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Found 42 papers, 24 papers with code
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over.
Data Science with LLMs and Interpretable Models
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans.
Rethinking Interpretability in the Era of Large Language Models
We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.
Explaining high-dimensional text classifiers
Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions.
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
Diagnosis Uncertain Models For Medical Risk Prediction
We consider a patient risk models which has access to patient features such as vital signs, lab values, and prior history but does not have access to a patient's diagnosis.
Extending Explainable Boosting Machines to Scientific Image Data
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern.
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Missing values are a fundamental problem in data science.
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed.
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed.
Augmenting Interpretable Models with LLMs during Training
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
Differentially Private Estimation of Heterogeneous Causal Effects
Our meta-algorithm can work with simple, single-stage CATE estimators such as S-learner and more complex multi-stage estimators such as DR and R-learner.
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment.
Extracting Expert's Goals by What-if Interpretable Modeling
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed.
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy.
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.
Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
Current forecast systems have low skill in predicting one- or 2-week-average weather patterns at S2S time scales.
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks
Conversely, the probability of an interaction of $k$ variables surviving Dropout at rate $p$ is $\mathcal{O}((1-p)^k)$.
Dropout as a Regularizer of Interaction Effects
We examine Dropout through the perspective of interactions.
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
Neural Additive Models: Interpretable Machine Learning with Neural Nets
They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction.
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers.
Efficient Forward Architecture Search
We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers.
Axiomatic Interpretability for Multiclass Additive Models
In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, and show that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.
Considerations When Learning Additive Explanations for Black-Box Models
In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations.
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model.
Interpretable & Explorable Approximations of Black Box Models
To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences.
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Predictive models deployed in the real world may assign incorrect labels to instances with high confidence.
Detecting Migrating Birds at Night
In this paper, we present a vision-based system for detecting migrating birds in flight at night.
A Dual Embedding Space Model for Document Ranking
A fundamental goal of search engines is to identify, given a query, documents that have relevant text.
Blending LSTMs into CNNs
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Sparse Partially Linear Additive Models
Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly.
Do Deep Nets Really Need to be Deep?
Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision.
Using Multiple Samples to Learn Mixture Models
In the mixture models problem it is assumed that there are $K$ distributions $\theta_{1},\ldots,\theta_{K}$ and one gets to observe a sample from a mixture of these distributions with unknown coefficients.
