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Show Results For
- All HBS Web
(2,779)
- People (14)
- News (625)
- Research (1,480)
- Events (14)
- Multimedia (9)
- Faculty Publications (744)
- 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
- 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
- 08 Dec 2016
- News
A Guide to Solving Social Problems with Machine Learning
- Web
1.4.1 HBS Learning Model - MBA
1.4 Academic Program Specifics 1.4.1 HBS Learning Model Welcome to HBS Being a Student at HBS 1. Academic Information & Policies 1.1 HBS Community Values 1.2 MBA Honor Code 1.3 Academic Calendar 1.4 Academic... View Details
- 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.)
- 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.
- 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).
- 17 Jan 2020
- News
AB InBev Taps Machine Learning to Root Out Corruption
- 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).
- Web
Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) - Research Computing Services
Software Tools Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) 6ms The HBSGrid offers artificial intelligence(AI) and machine View Details
- 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.
- 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).
- 04 Oct 2019
- Working Paper Summaries
Soul and Machine (Learning)
- 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.
- November 2007
- Article
A Model of Consumer Learning for Service Quality and Usage
By: Raghuram Iyengar, Asim Ansari and Sunil Gupta
In many services, e.g., the wireless service industry, consumers choose a service plan based on their expected consumption. In such situations, consumers experience two forms of uncertainty. First, consumers may be uncertain about the quality of their service provider... View Details
Keywords: Experience and Expertise; Customer Value and Value Chain; Learning; Price; Knowledge Use and Leverage; Marketing Strategy; Consumer Behavior; Service Delivery; Quality; Risk and Uncertainty; Service Industry
Iyengar, Raghuram, Asim Ansari, and Sunil Gupta. "A Model of Consumer Learning for Service Quality and Usage." Journal of Marketing Research (JMR) 44, no. 4 (November 2007): 529–544.
- 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 2018 (Revised February 2019)
- Supplement
Improving Worker Safety in the Era of Machine Learning (B)
By: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson
Supplements the (A) case. View Details
Toffel, Michael W., Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (B)." Harvard Business School Supplement 618-064, April 2018. (Revised February 2019.)
- Forthcoming
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics (forthcoming). (Pre-published online July 8, 2024.)
- November 25, 2016
- Article
How to Tell If Machine Learning Can Solve Your Business Problem
By: Anastassia Fedyk
Fedyk, Anastassia. "How to Tell If Machine Learning Can Solve Your Business Problem." Harvard Business Review (website) (November 25, 2016).