A Robust Utility Learning Framework via Inverse Optimization
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Ming Jin | Costas J. Spanos | S. Shankar Sastry | Lillian J. Ratliff | Ioannis C. Konstantakopoulos | S. Sastry | Ming Jin | C. Spanos | L. Ratliff | S. Sastry
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] J. Goodman. Note on Existence and Uniqueness of Equilibrium Points for Concave N-Person Games , 1965 .
[3] R. Aumann. Subjectivity and Correlation in Randomized Strategies , 1974 .
[4] A. Tversky,et al. The framing of decisions and the psychology of choice. , 1981, Science.
[5] M. Gendreau. On the location of eigenvalues of off-diagonal constant matrices , 1986 .
[6] John H. Sheesley,et al. Quality Engineering in Production Systems , 1988 .
[7] S. Flåm. Solving non-cooperative games by continuous subgradient projection methods , 1990 .
[8] Robert Tibshirani,et al. Model Search and Inference By Bootstrap "bumping , 1995 .
[9] Steven T. Berry,et al. Automobile Prices in Market Equilibrium , 1995 .
[10] Clifford M. Hurvich,et al. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion , 1998 .
[11] J. Laitner. Energy efficiency: rebounding to a sound analytical perspective , 2000 .
[12] L. Schipper,et al. On the rebound? Feedback between energy intensities and energy uses in IEA countries , 2000 .
[13] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[14] Kevin Barraclough,et al. I and i , 2001, BMJ : British Medical Journal.
[15] J. Laffont,et al. The Theory of Incentives: The Principal-Agent Model , 2001 .
[16] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[17] Francisco Cribari-Neto,et al. Asymptotic inference under heteroskedasticity of unknown form , 2004, Comput. Stat. Data Anal..
[18] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[19] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .
[20] Magnus Bång,et al. The PowerHhouse: A Persuasive Computer Game Designed to Raise Awareness of Domestic Energy Consumption , 2006, PERSUASIVE.
[21] Christoph F. Reinhart,et al. Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control , 2006 .
[22] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[23] T. Mulgan. The Contract Theory , 2006 .
[24] M. Boman,et al. Energy Saving and Added Customer Value in Intelligent Buildings , 2007 .
[25] David E. Culler,et al. Design and implementation of a high-fidelity AC metering network , 2009, 2009 International Conference on Information Processing in Sensor Networks.
[26] Y. Narahari,et al. Game Theoretic Problems in Network Economics and Mechanism Design Solutions , 2009, Advanced Information and Knowledge Processing.
[27] Rolf Isermann,et al. Identification of Dynamic Systems: An Introduction with Applications , 2010 .
[28] Andrew Ledvina,et al. Dynamic Bertrand Oligopoly , 2010, 1004.1726.
[29] Stephen P. Boyd,et al. Imputing a convex objective function , 2011, 2011 IEEE International Symposium on Intelligent Control.
[30] David E. Culler,et al. Identifying models of HVAC systems using semiparametric regression , 2012, 2012 American Control Conference (ACC).
[31] M. Jahn,et al. Saving energy at work: the design of a pervasive game for office spaces , 2012, MUM.
[32] Claire J. Tomlin,et al. Incentive design for efficient building quality of service , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[33] Henrik Ohlsson,et al. Incentive Design and Utility Learning via Energy Disaggregation , 2013, 1312.1394.
[34] Ming Jin,et al. Social game for building energy efficiency: Incentive design , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[35] MengChu Zhou,et al. Social incentive policies to engage commercial building occupants in demand response , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).
[36] PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring , 2014, 1407.4395.
[37] Ming Jin,et al. PresenceSense: zero-training algorithm for individual presence detection based on power monitoring , 2014, BuildSys@SenSys.
[38] Lillian J. Ratliff,et al. Incentivizing Efficiency in Societal-Scale Cyber-Physical Systems , 2015 .
[39] Ming Jin,et al. Sensing by Proxy : Occupancy Detection Based on Indoor CO 2 Concentration , 2015 .
[40] Ming Jin,et al. REST: a reliable estimation of stopping time algorithm for social game experiments , 2015, ICCPS.
[41] Vishal Gupta,et al. Data-driven estimation in equilibrium using inverse optimization , 2013, Mathematical Programming.
[42] S. Shankar Sastry,et al. On the Characterization of Local Nash Equilibria in Continuous Games , 2014, IEEE Transactions on Automatic Control.
[43] Ming Jin,et al. Inverse modeling of non-cooperative agents via mixture of utilities , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).
[44] Ming Jin,et al. Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence , 2017, IEEE Transactions on Mobile Computing.
[45] Ming Jin,et al. MOD-DR: Microgrid optimal dispatch with demand response , 2017 .
[46] Kevin Weekly,et al. Occupancy Detection via Environmental Sensing , 2018, IEEE Transactions on Automation Science and Engineering.
[47] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .