Search Results for author: Zachary Lipton

Found 6 papers, 2 papers with code

Uncertainty-Aware Lookahead Factor Models for Improved Quantitative Investing

no code implementations ICML 2020 Lakshay Chauhan, John Alberg, Zachary Lipton

On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, earnings, debt, etc., providing insight into the company’s financial health.

Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction

no code implementations14 Feb 2023 Tom Yan, Shantanu Gupta, Zachary Lipton

While earlier works cast these manipulations as undesirable gaming, recent works have adopted a more nuanced causal framing in which manipulations can improve outcomes of interest, and setting coherent mechanisms requires accounting for both predictive accuracy and improvement of the outcome.

Causal Discovery

The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies

1 code implementation3 Sep 2021 Riccardo Fogliato, Alexandra Chouldechova, Zachary Lipton

As algorithmic risk assessment instruments (RAIs) are increasingly adopted to assist decision makers, their predictive performance and potential to promote inequity have come under scrutiny.

How Transferable are the Representations Learned by Deep Q Agents?

no code implementations24 Feb 2020 Jacob Tyo, Zachary Lipton

In this paper, we consider the source of Deep Reinforcement Learning (DRL)'s sample complexity, asking how much derives from the requirement of learning useful representations of environment states and how much is due to the sample complexity of learning a policy.

Transfer Learning

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

1 code implementation ICLR Workshop LLD 2019 Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton

Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.

Domain Adaptation Test

BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

no code implementations15 Nov 2017 Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng

We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.

Efficient Exploration Q-Learning +4

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