2.1k citations · 6.2k across the 128 of their papers we have counts for
12 papers · 1 filter
Online Data Collection for Efficient Semiparametric Inference
Shantanu Gupta, Zachary C. Lipton, David Childers
While many works have studied statistical data fusion, they typically assume that the various datasets are given in advance. However, in practice, estimation requires difficult dat…
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen +3
This paper focuses on supervised and unsupervised online label shift, where the class marginals varies but the class-conditionals remain invariant. In the unsupervi…
Supervised Learning with General Risk Functionals
Liu Leqi, Audrey Huang, Zachary C. Lipton +1
Standard uniform convergence results bound the generalization gap of the expected loss over a hypothesis class. The emergence of risk-sensitive learning requires generalization gua…
Causal Inference With Selectively Deconfounded Data
Kyra Gan, Andrew A. Li, Zachary C. Lipton +1
Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner mu…
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on prope…
Troubling Trends in Machine Learning Scholarship
Zachary C. Lipton, Jacob Steinhardt
Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire…
Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen +2
Knowledge Distillation (KD) consists of transferring “knowledge†from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacit…
The Doctor Just Won't Accept That!
Zachary C. Lipton
Calls to arms to build interpretable models express a well-founded discomfort with machine learning. Should a software agent that does not even know what a loan is decide who quali…
Does mitigating ML's impact disparity require treatment disparity?
Zachary C. Lipton, Alexandra Chouldechova, Julian McAuley
Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making. Algorithms exhibit treatment disparity i…
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
John Alberg, Zachary C. Lipton
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide…
Predicting Surgery Duration with Neural Heteroscedastic Regression
Nathan Ng, Rodney A Gabriel, Julian McAuley +2
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking. W…
Thresholding Classifiers to Maximize F1 Score
Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy
This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precisi…