4 citations · 5 across the 12 of their papers we have counts for
13 papers
TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Kacper Cybiński, Björn van Zwol, James Enouen +5
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is gui…
Higher-Order Causal Structure Learning with Additive Models
James Enouen, Yujia Zheng, Ignavier Ng +2
Causal structure learning has long been the central task of inferring causal insights from data. Despite the abundance of real-world processes exhibiting higher-order mechanisms, h…
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
James Enouen, Yan Liu
In recent years, the Shapley value and SHAP explanations have emerged as one of the most dominant paradigms for providing post-hoc explanations of black-box models. Despite their w…
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Kacper Cybiński, James Enouen, Antoine Georges +1
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating…
A Complete Decomposition of KL Error using Refined Information and Mode Interaction Selection
James Enouen, Mahito Sugiyama
The log-linear model has received a significant amount of theoretical attention in previous decades and remains the fundamental tool used for learning probability distributions ove…
Discovering Car-following Dynamics from Trajectory Data through Deep Learning
Ohay Angah, James Enouen, Xuegang +2
This study aims to discover the governing mathematical expressions of car-following dynamics from trajectory data directly using deep learning techniques. We propose an expression…
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
James Enouen, Hootan Nakhost, Sayna Ebrahimi +3
Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature…
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments
James Enouen, Tianshu Sun, Yan Liu
Algorithm fairness has become a central problem for the broad adoption of artificial intelligence. Although the past decade has witnessed an explosion of excellent work studying al…
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Defu Cao, James Enouen, Yujing Wang +4
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, health…
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Defu Cao, James Enouen, Yan Liu
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making. Nevertheless, estimating treatm…
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
James Enouen, Yan Liu
There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural netwo…
Interpretable Artificial Intelligence through the Lens of Feature Interaction
Michael Tsang, James Enouen, Yan Liu
Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature repr…
What is the Perfect Shuffle?
James Enouen
When shuffling a deck of cards, one probably wants to make sure it is thoroughly shuffled. A way to do this is by sifting through the cards to ensure that no adjacent cards are the…