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Show Results For
- All HBS Web
(2,879)
- People (14)
- News (652)
- Research (1,599)
- Events (19)
- Multimedia (9)
- Faculty Publications (872)
- 2024
- Working Paper
Sharing Models to Interpret Data
By: Joshua Schwartzstein and Adi Sunderam
To understand new data, we share models or interpretations with others. This paper studies such exchanges of models in a community. The key assumption is that people adopt the interpretation in their community that best explains the data, given their prior beliefs. An... View Details
Keywords: Social Learning Theory; Theory; Social Issues; Cognition and Thinking; Social and Collaborative Networks; Attitudes
Schwartzstein, Joshua, and Adi Sunderam. "Sharing Models to Interpret Data." Harvard Business School Working Paper, No. 25-011, August 2024. (Revised August 2024.)
- Web
2.2.1 HBS Learning Model | MBA
2.2.1 HBS Learning Model 2.2 Academic Program Specifics The mission of the HBS MBA Program is to educate leaders who make a difference in the world. The education of these leaders occurs in a community... View Details
- 2018
- Working Paper
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
By: Xiaojia Guo, Yael Grushka-Cockayne and Bert De Reyck
Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces... View Details
Keywords: Quantile Forecasts; Regression Tree; Copula; Passenger Flow Management; Data-driven Operations; Forecasting and Prediction; Data and Data Sets
Guo, Xiaojia, Yael Grushka-Cockayne, and Bert De Reyck. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Harvard Business School Working Paper, No. 19-040, October 2018.
- December 2019
- Article
Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning
By: Guofang Huang, Hong Luo and Jing Xia
Pricing idiosyncratic products is often challenging because the seller, ex ante, lacks information about the demand for individual items. This paper develops a model of dynamic pricing for idiosyncratic products that features the optimal stopping structure and a seller... View Details
Keywords: Dynamic Pricing; Idiosyncratic Products; Item-specific Demand; Demand Uncertainty; Active Seller Learning; The Value Of Information; Price; Information; Value; Learning
Huang, Guofang, Hong Luo, and Jing Xia. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning." Management Science 65, no. 12 (December 2019): 5556–5583.
- 21 Aug 2019
- Research & Ideas
What Machine Learning Teaches Us about CEO Leadership Style
CEOs are communicators. Studies show that CEOs spend 85 percent of their time in communication-related activities, including speeches, meetings, and phone calls with people both inside and outside the firm. Now, new research using machine... View Details
Keywords: by Michael Blanding
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 2020
- Conference Presentation
Learning World Models with Progress-driven Exploration
By: K-H Kim, M. Sano, J. De Freitas, N. Haber and D. L. K. Yamins
- Article
Counterfactual Explanations Can Be Manipulated
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate... View Details
Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- 08 Dec 2016
- News
A Guide to Solving Social Problems with Machine Learning
- 17 Jan 2020
- News
AB InBev Taps Machine Learning to Root Out Corruption
- Article
Productivity and Selection of Human Capital with Machine Learning
By: Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig and Sendhil Mullainathan
Keywords: Analytics and Data Science; Selection and Staffing; Performance Productivity; Mathematical Methods; Policy
Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127.
- 04 Oct 2019
- Working Paper Summaries
Soul and Machine (Learning)
- 2020
- Conference Presentation
Active World Model Learning with Progress-driven Exploration
By: K-H Kim, M. Sano, J. De Freitas, N. Haber and D. L. K. Yamins
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- 14 Mar 2023
- News
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- Web
Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) - Research Computing Services
Software Tools Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) 2ms The HBSGrid offers artificial intelligence(AI) and machine View Details
- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.