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Show Results For
- All HBS Web
(573)
- News (145)
- Research (212)
- Events (7)
- Multimedia (6)
- Faculty Publications (179)
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- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- 03 Oct 2017
- First Look
First Look at Research and Ideas, October 3, 2017
prospects of females. However, Edlund (1999) proposes an (as yet untested) theory that, in environments where hypergamy is practiced and parents derive utility from married children, a male-skewed sex ratio can generate a permanent female... View Details
Keywords: by Sean Silverthorne
- 05 Jun 2023
- What Do You Think?
Is the Anxious Achiever a Post-Pandemic Relic?
book. We all would like both time and money. But this generation appears, at least for now, to place a higher value on time, personal development, and lifestyle than on maximizing income. In addition, many Gen Zers have an aversion to... View Details
Keywords: by James Heskett
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 30 Nov 2021
- In Practice
What's the Role of Business in Confronting Climate Change?
The 26th annual United Nations Climate Change Conference of the Parties, also known as COP26, ended with a hard-fought pact that called on businesses and governments to meet their climate change goals faster. The event followed an August report by the Intergovernmental... View Details
Keywords: by Lynn Schenk and Dina Gerdeman
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
Keywords: Technological Innovation; AI and Machine Learning; Analytics and Data Science; Product Development; Competitive Advantage
Bojinov, Iavor, David Holtz, Ramesh Johari, Sven Schmit, and Martin Tingley. "Want Your Company to Get Better at Experimentation? Learn Fast by Democratizing Testing." Harvard Business Review 103, no. 1 (January–February 2025): 96–103.
- 16 May 2023
- In Practice
After Silicon Valley Bank's Flameout, What's Next for Entrepreneurs?
process. Today, it’s SVB, tomorrow it may be something else. While they can’t prepare for every possible scenario, founders should at least know what general processes are in place. Don’t wait for the inevitable. Julia Austin is a senior... View Details
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- 28 Feb 2018
- Sharpening Your Skills
Master the Team Meeting
monkeybusinessimages No matter how much we hate going to meetings, there’s a generally accepted best practice that teams should meet with their managers on a regular cadence. More often than not, unfortunately, I hear leaders and their... View Details
Keywords: by Julia Austin
- 11 Jan 2021
- Research & Ideas
Is A/B Testing Effective? Evidence from 35,000 Startups
percent more products. "If you’re generating more ideas, you’re more likely to generate new products." “We think there are more product introductions because when you lower the cost of testing, that makes it... View Details
Keywords: by Kristen Senz
- 22 Oct 2019
- Research & Ideas
Use Artificial Intelligence to Set Sales Targets That Motivate
handle human tasks—allows companies to use multiple variables to compute the best targets for employees, often in real time. Many companies have started using machine-learning algorithms to construct AI systems. Such algorithms can take... View Details
Keywords: by Michael Blanding
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- 25 Jun 2024
- Research & Ideas
How Transparency Sped Innovation in a $13 Billion Wireless Sector
products. But, in general, I would have to say the more partners, the better.” You Might Also Like: Open Source Software: The $9 Trillion Resource Companies Take for Granted How to Make AI 'Forget' All the Private Data It Shouldn't Have... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia, 217–238. Review of Marketing Research. Emerald Publishing Limited, 2023.
- 09 Apr 2024
- Research & Ideas
When Climate Goals, Housing Policy, and Corporate R&D Collide, Social Good Can Emerge
For almost four years, Omar Asensio and his colleagues have been studying the impact of federal energy programs on low-income neighborhoods. The intersection of technology—artificial intelligence, in particular—and public policy has long been an area of focus for... View Details
Keywords: by Glen Justice
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 12 May 2009
- First Look
First Look: May 12, 2009
work together to achieve better results and include the recipients in the process? Purchase this note: http://hbsp.harvard.edu/b01/en/common/item_detail.jhtml?id=909406 Generation Investment Management Harvard Business School Case 609-057... View Details
Keywords: Martha Lagace