Publications

SSRN

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

Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods

Research Summary

Sell-Side Analysts and Corporate Spinoffs

This study investigates the information content and accuracy of analyst reports written about companies that are about to undertake equity spinoffs. This research is among the first to provide a detailed look at the extent to which analysts evaluate upcoming... View Details

2015
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

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September 2014
Article

Advancing Consumer Neuroscience

By: Ale Smidts, Ming Hsu, Alan G. Sanfey, Maarten A. S. Boksem, Richard B. Ebstein, Scott A. Huettel, Joe W. Kable, Uma R. Karmarkar, Shinobu Kitayama, Brian Knutson, Israel Liberzon, Terry Lohrenz, Mirre Stallen and Carolyn Yoon

In the first decade of consumer neuroscience, strong progress has been made in understanding how neuroscience can inform consumer decision making. Here, we sketch the development of this discipline and compare it to that of the adjacent field of neuroeconomics. We... View Details

Keywords: Consumer Neuroscience; Neuroeconomics; Social Neuroscience; Genes; Machine Learning; Meta-analysis; Consumer Behavior; Decision Making; Science

2011
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

Article

The Similarity Heuristic

By: Daniel Read and Yael Grushka-Cockayne

Decision makers often make snap judgments using fast‐and‐frugal decision rules called cognitive heuristics. Research into cognitive heuristics has been divided into two camps. One camp has emphasized the limitations and biases produced by the heuristics; another has... View Details

Keywords: Heuristics And Biases; Fast-and-frugal Heuristics; Similarity; Representative Design

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2014
Article

The Promise of Prediction Contests

By: Phillip E. Pfeifer, Yael Grushka-Cockayne and Kenneth C. Lichtendahl

This article examines the prediction contest as a vehicle for aggregating the opinions of a crowd of experts. After proposing a general definition distinguishing prediction contests from other mechanisms for harnessing the wisdom of crowds, we focus on... View Details

Keywords: Prediction; Forecasting and Prediction

January 2008 (Revised July 2009)
Case

Forecasting the Great Depression

By: Walter A. Friedman

What is proper role of professional economic forecasting in financial decision making? The case presents excerpts from three leading economic forecasters on the eve of, and just after, the stock market crash of October 1929. The first set of excerpts is from Roger... View Details

Keywords: History; Mathematical Methods; Personal Development and Career; Forecasting and Prediction; Financial Crisis

Educators

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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

2023
Article

Post Hoc Explanations of Language Models Can Improve Language Models

By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details

Keywords: AI and Machine Learning; Performance Effectiveness