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Found 107 papers, 54 papers with code
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive.
What is different between these datasets?
The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges.
Sparse and Faithful Explanations Without Sparse Models
In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied.
Optimal Sparse Survival Trees
Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health.
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors.
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics.
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses.
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Both of these have disadvantages: black box models are unacceptable for use in hospitals, whereas manual creation of models (including hand-tuning of logistic regression parameters) relies on humans to perform high-dimensional constrained optimization, which leads to a loss in performance.
Safe and Interpretable Estimation of Optimal Treatment Regimes
Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes.
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals.
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
However, for a given dataset, there may be many models that explain the target outcome equally well; without accounting for all possible explanations, different researchers may arrive at many conflicting yet equally valid conclusions given the same data.
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables
Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health.
Learned Kernels for Sparse, Interpretable, and Efficient Medical Time Series Processing
Results: Our interpretable method achieves greater than 99% of the performance of the state-of-the-art methods on the PPG artifact detection task, and even outperforms the state-of-the-art on a challenging out-of-distribution test set, while using dramatically fewer parameters (2% of the parameters of Segade, and about half of the parameters of Tiny-PPG).
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions.
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Missing values are a fundamental problem in data science.
OKRidge: Scalable Optimal k-Sparse Ridge Regression
We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems.
Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics
We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference.
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a single model does not facilitate such interaction.
Variable Importance Matching for Causal Inference
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data.
Optimal Sparse Regression Trees
Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications.
Interpretable Machine Learning System to EEG Patterns on the Ictal-Interictal-Injury Continuum
To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions.
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8. 5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets.
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used.
FasterRisk: Fast and Accurate Interpretable Risk Scores
Specifically, our approach produces a pool of almost-optimal sparse continuous solutions, each with a different support set, using a beam-search algorithm.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Given thousands of equally accurate machine learning (ML) models, how can users choose among them?
Exploring the Whole Rashomon Set of Sparse Decision Trees
We show three applications of the Rashomon set: 1) it can be used to study variable importance for the set of almost-optimal trees (as opposed to a single tree), 2) the Rashomon set for accuracy enables enumeration of the Rashomon sets for balanced accuracy and F1-score, and 3) the Rashomon set for a full dataset can be used to produce Rashomon sets constructed with only subsets of the data set.
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes
Interpretability is an essential building block for trustworthiness in reinforcement learning systems.
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation
Experimental results provide evidence that our method can discover multiple concepts within a single image and outperforms state-of-the-art unsupervised methods on complex datasets such as Cityscapes and COCO-Stuff.
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.
Fast Sparse Classification for Generalized Linear and Additive Models
For fast sparse logistic regression, our computational speed-up over other best-subset search techniques owes to linear and quadratic surrogate cuts for the logistic loss that allow us to efficiently screen features for elimination, as well as use of a priority queue that favors a more uniform exploration of features.
Fast Sparse Decision Tree Optimization via Reference Ensembles
We show that by using these guesses, we can reduce the run time by multiple orders of magnitude, while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power.
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems.
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales
Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists.
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
Compared to other methods, our model detects clinical features (mass margins) with equal or higher accuracy, provides a more detailed explanation of its prediction, and is better able to differentiate the classification-relevant parts of the image.
Multitask Learning for Citation Purpose Classification
The goal of the competition is to classify a citation in a scientific article based on its purpose.
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.
Playing Codenames with Language Graphs and Word Embeddings
Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored.
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization
We then consider the broader ethical issues involved.
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present, and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone.
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It
Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter.
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce.
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning
Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution.
Bandits for BMO Functions
We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function.
A Transformer Approach to Contextual Sarcasm Detection in Twitter
Understanding tone in Twitter posts will be increasingly important as more and more communication moves online.
Metaphor Detection Using Contextual Word Embeddings From Transformers
The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation.
Generalized and Scalable Optimal Sparse Decision Trees
Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning.
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
We compared predictive performance and fairness of these models against two methods that are currently used in the justice system to predict pretrial recidivism: the Arnold PSA and COMPAS.
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
Ranked #10 on
Image Super-Resolution
on FFHQ 256 x 256 - 4x upscaling
(PSNR metric)
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes.
Concept Whitening for Interpretable Image Recognition
What does a neural network encode about a concept as we traverse through the layers?
On the Existence of Simpler Machine Learning Models
We hypothesize that there is an important reason that simple-yet-accurate models often do exist.
Reducing Exploration of Dying Arms in Mortal Bandits
Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the set of available options changes over time.
Interpretable Almost-Matching-Exactly With Instrumental Variables
Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i. e., the presence of unobserved covariates linking treatments and outcomes.
Interpretable Image Recognition with Hierarchical Prototypes
Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy.
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
9) There is a method to the madness of deep neural architectures, but not always.
Optimal Sparse Decision Trees
Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's.
Towards Practical Lipschitz Bandits
Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains.
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains.
A robust approach to quantifying uncertainty in matching problems of causal inference
However, as we show in this work, there is a typical source of uncertainty that is essentially never considered in observational causal studies: the choice of match assignment for matched groups, that is, which unit is matched to which other unit before a hypothesis test is conducted.
Methodology
An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains.
MALTS: Matching After Learning to Stretch
In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches.
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation
We provide an approach for generating beautiful poetry.
Bayesian Patchworks: An Approach to Case-Based Reasoning
Doctors often rely on their past experience in order to diagnose patients.
This Looks Like That: Deep Learning for Interpretable Image Recognition
In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification.
Interpretable Almost Matching Exactly for Causal Inference
Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling.
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data.
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes.
Direct Learning to Rank and Rerank
Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items.
Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective
Expanding on MR, we propose Model Class Reliance (MCR) as the upper and lower bounds on the degree to which any well-performing prediction model within a class may rely on a variable of interest, or set of variables of interest.
Methodology
Extreme Dimension Reduction for Handling Covariate Shift
In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor's average loss over the training and test distributions also differ.
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}.
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input.
An Optimization Approach to Learning Falling Rule Lists
A falling rule list is a probabilistic decision list for binary classification, consisting of a series of if-then rules with antecedents in the if clauses and probabilities of the desired outcome ("1") in the then clauses.
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.
Learning Certifiably Optimal Rule Lists for Categorical Data
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
We formulate this as a problem of learning a decision list -- a sequence of if-then-else rules -- which maps characteristics of subjects (eg., diagnostic test results of patients) to treatments.
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
We formulate this as a problem of learning a decision list -- a sequence of if-then-else rules -- which maps characteristics of subjects (eg., diagnostic test results of patients) to treatments.
Learning Optimized Risk Scores
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers.
Interpretable Machine Learning Models for the Digital Clock Drawing Test
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions.
Scalable Bayesian Rule Lists
They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree.
Learning Optimized Or's of And's
Or's of And's (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form.
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
We present sparse tree-based and list-based density estimation methods for binary/categorical data.
Causal Falling Rule Lists
A causal falling rule list (CFRL) is a sequence of if-then rules that specifies heterogeneous treatment effects, where (i) the order of rules determines the treatment effect subgroup a subject belongs to, and (ii) the treatment effect decreases monotonically down the list.
CRAFT: ClusteR-specific Assorted Feature selecTion
We present a framework for clustering with cluster-specific feature selection.
Regulating Greed Over Time in Multi-Armed Bandits
In the corrected methods, exploitation (greed) is regulated over time, so that more exploitation occurs during higher reward periods, and more exploration occurs in periods of low reward.
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems
In both cases, there are prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability.
Modeling Recovery Curves With Application to Prostatectomy
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event.
Interpretable Classification Models for Recidivism Prediction
We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making.
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering.
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction.
Falling Rule Lists
Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list.
Robust Optimization using Machine Learning for Uncertainty Sets
Our goal is to build robust optimization problems for making decisions based on complex data from the past.
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge
In this paper, we consider a supervised learning setting where side knowledge is provided about the labels of unlabeled examples.
Methods and Models for Interpretable Linear Classification
We present an integer programming framework to build accurate and interpretable discrete linear classification models.
Box Drawings for Learning with Imbalanced Data
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes.
A Statistical Learning Theory Framework for Supervised Pattern Discovery
This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pattern.''
Supersparse Linear Integer Models for Interpretable Classification
We illustrate the practical and interpretable nature of SLIM scoring systems through applications in medicine and criminology, and show that they are are accurate and sparse in comparison to state-of-the-art classification models using numerical experiments.
Supersparse Linear Integer Models for Predictive Scoring Systems
We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification.
Machine Learning with Operational Costs
This work proposes a way to align statistical modeling with decision making.
On Combining Machine Learning with Decision Making
We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory.
