Search Results for author:
Found 10 papers, 1 papers with code
Ranking Large Language Models without Ground Truth
and a set of LLMs, we rank them without access to any ground truth or reference responses.
Explaining Knock-on Effects of Bias Mitigation
In this paper, we aim to characterise impacted cohorts when mitigation interventions are applied.
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration.
Leveraging Explanations in Interactive Machine Learning: An Overview
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 model of a trained ML model.
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context.
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
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, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.
User Profiling from Reviews for Accurate Time-Based Recommendations
However, temporal aspects of a user profile may not always be explicitly available and so we may need to infer this information from available resources.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
Know Who Your Friends Are: Understanding Social Connections from Unstructured Text
Having an understanding of interpersonal relationships is helpful in many contexts.
