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Publications

Publications

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    • Faculty Publications  (12)

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    • All HBS Web  (160)
      • Faculty Publications  (12)

      Representation LearningRemove Representation Learning →

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      • 2025
      • Working Paper

      Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

      By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
      Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details
      Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
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      Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
      • 2024
      • Working Paper

      Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization

      By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
      This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
      Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
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      Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
      • June 2024
      • Article

      Rationalizing Outcomes: Interdependent Learning in Competitive Markets

      By: Anoop R. Menon and Dennis Yao
      In this article we use simulation models to explore interdependent learning in competitive markets. Such interactions require attention to both the mental representations held by the management of the focal firm as well as the beliefs of that management about the... View Details
      Keywords: Mental Models; Strategic Interactions; Rationalization; Explanation-based View; Competition
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      Menon, Anoop R., and Dennis Yao. "Rationalizing Outcomes: Interdependent Learning in Competitive Markets." Strategy Science 9, no. 2 (June 2024): 97–117.
      • December 2023
      • Article

      Self-Orienting in Human and Machine Learning

      By: Julian De Freitas, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and T. Ullman
      A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging... View Details
      Keywords: AI and Machine Learning; Behavior; Learning
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      De Freitas, Julian, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and T. Ullman. "Self-Orienting in Human and Machine Learning." Nature Human Behaviour 7, no. 12 (December 2023): 2126–2139.
      • July 2023 (Revised July 2023)
      • Background Note

      Generative AI Value Chain

      By: Andy Wu and Matt Higgins
      Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
      Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
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      Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
      • December 2022
      • Article

      Entry Points: Gaining Momentum in Early-Stage Cross-Boundary Collaborations

      By: Eva Flavia Martínez Orbegozo, Jorrit de Jong, Hannah Riley Bowles, Amy Edmondson, Anahide Nahhal and Lisa Cox
      To address complex social challenges, it is widely recognized that leaders from public, for-profit, and civic organizations should join forces. Yet, well-intended collaborators often struggle to achieve alignment and fail to gain traction in their joint efforts. This... View Details
      Keywords: Groups and Teams; Social and Collaborative Networks; Goals and Objectives
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      Orbegozo, Eva Flavia Martínez, Jorrit de Jong, Hannah Riley Bowles, Amy Edmondson, Anahide Nahhal, and Lisa Cox. "Entry Points: Gaining Momentum in Early-Stage Cross-Boundary Collaborations." Journal of Applied Behavioral Science 58, no. 4 (December 2022): 595–645.
      • 2022
      • Working Paper

      Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments

      By: Fanglin Chen, Xiao Liu, Davide Proserpio and Isamar Troncoso
      We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s... View Details
      Keywords: Consumer Choice; Consumer Behavior; Competition; Product Marketing
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      Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. "Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments." NYU Stern School of Business Research Paper Series, July 2022.
      • 2021
      • Chapter

      Towards a Unified Framework for Fair and Stable Graph Representation Learning

      By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
      As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
      Keywords: Graph Neural Networks; AI and Machine Learning; Prejudice and Bias
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      Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
      • 2019
      • Article

      Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity

      By: Maryam Ghasemaghaei and Goran Calic
      Anecdotal evidence suggests that, despite the large variety of data, the huge volume of generated data, and the fast velocity of obtaining data (i.e., big data), quality of big data is far from perfect. Therefore, many firms defer collecting and integrating big data as... View Details
      Keywords: Big Data; Analytics and Data Science; Decisions; Quality
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      Ghasemaghaei, Maryam, and Goran Calic. "Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity." Decision Support Systems 120 (2019): 38–49.
      • March 2013
      • Case

      NovaStar Financial: A Short Seller's Battle

      By: Suraj Srinivasan and Amy Kaser
      The NovaStar case describes the challenges faced by short seller Marc Cohodes of hedge fund Rocker Partners as he tried to expose what he thought was widespread fraud in mortgage lender NovaStar Financial. The case is set in the time period from 2001 to 2007 and tracks... View Details
      Keywords: Short Selling; Financial Accounting; Financial Analysis; Financial Analysts; Valuation; Business Analysis; Financial Statement Analysis; Financial Statements; Securitization; Securities Analysis; Fraud; Accounting Quality; Accounting Red Flags; Accounting Restatements; Hedge Fund; Hedge Funds; Accounting Scandal; Accounting Fraud; Financial Crisis; Financial Intermediaries; Financial Firms; Corporate Accountability; Subprime Lending; Mortgage Lending; Accounting; Accrual Accounting; Fair Value Accounting; Governance; Governance Compliance; Corporate Governance; Governance Controls; Financial Services Industry; United States; California
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      Srinivasan, Suraj, and Amy Kaser. "NovaStar Financial: A Short Seller's Battle." Harvard Business School Case 113-120, March 2013.
      • 2012
      • Working Paper

      An Outside-Inside Evolution in Gender and Professional Work

      By: Lakshmi Ramarajan, Kathleen McGinn and Deborah Kolb
      We study the process by which a professional service firm reshaped its activities and beliefs over nearly two decades as it adapted to shifts in the social discourse regarding gender and work. Analyzing archival data from the firm over eighteen years and... View Details
      Keywords: Professional Service Firms; Social Institutions; Organizational Learning; Organizational Change and Adaptation; Employment; Gender; Society; Service Industry
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      Ramarajan, Lakshmi, Kathleen McGinn, and Deborah Kolb. "An Outside-Inside Evolution in Gender and Professional Work." Harvard Business School Working Paper, No. 13-051, November 2012. (Work in progress for requested submission, Research in Organizational Behavior.)
      • Teaching Interest

      Investing: Risk, Return and Impact (MBA)

      By: Shawn A. Cole

      This is an investing/finance course, designed to build on skills introduced in the RC finance course, but with an emphasis on how and whether investors should incorporate what have traditionally been considered “non-financial” criteria in their decisions: for... View Details

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