optimal Designs
1989
Earlier work this paper cites.
M. E. Johnson, L. M. Moore and D. Ylvisaker (1990), Minimax and maximin distance designs, Journal of Statistical Planning and Inference
1990
Earlier work this paper cites.
K. Healy and L. W. Schruben (1991), Retrospective simulation response optimization, in Proceedings of the 1991 Winter Simulation Conference (WSC ’91)
1991
Earlier work this paper cites.
J. Pilz (1991), Bayesian Estimation and Experimental Design in Linear Regression Models
1991
Earlier work this paper cites.
A. Shapiro (1991), Asymptotic analysis of stochastic programs, Annals of Operations Research
1991
Earlier work this paper cites.
G. E. P. Box (1992), Sequential experimentation and sequential assembly of designs, Quality Engineering
1992
Earlier work this paper cites.
D. J. C. MacKay (1992), Information-based objective functions for active data selection, Neural Computation
1992
Earlier work this paper cites.
H. Niederreiter (1992), Random Number Generation and Quasi-Monte Carlo Methods
1992
Earlier work this paper cites.
A. B. Owen (1992), Orthogonal arrays for computer experiments, integration and visualization, Statistica Sinica
1992
Earlier work this paper cites.
B. T. Polyak and A. B. Juditsky (1992), Acceleration of stochastic approximation by averaging, SIAM Journal on Control and Optimization
1992
Earlier work this paper cites.
B. Tang (1993), Orthogonal array-based Latin hypercubes, Journal of the American Statistical Association
1993
Earlier work this paper cites.
J. O. Berger (1994), An overview of robust Bayesian analysis (with discussion), Test
1994
Earlier work this paper cites.
G. Gürkan, A. Y. Özge and S. M. Robinson (1994), Sample-path optimization in simulation, in Proceedings of the 1994 Winter Simulation Conference (WSC ’94)
1994
Earlier work this paper cites.
K. Chaloner and I. Verdinelli (1995), Bayesian experimental design: A review, Statistical Science
1995
Earlier work this paper cites.
A. DasGupta (1995), Review of optimal Bayes designs, Technical report, Purdue University, West Lafayette, IN
1995
Earlier work this paper cites.
C.-W. Ko, J. Lee and M. Queyranne (1995), An exact algorithm for maximum entropy sampling, Operations Research
1995
Earlier work this paper cites.
D. D. Lewis (1995), A sequential algorithm for training text classifiers: Corrigendum and additional data, SIGIR Forum
1995
Earlier work this paper cites.
R. K. Meyer and C. J. Nachtsheim (1995), The coordinate-exchange algorithm for constructing exact optimal experimental designs, Technometrics
1995
Earlier work this paper cites.
D. A. Cohn, Z. Ghahramani and M. I. Jordan (1996), Active learning with statistical models, Journal of Artificial Intelligence Research
1996
Earlier work this paper cites.
V. V. Fedorov (1996), Design of spatial experiments: Model fitting and prediction, Technical report, Oak Ridge National Laboratory, Oak Ridge, TN
1996
Earlier work this paper cites.
L. P. Kaelbling, M. L. Littman and A. W. Moore (1996), Reinforcement learning: A survey, Journal of Artificial Intelligence Research
1996
Earlier work this paper cites.
V. V. Fedorov and D. Flanagan (1997), Optimal monitoring network design based on Mercer’s expansion of covariance kernel, Journal of Combinatorics, Information and System Sciences
1997
Earlier work this paper cites.
V. V. Fedorov and P. Hackl (1997), Model-Oriented Design of Experiments
1997
Earlier work this paper cites.
Y. Freund, H. S. Seung, E. Shamir and N. Tishby (1997), Selective sampling using the query by committee algorithm, Machine Learning
1997
Earlier work this paper cites.
D. S. Hochba (1997), Approximation algorithms for NP-hard problems, ACM SIGACT News
1997
Earlier work this paper cites.
V. Torczon (1997), On the convergence of pattern search algorithms, SIAM Journal on Optimization
1997
Earlier work this paper cites.
R. E. Caflisch (1998), Monte Carlo and quasi-Monte Carlo methods, Acta numerica
1998
Earlier work this paper cites.
B. P. Carlin, J. B. Kadane and A. E. Gelfand (1998), Approaches for optimal sequential decision analysis in clinical trials, Biometrics
1998
Earlier work this paper cites.
D. R. Jones, M. Schonlau and W. J. Welch (1998), Efficient global optimization of expensive black-box functions, Journal of Global Optimization
1998
Earlier work this paper cites.
L. P. Kaelbling, M. L. Littman and A. R. Cassandra (1998), Planning and acting in partially observable stochastic domains, Artificial Intelligence
1998
Earlier work this paper cites.
E. L. Lehmann and G. Casella (1998), Theory of Point Estimation
1998
Earlier work this paper cites.
V. Norkin, G. Pflug and A. Ruszczynski (1998), A branch and bound method for stochastic global optimization, Mathematical Programming
1998
Earlier work this paper cites.
C. H. Papadimitriou and K. Steiglitz (1998), Combinatorial Optimization: Algorithms and Complexity
1998
Earlier work this paper cites.
E. Pardo-Igúzquiza (1998), Maximum likelihood estimation of spatial covariance parameters, Mathematical Geology
1998
Earlier work this paper cites.
J. C. Spall (1998 a
1998
Earlier work this paper cites.
J. C. Spall (1998 b
1998
Earlier work this paper cites.
P. Artzner, F. Delbaen, J. Eber and D. Heath (1999), Coherent measures of risk, Mathematical Finance
1999
Earlier work this paper cites.
V. R. Konda and J. N. Tsitsiklis (1999), Actor-critic algorithms, in Advances in Neural Information Processing Systems 12
1999
Earlier work this paper cites.
S. Korkel, I. Bauer, H. G. Bock and J. P. Schloder (1999), A sequential approach for nonlinear optimum experimental design in DAE systems, in Scientific Computing in Chemical Engineering II
1999
Earlier work this paper cites.
W.-K. Mak, D. P. Morton and R. K. Wood (1999), Monte Carlo bounding techniques for determining solution quality in stochastic programs, Operations Research Letters
1999
Earlier work this paper cites.
R. S. Sutton, D. McAllester, S. P. Singh and Y. Mansour (1999), Policy gradient methods for reinforcement learning with function approximation, in Advances in Neural Information Processing Systems 12
1999
Earlier work this paper cites.
L. A. Wolsey and G. L. Nemhauser (1999), Integer and Combinatorial Optimization
1999
Earlier work this paper cites.
C. Zong (1999), Sphere Packings
1999
Earlier work this paper cites.
J. M. Bernardo and A. F. M. Smith (2000), Bayesian Theory
2000
Earlier work this paper cites.
R. Gautier and L. Pronzato (2000), Adaptive control for sequential design, Discussiones Mathematicae Probability and Statistics
2000
Earlier work this paper cites.
J. King and W.-K. Wong (2000), Minimax D-optimal designs for the logistic model, Biometrics
2000
Earlier work this paper cites.
A. Ng and S. Russell (2000), Algorithms for inverse reinforcement learning, in Proceedings of the 17th International Conference on Machine Learning (ICML 2000)
2000
Earlier work this paper cites.
P. Sebastiani and H. P. Wynn (2000), Maximum entropy sampling and optimal Bayesian experimental design, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2000
Earlier work this paper cites.
S. Seo, M. Wallat, T. Graepel and K. Obermayer (2000), Gaussian process regression: Active data selection and test point rejection, in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000)
2000
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (2001), Lectures on Modern Convex Optimization
2001
Earlier work this paper cites.
M. Clyde (2001), Experimental design: Bayesian designs, in International Encyclopedia of the Social & Behavioral Sciences
2001
Earlier work this paper cites.
M. C. Kennedy and A. O’Hagan (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2001
Earlier work this paper cites.
V. V. Vazirani (2001), Approximation Algorithms
2001
Earlier work this paper cites.
C. Audet and J. E. Dennis (2002), Analysis of generalized pattern searches, SIAM Journal on Optimization
2002
Earlier work this paper cites.
F. R. Bach and M. I. Jordan (2002), Kernel independent component analysis, Journal of Machine Learning Research
2002
Earlier work this paper cites.
A. J. Kleywegt, A. Shapiro and T. Homem-de Mello (2002), The sample average approximation method for stochastic discrete optimization, SIAM Journal on Optimization
2002
Earlier work this paper cites.
L. Pronzato and É. Thierry (2002), Sequential experimental design and response optimisation, Statistical Methods and Applications
2002
Earlier work this paper cites.
R. T. Rockafellar and S. Uryasev (2002), Conditional value-at-risk for general loss distributions, Journal of Banking & Finance
2002
Earlier work this paper cites.
D. Barber and F. Agakov (2003), The IM algorithm: A variational approach to information maximization, in Advances in Neural Information Processing Systems 16
2003
Earlier work this paper cites.
A. E. Brockwell and J. B. Kadane (2003), A gridding method for Bayesian sequential decision problems, Journal of Computational and Graphical Statistics
2003
Earlier work this paper cites.
J. A. Christen and M. Nakamura (2003), Sequential stopping rules for species accumulation, Journal of Agricultural, Biological & Environmental Statistics
2003
Earlier work this paper cites.
E. T. Jaynes and G. L. Bretthorst (2003), Probability Theory: The Logic of Science
2003
Earlier work this paper cites.
H. J. Kushner and G. G. Yin (2003), Stochastic Approximation and Recursive Algorithms and Applications
2003
Earlier work this paper cites.
S. A. Murphy (2003), Optimal dynamic treatment regimes, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2003
Earlier work this paper cites.
K. J. Ryan (2003), Estimating expected information gains for experimental designs with application to the random fatigue-limit model, Journal of Computational and Graphical Statistics
2003
Earlier work this paper cites.
A. Schrijver (2003), Combinatorial Optimization: Polyhedra and Efficiency
2003
Earlier work this paper cites.
C. Audet (2004), Convergence results for generalized pattern search algorithms are tight, Optimization and Engineering
2004
Earlier work this paper cites.
S. P. Boyd and L. Vandenberghe (2004), Convex Optimization
2004
Cited alongside, same era.
A. Kraskov, H. Stögbauer and P. Grassberger (2004), Estimating mutual information, Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
2004
Cited alongside, same era.
M. Sviridenko (2004), A note on maximizing a submodular set function subject to a knapsack constraint, Operations Research Letters
2004
Cited alongside, same era.
P. K. Agarwal, S. Har-Peled and K. R. Varadarajan (2005), Geometric approximation via coresets, Combinatorial and Computational Geometry
2005
Cited alongside, same era.
D. P. Bertsekas (2005), Dynamic Programming and Optimal Control
2005
Cited alongside, same era.
J. Beck, B. M. Dia, L. F. Espath, Q. Long and R. Tempone (2018), Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain, Computer Methods in Applied Mechanics and Engineering
2018
Later among the works it cites.
M. I. Belghazi, A. Baratin, S. Rajeswar, S. Ozair, Y. Bengio, A. Courville and R. D. Hjelm (2018), Mutual information neural estimation, in Proceedings of the 35th International Conference on Machine Learning (ICML 2018)
2018
Later among the works it cites.
I. Bogunovic, J. Zhao and V. Cevher (2018), Robust maximization of non-submodular objectives, in Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics
2018
Later among the works it cites.
C. Borges and G. Biros (2018), Reconstruction of a compactly supported sound profile in the presence of a random background medium, Inverse Problems
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. I. Bogachev, A. V. Kolesnikov and K. V. Medvedev (2005), Triangular transformations of measures, Sbornik Mathematics
2005
Cited alongside, same era.
S. Fujishige (2005), Submodular Functions and Optimization
2005
Cited alongside, same era.
Z. Zhu and M. L. Stein (2005), Spatial sampling design for parameter estimation of the covariance function, Journal of Statistical Planning and Inference
2005
Cited alongside, same era.
R. Baptista, B. Hosseini, N. B. Kovachki and Y. Marzouk (2024 b · 2006
Cited alongside, same era.
T. A. Cover and J. A. Thomas (2006), Elements of Information Theory
2006
Cited alongside, same era.
J. Kaipio and E. Somersalo (2006), Statistical and Computational Inverse Problems
2006
Cited alongside, same era.
J. Nocedal and S. J. Wright (2006), Numerical Optimization
2006
Cited alongside, same era.
T. Campbell and T. Broderick (2018), Bayesian coreset construction via greedy iterative geodesic ascent, in Proceedings of the 35th International Conference on Machine Learning (ICML 2018)
2018
Later among the works it cites.
P. I. Frazier (2018), Bayesian optimization, INFORMS TutORials in Operations Research
2018
Later among the works it cites.
W. Gao, S. Oh and P. Viswanath (2018), Demystifying fixed k k -nearest neighbor information estimators, IEEE Transactions on Information Theory
2018
Later among the works it cites.
L. Giraldi, O. P. Le Maître, I. Hoteit and O. M. Knio (2018), Optimal projection of observations in a Bayesian setting, Computational Statistics & Data Analysis
2018
Later among the works it cites.
O. Karaca and M. Kamgarpour (2018), Exploiting weak supermodularity for coalition-proof mechanisms, in 2018 IEEE Conference on Decision and Control (CDC)
2018
Later among the works it cites.
R. E. Morrison, T. A. Oliver and R. D. Moser (2018), Representing model inadequacy: A stochastic operator approach, SIAM/ASA Journal on Uncertainty Quantification
2018
Later among the works it cites.
A. M. Overstall, J. M. McGree and C. C. Drovandi (2018), An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions, Statistics and Computing
2018
Later among the works it cites.
T. Rainforth, R. Cornish, H. Yang, A. Warrington and F. Wood (2018), On nesting Monte Carlo estimators, in Proceedings of the 35th International Conference on Machine Learning (ICML 2018)
2018
Later among the works it cites.
D. Rudolf and B. Sprungk (2018), On a generalization of the preconditioned Crank–Nicolson Metropolis algorithm, Foundations of Computational Mathematics
2018
Later among the works it cites.
L. Ruthotto, J. Chung and M. Chung (2018), Optimal experimental design for inverse problems with state constraints, SIAM Journal on Scientific Computing
2018
Later among the works it cites.
T. J. Santner, B. J. Williams and W. I. Notz (2018), The Design and Analysis of Computer Experiments
2018
Later among the works it cites.
S. Shashaani, F. S. Hashemi and R. Pasupathy (2018), ASTRO-DF: A class of adaptive sampling trust-region algorithms for derivative-free stochastic optimization, SIAM Journal on Optimization
2018
Later among the works it cites.
A. Spantini, D. Bigoni and Y. Marzouk (2018), Inference via low-dimensional couplings, Journal of Machine Learning Research
2018
Later among the works it cites.
A. Stuart and A. Teckentrup (2018), Posterior consistency for Gaussian process approximations of Bayesian posterior distributions, Mathematics of Computation
2018
Later among the works it cites.
R. S. Sutton and A. G. Barto (2018), Reinforcement Leaning
2018
Later among the works it cites.
A. van den Oord, Y. Li and O. Vinyals (2018), Representation learning with contrastive predictive coding · 2018
Later among the works it cites.
N. Bochkina (2019), Bernstein–von Mises theorem and misspecified models: A review, in Foundations of Modern Statistics
2019
Later among the works it cites.
T. Campbell and B. Beronov (2019), Sparse variational inference: Bayesian coresets from scratch, in Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
T. Campbell and T. Broderick (2019), Automated scalable Bayesian inference via Hilbert coresets, Journal of Machine Learning Research
2019
Later among the works it cites.
A. Foster, M. Jankowiak, E. Bingham, P. Horsfall, Y. W. Teh, T. Rainforth and N. Goodman (2019), Variational Bayesian optimal experimental design, in Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
D. Kuhn, P. M. Esfahani, V. A. Nguyen and S. Shafieezadeh-Abadeh (2019), Wasserstein distributionally robust optimization: Theory and applications in machine learning, in Operations Research & Management Science in the Age of Analytics
2019
Later among the works it cites.
J. Larson, M. Menickelly and S. M. Wild (2019), Derivative-free optimization methods, Acta Numerica
2019
Later among the works it cites.
V. Madan, M. Singh, U. Tantipongpipat and W. Xie (2019), Combinatorial algorithms for optimal design, in Proceedings of the 32nd Conference on Learning Theory
2019
Later among the works it cites.
J. W. Miller and D. B. Dunson (2019), Robust Bayesian inference via coarsening, Journal of the American Statistical Association
2019
Later among the works it cites.
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi and G. Tucker (2019), On variational bounds of mutual information, in Proceedings of the 36th International Conference on Machine Learning (ICML 2019)
2019
Later among the works it cites.
Z. B. Riley, R. A. Perez, G. W. Bartram, S. M. Spottswood, B. P. Smarslok and T. J. Beberniss (2019), Aerothermoelastic experimental design for the AEDC/VKF Tunnel C: Challenges associated with measuring the response of flexible panels in high-temperature, high-speed wind tunnels, Journal of Sound and Vibration
2019
Later among the works it cites.
K. Sargsyan, X. Huan and H. N. Najm (2019), Embedded model error representation for Bayesian model calibration, International Journal for Uncertainty Quantification
2019
Later among the works it cites.
J. Song, Y. Chen and Y. Yue (2019), A general framework for multi-fidelity Bayesian optimization with Gaussian processes, in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics
2019
Later among the works it cites.
J. Wu and P. Frazier (2019), Practical two-step lookahead Bayesian optimization, in Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
Z. Ao and J. Li (2020), An approximate KLD based experimental design for models with intractable likelihoods, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics
2020
Later among the works it cites.
J. Beck, B. M. Dia, L. Espath and R. Tempone (2020), Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design, International Journal for Numerical Methods in Engineering
2020
Later among the works it cites.
A. G. Carlon, B. M. Dia, L. Espath, R. H. Lopez and R. Tempone (2020), Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization, Computer Methods in Applied Mechanics and Engineering
2020
Later among the works it cites.
C. U. Carmona and G. K. Nicholls (2020), Semi-modular inference: Enhanced learning in multi-modular models by tempering the influence of components, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics
2020
Later among the works it cites.
A. Foster, M. Jankowiak, M. O’Meara, Y. W. Teh and T. Rainforth (2020), A unified stochastic gradient approach to designing Bayesian-optimal experiments, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics
2020
Later among the works it cites.
T. Goda, T. Hironaka and T. Iwamoto (2020), Multilevel Monte Carlo estimation of expected information gains, Stochastic Analysis and Applications
2020
Later among the works it cites.
R. B. Gramacy (2020), Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences
2020
Later among the works it cites.
L. Herrmann, C. Schwab and J. Zech (2020), Deep neural network expression of posterior expectations in Bayesian PDE inversion, Inverse Problems
2020
Later among the works it cites.
V. R. Joseph, E. Gul and S. Ba (2020), Designing computer experiments with multiple types of factors: The MaxPro approach, Journal of Quality Technology
2020
Later among the works it cites.
S. Kleinegesse and M. U. Gutmann (2020), Bayesian experimental design for implicit models by mutual information neural estimation, in Proceedings of the 37th International Conference on Machine Learning (ICML 2020)
2020
Later among the works it cites.
I. Kobyzev, S. J. Prince and M. A. Brubaker (2020), Normalizing flows: An introduction and review of current methods, IEEE Transactions on Pattern Analysis and Machine Intelligence
2020
Later among the works it cites.
K. Koval, A. Alexanderian and G. Stadler (2020), Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs, Inverse Problems
2020
Later among the works it cites.
L. C. Lau and H. Zhou (2020), A spectral approach to network design, in Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2020)
2020
Later among the works it cites.
V. Madan, A. Nikolov, M. Singh and U. Tantipongpipat (2020), Maximizing determinants under matroid constraints, in 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS)
2020
Later among the works it cites.
D. McAllester and K. Stratos (2020), Formal limitations on the measurement of mutual information, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics
2020
Later among the works it cites.
P. Mertikopoulos, N. Hallak, A. Kavis and V. Cevher (2020), On the almost sure convergence of stochastic gradient descent in non-convex problems, in Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
S. Mohamed, M. Rosca, M. Figurnov and A. Mnih (2020), Monte Carlo gradient estimation in machine learning, Journal of Machine Learning Research
2020
Later among the works it cites.
C. Schillings, B. Sprungk and P. Wacker (2020), On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems, Numerische Mathematik
2020
Later among the works it cites.
M. Singh and W. Xie (2020), Approximation algorithms for D-optimal design, Mathematics of Operations Research
2020
Later among the works it cites.
Available at https://openreview.net/forum?id=B1x62TNtDS
J. Song and S. Ermon (2020), Understanding the limitations of variational mutual information estimators, in Proceedings of the 8th International Conference on Learning Representations (ICLR 2020) · 2020
Later among the works it cites.
B. Sprungk (2020), On the local Lipschitz stability of Bayesian inverse problems, Inverse Problems
2020
Later among the works it cites.
J. Wang, S. C. Clark, E. Liu and P. I. Frazier (2020), Parallel Bayesian global optimization of expensive functions, Operations Research
2020
Later among the works it cites.
Z. Xu and Q. Liao (2020), Gaussian process based expected information gain computation for Bayesian optimal design, Entropy
2020
Later among the works it cites.
D. Zhan and H. Xing (2020), Expected improvement for expensive optimization: A review, Journal of Global Optimization
2020
Later among the works it cites.
S. Zheng, D. Hayden, J. Pacheco and J. W. Fisher (2020), Sequential Bayesian experimental design with variable cost structure, in Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
A. Alexanderian (2021), Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: A review, Inverse Problems
2021
Later among the works it cites.
A. Alexanderian, N. Petra, G. Stadler and I. Sunseri (2021), Optimal design of large-scale Bayesian linear inverse problems under reducible model uncertainty: Good to know what you don’t know, SIAM/ASA Journal on Uncertainty Quantification
2021
Later among the works it cites.
A. Blanchard and T. Sapsis (2021), Output-weighted optimal sampling for Bayesian experimental design and uncertainty quantification, SIAM/ASA Journal on Uncertainty Quantification
2021
Later among the works it cites.
Available at https://openreview.net/forum?id=AY8zfZm0tDd
X. Chen, C. Wang, Z. Zhou and K. Ross (2021), Randomized ensembled double Q-learning: Learning fast without a model, in 9th International Conference on Learning Representations (ICLR 2021) · 2021
Later among the works it cites.
A. Foster, D. R. Ivanova, I. Malik and T. Rainforth (2021), Deep adaptive design: Amortizing sequential Bayesian experimental design, in Proceedings of the 38th International Conference on Machine Learning (ICML 2021)
2021
Later among the works it cites.
O. Ghattas and K. Willcox (2021), Learning physics-based models from data: Perspectives from inverse problems and model reduction, Acta Numerica
2021
Later among the works it cites.
E. Giné and R. Nickl (2021), Mathematical Foundations of Infinite-Dimensional Statistical Models
2021
Later among the works it cites.
D. R. Ivanova, A. Foster, S. Kleinegesse, M. U. Gutmann and T. Rainforth (2021), Implicit deep adaptive design: Policy-based experimental design without likelihoods, in Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
J. Jagalur-Mohan and Y. Marzouk (2021), Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design, Journal of Machine Learning Research
2021
Later among the works it cites.
S. Kleinegesse and M. U. Gutmann (2021), Gradient-based Bayesian experimental design for implicit models using mutual information lower bounds · 2021
Later among the works it cites.
S. Kleinegesse, C. Drovandi and M. U. Gutmann (2021), Sequential Bayesian experimental design for implicit models via mutual information, Bayesian Analysis
2021
Later among the works it cites.
T. Manole, S. Balakrishnan, J. Niles-Weed and L. Wasserman (2021), Plugin estimation of smooth optimal transport maps · 2021
Later among the works it cites.
J. A. Melendez, R. J. Furnstahl, H. W. Grießhammer, J. A. McGovern, D. R. Phillips and M. T. Pratola (2021), Designing optimal experiments: An application to proton Compton scattering, The European Physical Journal A
2021
Later among the works it cites.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed and B. Lakshminarayanan (2021), Normalizing flows for probabilistic modeling and inference, Journal of Machine Learning Research
2021
Later among the works it cites.
E. Pompe and P. E. Jacob (2021), Asymptotics of cut distributions and robust modular inference using posterior bootstrap · 2021
Later among the works it cites.
A.-A. Pooladian and J. Niles-Weed (2021), Entropic estimation of optimal transport maps · 2021
Later among the works it cites.
A. Shapiro, D. Dentcheva and A. Ruszczynski (2021), Lectures on Stochastic Programming: Modeling and Theory
2021
Later among the works it cites.
W. Shen and X. Huan (2021), Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning · 2021
Later among the works it cites.
V. Tzoumas, L. Carlone, G. J. Pappas and A. Jadbabaie (2021), LQG control and sensing co-design, IEEE Transactions on Automatic Control
2021
Later among the works it cites.
U. Villa, N. Petra and O. Ghattas (2021), HIPPYlib: An extensible software framework for large-scale inverse problems governed by PDEs: Part I: Deterministic inversion and linearized Bayesian inference, ACM Transactions on Mathematical Software
2021
Later among the works it cites.
B. P. Weaver and W. Q. Meeker (2021), Bayesian methods for planning accelerated repeated measures degradation tests, Technometrics
2021
Later among the works it cites.
J. Zhang, S. Bi and G. Zhang (2021), A scalable gradient-free method for Bayesian experimental design with implicit models, in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics
2021
Later among the works it cites.
A. Alexanderian, R. Nicholson and N. Petra (2022), Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty · 2022
Later among the works it cites.
R. Baptista, Y. Marzouk and O. Zahm (2022), Gradient-based data and parameter dimension reduction for Bayesian models: An information theoretic perspective · 2022
Later among the works it cites.
T. Blau, E. V. Bonilla, I. Chades and A. Dezfouli (2022), Optimizing sequential experimental design with deep reinforcement learning, in Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
2022
Later among the works it cites.
T. Cui and X. T. Tong (2022), A unified performance analysis of likelihood-informed subspace methods, Bernoulli
2022
Later among the works it cites.
Y. Englezou, T. W. Waite and D. C. Woods (2022), Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models, Statistics and Computing
2022
Later among the works it cites.
J. Go and T. Isaac (2022), Robust expected information gain for optimal Bayesian experimental design using ambiguity sets, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence
2022
Later among the works it cites.
T. Goda, T. Hironaka, W. Kitade and A. Foster (2022), Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs, SIAM Journal on Scientific Computing
2022
Later among the works it cites.
Available at https://cran.r-project.org/package=plgp
R. B. Gramacy (2022), plgp: Particle learning of Gaussian processes · 2022
Later among the works it cites.
M. Hainy, D. J. Price, O. Restif and C. Drovandi (2022), Optimal Bayesian design for model discrimination via classification, Statistics and Computing
2022
Later among the works it cites.
X. D. He, S. Kou and X. Peng (2022), Risk measures: Robustness, elicitability, and backtesting, Annual Review of Statistics and Its Application
2022
Later among the works it cites.
T. Helin and R. Kretschmann (2022), Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems, Numerische Mathematik
2022
Later among the works it cites.
T. Helin, N. Hyvönen and J.-P. Puska (2022), Edge-promoting adaptive Bayesian experimental design for X-ray imaging, SIAM Journal on Scientific Computing
2022
Later among the works it cites.
D. P. Kouri, J. D. Jakeman and J. Gabriel Huerta (2022), Risk-adapted optimal experimental design, SIAM/ASA Journal on Uncertainty Quantification
2022
Later among the works it cites.
L. C. Lau and H. Zhou (2022), A local search framework for experimental design, SIAM Journal on Computing
2022
Later among the works it cites.
P. Müller, Y. Duan and M. Garcia Tec (2022), Simulation‐based sequential design, Pharmaceutical Statistics
2022
Later among the works it cites.
A. Nikolov, M. Singh and U. Tantipongpipat (2022), Proportional volume sampling and approximation algorithms for A-optimal design, Mathematics of Operations Research
2022
Later among the works it cites.
A. Overstall and J. McGree (2022), Bayesian decision-theoretic design of experiments under an alternative model, Bayesian Analysis
2022
Later among the works it cites.
A. M. Overstall (2022), Properties of Fisher information gain for Bayesian design of experiments, Journal of Statistical Planning and Inference
2022
Later among the works it cites.
C. Riis, F. Antunes, F. Hüttel, C. Lima Azevedo and F. Pereira (2022), Bayesian active learning with fully Bayesian Gaussian processes, in Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
J. O. Royset (2022), Risk-adaptive approaches to learning and decision making: A survey · 2022
Later among the works it cites.
S. Sreekumar and Z. Goldfeld (2022), Neural estimation of statistical divergences, Journal of Machine Learning Research
2022
Later among the works it cites.
S. Wang and Y. Marzouk (2022), On minimax density estimation via measure transport · 2022
Later among the works it cites.
O. Zahm, T. Cui, K. Law, A. Spantini and Y. Marzouk (2022), Certified dimension reduction in nonlinear Bayesian inverse problems, Mathematics of Computation
2022
Later among the works it cites.
X. Zhang, J. Blanchet, Y. Marzouk, V. A. Nguyen and S. Wang (2022), Distributionally robust Gaussian process regression and Bayesian inverse problems · 2022
Later among the works it cites.
A. Attia, S. Leyffer and T. Munson (2023), Robust A-optimal experimental design for Bayesian inverse problems · 2023
Later among the works it cites.
Available at doi:10.1007/s10208-023-09630-x
R. Baptista, Y. Marzouk and O. Zahm (2023), On the representation and learning of monotone triangular transport maps, Foundations of Computational Mathematics · 2023
Later among the works it cites.
T. A. Catanach and N. Das (2023), Metrics for Bayesian optimal experiment design under model misspecification, in 2023 62nd IEEE Conference on Decision and Control (CDC)
2023
Later among the works it cites.
A. Chowdhary, S. Tong, G. Stadler and A. Alexanderian (2023), Sensitivity analysis of the information gain in infinite-dimensional Bayesian linear inverse problems · 2023
Later among the works it cites.
T. Cui, S. Dolgov and O. Zahm (2023), Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction, Journal of Computational Physics
2023
Later among the works it cites.
P. Czyż, F. Grabowski, J. Vogt, N. Beerenwinkel and A. Marx (2023), Beyond normal: On the evaluation of mutual information estimators, in Advances in Neural Information Processing Systems 36
2023
Later among the works it cites.
M. Dewaskar, C. Tosh, J. Knoblauch and D. B. Dunson (2023), Robustifying likelihoods by optimistically re-weighting data · 2023
Later among the works it cites.
D.-L. Duong, T. Helin and J. R. Rojo-Garcia (2023), Stability estimates for the expected utility in Bayesian optimal experimental design, Inverse Problems
2023
Later among the works it cites.
J. H. Huggins and J. W. Miller (2023), Reproducible model selection using bagged posteriors, Bayesian Analysis
2023
Later among the works it cites.
N. Kennamer, S. Walton and A. Ihler (2023), Design amortization for Bayesian optimal experimental design, in Proceedings of the 37th AAAI Conference on Artificial Intelligence
2023
Later among the works it cites.
N. A. Letizia, N. Novello and A. M. Tonello (2023), Variational f f -divergence and derangements for discriminative mutual information estimation · 2023
Later among the works it cites.
D. Prangle, S. Harbisher and C. S. Gillespie (2023), Bayesian experimental design without posterior calculations: An adversarial approach, Bayesian Analysis
2023
Later among the works it cites.
T. Rainforth, A. Foster, D. R. Ivanova and F. B. Smith (2023), Modern Bayesian experimental design, Statistical Science
2023
Later among the works it cites.
W. Shen (2023), Reinforcement learning based sequential and robust Bayesian optimal experimental design, PhD thesis, University of Michigan
2023
Later among the works it cites.
W. Shen and X. Huan (2023), Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning, Computer Methods in Applied Mechanics and Engineering
2023
Later among the works it cites.
W. Shen, J. Dong and X. Huan (2023), Variational sequential optimal experimental design using reinforcement learning · 2023
Later among the works it cites.
V. Spokoiny (2023), Dimension free nonasymptotic bounds on the accuracy of high-dimensional Laplace approximation, SIAM/ASA Journal on Uncertainty Quantification
2023
Later among the works it cites.
M. Tec, Y. Duan and P. Müller (2023), A comparative tutorial of Bayesian sequential design and reinforcement learning, The American Statistician
2023
Later among the works it cites.
X. Wang, Y. Jin, S. Schmitt and M. Olhofer (2023), Recent advances in Bayesian optimization, ACM Computing Surveys
2023
Later among the works it cites.
K. Wu, P. Chen and O. Ghattas (2023 a
2023
Later among the works it cites.
K. Wu, T. O’Leary-Roseberry, P. Chen and O. Ghattas (2023 b
2023
Later among the works it cites.
Z. Ao and J. Li (2024), On estimating the gradient of the expected information gain in Bayesian experimental design, in Proceedings of the 38th AAAI Conference on Artificial Intelligence
2024
Closest in time.
R. Baptista, L. Cao, J. Chen, O. Ghattas, F. Li, Y. M. Marzouk and J. T. Oden (2024 a
2024
Closest in time.
J. Dong, C. Jacobsen, M. Khalloufi, M. Akramc, W. Liu, K. Duraisamy and X. Huan (2024), Variational Bayesian optimal experimental design with normalizing flows · 2024
Closest in time.
A. Eskenazis and Y. Shenfeld (2024), Intrinsic dimensional functional inequalities on model spaces, Journal of Functional Analysis
2024
Closest in time.
S. Eswar, V. Rao and A. K. Saibaba (2024), Bayesian D-optimal experimental designs via column subset selection · 2024
Closest in time.
K. Koval, R. Herzog and R. Scheichl (2024), Tractable optimal experimental design using transport maps · 2024
Closest in time.
Forthcoming
F. Li, R. Baptista and Y. Marzouk (2024 a · 2024
Closest in time.
To appear in Bernoulli
M. T. C. Li, Y. Marzouk and O. Zahm (2024 b · 2024
Closest in time.
R. Orozco, F. J. Herrmann and P. Chen (2024), Probabilistic Bayesian optimal experimental design using conditional normalizing flows · 2024
Closest in time.
D. Strutz and A. Curtis (2024), Variational Bayesian experimental design for geophysical applications: Seismic source location, amplitude versus offset inversion, and estimating CO 2 saturations in a subsurface reservoir, Geophysical Journal International
2024
Closest in time.
S. Zhong, W. Shen, T. Catanach and X. Huan (2024), Goal-oriented Bayesian optimal experimental design for nonlinear models using Markov chain Monte Carlo · 2024
Closest in time.
B. Mirzasoleiman, A. Karbasi, R. Sarkar and A. Krause (2013), Distributed submodular maximization: Identifying representative elements in massive data, in Advances in Neural Information Processing Systems 26
2057
Closest in time.