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Show Results For
- All HBS Web
(958)
- People (1)
- News (155)
- Research (640)
- Events (13)
- Multimedia (3)
- Faculty Publications (539)
- February 2024
- Teaching Note
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to... View Details
- 2015
- Article
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes
By: Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani and Kecia Addison
Lakkaraju, Himabindu, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, and Kecia Addison. "A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 21st (2015).
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- February 2018 (Revised June 2021)
- Case
New Constructs: Disrupting Fundamental Analysis with Robo-Analysts
By: Charles C.Y. Wang and Kyle Thomas
This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions,... View Details
Keywords: Fundamental Analysis; Machine Learning; Robo-analysts; Financial Statements; Financial Reporting; Analysis; Information Technology; Accounting Industry; Financial Services Industry; Information Technology Industry; North America; Tennessee
Wang, Charles C.Y., and Kyle Thomas. "New Constructs: Disrupting Fundamental Analysis with Robo-Analysts." Harvard Business School Case 118-068, February 2018. (Revised June 2021.)
- June 2016 (Revised August 2019)
- Case
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie, Liz Kind and David Ben Shimol
In March 2016, Donna Dubinsky (co-founder and CEO) and Jeff Hawkins (co-founder) were struggling with a key question: Could Numenta be successful in both creating fundamental technology and building a commercial business? Located in Redwood City, CA, Numenta was... View Details
Keywords: Artificial Intelligence; Machine Intelligence; Machine Learning; Strategy; Business Model; Entrepreneurship; Information; Technological Innovation; Research; Research and Development; Information Technology; Applications and Software; Technology Adoption; Digital Platforms; Commercialization; AI and Machine Learning
Yoffie, David B., Liz Kind, and David Ben Shimol. "Numenta: Inventing and (or) Commercializing AI." Harvard Business School Case 716-469, June 2016. (Revised August 2019.)
- Article
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
- 2021
- Conference Presentation
An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.... View Details
Jung, Christopher, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton, and Zhiwei Steven Wu. "An Algorithmic Framework for Fairness Elicitation." Paper presented at the 2nd Symposium on Foundations of Responsible Computing (FORC), 2021.
- Article
Robust and Stable Black Box Explanations
By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there
has been a growing interest in developing post hoc explanations that summarize the behaviors
of these black boxes. However, existing algorithms for generating such... View Details
Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
- February 2019
- Case
Miroglio Fashion (A)
By: Sunil Gupta and David Lane
Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can... View Details
Keywords: Inventory Management; Demand Forecasting; Artificial Intelligence; Machine Learning; Forecasting and Prediction; Operations; Management; Decision Making; AI and Machine Learning; Apparel and Accessories Industry; Fashion Industry
Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
- 29 Sep 2020
Life at HBS Chat Series: MBA Students in Tech Club and Coding, Analytics, and Machine Learning Club
Hear straight from current HBS students regarding their MBA experience. Students will share their backgrounds and how they have cultivated their personal and professional interests while at HBS. View Details
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines: Applications to Ridesharing CRM." Journal of Marketing Research (JMR) 61, no. 2 (April 2024): 368–392.
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- 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
- 18 Apr 2000
- Research & Ideas
Learning in Action
"The most effective learning strategy depends on the situation," writes David A. Garvin. "There is no stock answer, nor is there a single best approach." In Learning in Action, he illustrated the diversity... View Details
Keywords: by David A. Garvin
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- 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).
- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details
Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- 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.