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Found 6 papers, 2 papers with code
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.
How Transferable are the Representations Learned by Deep Q Agents?
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.
Uncertainty-Aware Lookahead Factor Models for Improved Quantitative Investing
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.
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies
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.
Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction
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.
