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Show Results For
- All HBS Web
(2,853)
- People (14)
- News (652)
- Research (1,576)
- Events (19)
- Multimedia (9)
- Faculty Publications (845)
- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.
- 23 Oct 2020
- News
Now Is the Time to Shake Up Your Sales Processes
- 01 Mar 2009
- News
Model Patient
as a national blueprint. Near-universal coverage, a pipe dream anywhere in the United States a few short years ago, is the chief reason that the state’s model has generated so much excitement. And the Bay State’s example has arguably put... View Details
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- June 2021
- Case
Acelero Learning
By: Mario Small, Kathleen L. McGinn, Amy Klopfenstein and Katherine Chen
In November 2020, Henry Wilde, co-founder and CEO of Acelero, Inc., must decide whether to change his company’s program model for delivering early childhood education to low-income children. One of the only for-profit Head Start providers in the United States, Acelero... View Details
Keywords: Early Childhood Education; Organizational Change and Adaptation; Growth and Development Strategy; Adoption; Customer Focus and Relationships; Operations; Education Industry; North and Central America; United States
Small, Mario, Kathleen L. McGinn, Amy Klopfenstein, and Katherine Chen. "Acelero Learning." Harvard Business School Case 921-029, June 2021.
- Research Summary
Selective Attention and Learning
What do we notice, and how does this affect what we learn? Standard economic models of learning ignore memory by assuming that we remember everything. But there is growing recognition that memory is imperfect. Further, memory imperfections do not stem from limited... View Details
- May 1999
- Article
The Effect of Adding a Constant to All Payoffs: Experimental Investigation, and a Reinforcement Learning Model with Self-Adjusting Speed of Learning
By: Ido Erev, Yoella Bereby-Meyer and Alvin E. Roth
Erev, Ido, Yoella Bereby-Meyer, and Alvin E. Roth. "The Effect of Adding a Constant to All Payoffs: Experimental Investigation, and a Reinforcement Learning Model with Self-Adjusting Speed of Learning." Journal of Economic Behavior & Organization 39, no. 1 (May 1999): 111–128.
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- April 2017
- Case
The Future of Patent Examination at the USPTO
By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta
The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April... View Details
Keywords: Machine Learning; Telework; Collaborating With Unions; Human Resources; Recruitment; Retention; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; Organizational Change and Adaptation; Performance Productivity; Performance Improvement; District of Columbia
Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. "The Future of Patent Examination at the USPTO." Harvard Business School Case 617-027, April 2017.
- December 1, 2021
- Article
Do You Know How Your Teams Get Work Done?
By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital... View Details
Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation
Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
- September 2009
- Article
A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement
By: Matthew Carty MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow and Dennis Orgill
Background: The increased focus on quality and efficiency improvement within academic surgery has met with variable success among plastic surgeons. Traditional surgical performance metrics, such as morbidity and mortality, are insufficient to improve the... View Details
Keywords: Experience and Expertise; Health Care and Treatment; Medical Specialties; Outcome or Result; Performance Efficiency; Performance Improvement
Carty, Matthew, MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow, and Dennis Orgill. "A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement." Plastic and Reconstructive Surgery 124, no. 3 (September 2009): 706–714.
- 07 Jan 2020
- News
Models of Success
lessons she learned on the basketball court—drive, endurance, teamwork—served her well in the business world, too. But not all of her college teammates and other friends found the same success after sports. “These were the most... View Details
- 2006
- Conference Paper
Modeling Repeated Play of the Prisoners' Dilemma with Reinforcement Learning over an Enriched Strategy Set
By: A. E. Roth and Ido Erev
- November 2022
- Article
A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups
By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining... View Details
Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture
Bhatt, Anjali M., Amir Goldberg, and Sameer B. Srivastava. "A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups." Sociological Methods & Research 51, no. 4 (November 2022): 1681–1720.
- Forthcoming
- Article
Beefing IT Up for Your Investor? Engagement with Open Source Communities, Innovation, and Startup Funding: Evidence from GitHub
By: Annamaria Conti, Christian Peukert and Maria P. Roche
We study the engagement of nascent firms with open source communities and its implications for innovation and attracting funding. To do so, we link data on 160,065 U.S. startups from Crunchbase to their activities on the open source software development platform... View Details
Keywords: Startups; Knowledge; Open Source Communities; GitHub; Machine Learning; Innovation; Business Startups; Venture Capital; Information Technology; Strategy
Conti, Annamaria, Christian Peukert, and Maria P. Roche. "Beefing IT Up for Your Investor? Engagement with Open Source Communities, Innovation, and Startup Funding: Evidence from GitHub." Organization Science (forthcoming). (Pre-published online March 7, 2025.)
- Article
Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time
By: Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison and Rayid Ghani
Aguiar, Everaldo, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, and Rayid Ghani. "Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time." Proceedings of the International Learning Analytics and Knowledge Conference 5th (2015).
- July 2019 (Revised November 2019)
- Case
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms... View Details
Keywords: Artificial Intelligence; Machine Learning; Robotics; Robots; Ecommerce; Fulfillment; Warehousing; AI; Startup; Technology Commercialization; Business Startups; Entrepreneurship; Logistics; Order Taking and Fulfillment; Information Technology; Commercialization; Learning; Complexity; Competition; E-commerce
Kerr, William R., James Palano, and Bastiane Huang. "Osaro: Picking the Best Path." Harvard Business School Case 820-012, July 2019. (Revised November 2019.)
- 2025
- Working Paper
How to Choose Among Technologies with Learning Curves: Making Better Investment Decisions
By: Christian Kaps and Arielle Anderer
Learning curves, the fact that technologies improve as a function of cumulative experience or investment, are desirable-think inexpensive solar panels or higher performing semiconductors. But, for firms that need to pick one technology among several candidates, such as... View Details
Keywords: Learning Curve; Technology; Innovation; Batteries; Energy Storage; Sequential Decision Making; TELCO; Exploration; Exploitation; Problems and Challenges; Cost vs Benefits; Technology Adoption; Battery Industry
Kaps, Christian, and Arielle Anderer. "How to Choose Among Technologies with Learning Curves: Making Better Investment Decisions." Working Paper, March 2025.
- October 2023 (Revised June 2024)
- Case
ReUp Education: Can AI Help Learners Return to College?
By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details
Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States
Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023. (Revised June 2024.)