10 citations · 24 across the 9 of their papers we have counts for
10 papers
Policy-as-logic for robust reasoning over rules
Rahul Nair, Bastian Lipka, Elizabeth Daly
In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect written policies or rules.…
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Jasmina Gajcin, James McCarthy, Rahul Nair +3
A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging…
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
AutoDOViz: Human-Centered Automation for Decision Optimization
Daniel Karl I. Weidele, Shazia Afzal, Abel N. Valente +12
We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being…
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
James McCarthy, Rahul Nair, Elizabeth Daly +2
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and…
Leveraging Explanations in Interactive Machine Learning: An Overview
Stefano Teso, Öznur Alkan, Wolfang Stammer +1
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental mod…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati +3
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, b…
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu +2
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements. Having an elegant way to…