Sumantrak Mukherjee
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View article: When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity Open
Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting machine learning models to promoting algorithmic fairness. Wh…
View article: Had Enough of Experts? Quantitative Knowledge Retrieval From Large Language Models
Had Enough of Experts? Quantitative Knowledge Retrieval From Large Language Models Open
Large language models (LLMs) have been extensively studied for their ability to generate convincing natural language sequences; however, their utility for quantitative information retrieval is less well understood. Here, we explore the fea…
View article: Neural Spatiotemporal Point Processes: Trends and Challenges
Neural Spatiotemporal Point Processes: Trends and Challenges Open
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning…
View article: Graph Agnostic Causal Bayesian Optimisation
Graph Agnostic Causal Bayesian Optimisation Open
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a causal graph is fo…
View article: Had enough of experts? Quantitative knowledge retrieval from large language models
Had enough of experts? Quantitative knowledge retrieval from large language models Open
Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. Here we explore the fea…
View article: X Hacking: The Threat of Misguided AutoML
X Hacking: The Threat of Misguided AutoML Open
Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusion…
View article: Flexible Group Fairness Metrics for Survival Analysis
Flexible Group Fairness Metrics for Survival Analysis Open
Purpose : Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification ho…