1 code implementation • 28 Oct 2024 • Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen

We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning.

no code implementations • 27 Oct 2024 • Yiyang Sun, Tong Wang, Cynthia Rudin

We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models.

no code implementations • 24 Oct 2024 • Jiachang Liu, Rui Zhang, Cynthia Rudin

Survival analysis is an important research topic with applications in healthcare, business, and manufacturing.

no code implementations • 15 Aug 2024 • Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin, L. Catherine Brinson

Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.

no code implementations • 13 Aug 2024 • Stephen Ni-Hahn, Weihan Xu, Jerry Yin, Rico Zhu, Simon Mak, Yue Jiang, Cynthia Rudin

Schenkerian Analysis (SchA) is a uniquely expressive method of music analysis, combining elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music.

no code implementations • 5 Jul 2024 • Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset.

no code implementations • 20 Jun 2024 • Frank Willard, Luke Moffett, Emmanuel Mokel, Jon Donnelly, Stark Guo, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin

Prototypical-part models are a popular interpretable alternative to black-box deep learning models for computer vision.

no code implementations • 10 Jun 2024 • Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin

In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models.

1 code implementation • 26 Apr 2024 • Cheng Ding, Zhicheng Guo, Zhaoliang Chen, Randall J Lee, Cynthia Rudin, Xiao Hu

However, large foundation models are typically trained on high-quality data, which poses a significant challenge, given the prevalence of poor-quality real-world data.

no code implementations • 6 Apr 2024 • Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju

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.

no code implementations • 8 Mar 2024 • Varun Babbar, Zhicheng Guo, Cynthia Rudin

The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges.

no code implementations • 15 Feb 2024 • Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin

In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied.

1 code implementation • 27 Jan 2024 • Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health.

1 code implementation • 17 Dec 2023 • Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors.

no code implementations • 4 Dec 2023 • Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B Nahab, Xiao Hu

This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics.

no code implementations • 3 Dec 2023 • Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, Brandon Westover

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.

1 code implementation • 21 Nov 2023 • Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin

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.

1 code implementation • 23 Oct 2023 • Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky

Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes.

1 code implementation • 13 Oct 2023 • Zhicheng Guo, Cheng Ding, Duc H. Do, Amit Shah, Randall J. Lee, Xiao Hu, Cynthia Rudin

Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals.

1 code implementation • NeurIPS 2023 • Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne

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.

no code implementations • 7 Jul 2023 • Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health.

1 code implementation • 6 Jul 2023 • Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin

Rapid, reliable, and accurate interpretation of medical time-series signals is crucial for high-stakes clinical decision-making.

no code implementations • 4 Jul 2023 • Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Experimental and observational studies often lack validity due to untestable assumptions.

no code implementations • 23 Apr 2023 • Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana

Missing values are a fundamental problem in data science.

1 code implementation • NeurIPS 2023 • Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin

We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems.

no code implementations • 3 Apr 2023 • Marco Morucci, Cynthia Rudin, Alexander Volfovsky

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.

1 code implementation • NeurIPS 2023 • Chudi Zhong, Zhi Chen, Jiachang Liu, Margo Seltzer, Cynthia Rudin

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.

1 code implementation • 23 Feb 2023 • Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

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.

2 code implementations • 28 Nov 2022 • Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

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.

1 code implementation • 9 Nov 2022 • Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Peter W. Kaplan, Wan Yee Kong, Ioannis Karakis, Aline Herlopian, Lakshman Arcot Jayagopal, Olga Taraschenko, Olga Selioutski, Gamaleldin Osman, Daniel Goldenholz, Cynthia Rudin, M. Brandon Westover

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.

1 code implementation • 7 Nov 2022 • Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Amit Shah, Duc H. Do, Randall J Lee, Gari Clifford, Fadi B Nahab, Xiao Hu

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.

no code implementations • 13 Oct 2022 • Ali Behrouz, Mathias Lecuyer, Cynthia Rudin, Margo Seltzer

Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used.

1 code implementation • 12 Oct 2022 • Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin

Specifically, our approach produces a pool of almost-optimal sparse continuous solutions, each with a different support set, using a beam-search algorithm.

3 code implementations • 19 Sep 2022 • Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer

Given thousands of equally accurate machine learning (ML) models, how can users choose among them?

2 code implementations • 16 Sep 2022 • Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin

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.

no code implementations • 9 Jun 2022 • Yishay Mansour, Michal Moshkovitz, Cynthia Rudin

Interpretability is an essential building block for trustworthiness in reinforcement learning systems.

no code implementations • 22 Apr 2022 • Haiyang Huang, Zhi Chen, Cynthia Rudin

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.

no code implementations • 9 Mar 2022 • Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover

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%.

2 code implementations • 23 Feb 2022 • Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin

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.

3 code implementations • 1 Dec 2021 • Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo Seltzer

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.

1 code implementation • 10 Nov 2021 • Zhi Chen, Alexander Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems.

no code implementations • 15 Sep 2021 • Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin

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.

no code implementations • 12 Jul 2021 • Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin

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.

no code implementations • NAACL (sdp) 2021 • Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin

The goal of the competition is to classify a citation in a scientific article based on its purpose.

no code implementations • 4 Jun 2021 • Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

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.

1 code implementation • 12 May 2021 • Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos, Cynthia Rudin

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored.

no code implementations • 30 Apr 2021 • Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan

We then consider the broader ethical issues involved.

no code implementations • 23 Mar 2021 • Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin

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.

no code implementations • 20 Mar 2021 • Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.

1 code implementation • 5 Mar 2021 • Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin

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.

1 code implementation • 6 Jan 2021 • Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.

2 code implementations • 8 Dec 2020 • Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik

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.

Ranked #2 on Data Augmentation on GA1457

no code implementations • 22 Nov 2020 • Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi

Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution.

no code implementations • ICML 2020 • Tianyu Wang, Cynthia Rudin

We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function.

no code implementations • WS 2020 • Jerry Liu, Nathan O{'}Hara, Alex Rubin, er, Rachel Draelos, Cynthia Rudin

The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation.

no code implementations • WS 2020 • Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Draelos, Cynthia Rudin

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online.

2 code implementations • ICML 2020 • Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer

Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning.

1 code implementation • 8 May 2020 • Caroline Wang, Bin Han, Bhrij Patel, Cynthia Rudin

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.

16 code implementations • CVPR 2020 • Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin

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)

1 code implementation • 3 Mar 2020 • Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.

1 code implementation • 2 Mar 2020 • M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

2 code implementations • 5 Feb 2020 • Zhi Chen, Yijie Bei, Cynthia Rudin

What does a neural network encode about a concept as we traverse through the layers?

no code implementations • 5 Aug 2019 • Lesia Semenova, Cynthia Rudin, Ronald Parr

We hypothesize that there is an important reason that simple-yet-accurate models often do exist.

2 code implementations • 4 Jul 2019 • Stefano Tracà, Cynthia Rudin, Weiyu Yan

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.

1 code implementation • 27 Jun 2019 • M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

1 code implementation • 25 Jun 2019 • Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin

Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy.

no code implementations • 4 Jun 2019 • Cynthia Rudin, David Carlson

9) There is a method to the madness of deep neural architectures, but not always.

2 code implementations • NeurIPS 2019 • Xiyang Hu, Cynthia Rudin, Margo Seltzer

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's.

no code implementations • 26 Jan 2019 • Tianyu Wang, Weicheng Ye, Dawei Geng, Cynthia Rudin

Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains.

1 code implementation • 10 Jan 2019 • Jiayun Dong, Cynthia Rudin

Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains.

2 code implementations • 5 Dec 2018 • Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin

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

no code implementations • 30 Nov 2018 • Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

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.

3 code implementations • 26 Nov 2018 • Cynthia Rudin

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.

no code implementations • 18 Nov 2018 • Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches.

2 code implementations • 13 Nov 2018 • John Benhardt, Peter Hase, Liuyi Zhu, Cynthia Rudin

We provide an approach for generating beautiful poetry.

no code implementations • 10 Sep 2018 • Ramin Moghaddass, Cynthia Rudin

Doctors often rely on their past experience in order to diagnose patients.

3 code implementations • NeurIPS 2019 • Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, Cynthia Rudin

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.

3 code implementations • 18 Jun 2018 • Yameng Liu, Aw Dieng, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

1 code implementation • 9 May 2018 • Yijie Bei, Alex Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin

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.

2 code implementations • 23 Apr 2018 • Beau Coker, Cynthia Rudin, Gary King

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.

1 code implementation • 10 Mar 2018 • Siong Thye Goh, Cynthia Rudin

We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes.

no code implementations • 21 Feb 2018 • Cynthia Rudin, Yining Wang

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.

3 code implementations • 4 Jan 2018 • Aaron Fisher, Cynthia Rudin, Francesca Dominici

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

no code implementations • 29 Nov 2017 • Fulton Wang, Cynthia Rudin

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.

no code implementations • 16 Oct 2017 • Tong Wang, Cynthia Rudin

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}.

5 code implementations • 13 Oct 2017 • Oscar Li, Hao liu, Chaofan Chen, Cynthia Rudin

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.

1 code implementation • 6 Oct 2017 • Chaofan Chen, Cynthia Rudin

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.

no code implementations • 19 Jul 2017 • Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.

5 code implementations • 6 Apr 2017 • Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.

no code implementations • 23 Nov 2016 • Himabindu Lakkaraju, Cynthia Rudin

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.

no code implementations • 21 Oct 2016 • Himabindu Lakkaraju, Cynthia Rudin

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.

2 code implementations • 1 Oct 2016 • Berk Ustun, Cynthia Rudin

Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers.

no code implementations • 23 Jun 2016 • William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, Dana Penney

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions.

7 code implementations • ICML 2017 • Hongyu Yang, Cynthia Rudin, Margo Seltzer

They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree.

no code implementations • 6 Nov 2015 • Tong Wang, Cynthia Rudin

Or's of And's (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form.

2 code implementations • 5 Nov 2015 • Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan

We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.

1 code implementation • 22 Oct 2015 • Siong Thye Goh, Lesia Semenova, Cynthia Rudin

We present sparse tree-based and list-based density estimation methods for binary/categorical data.

no code implementations • 18 Oct 2015 • Fulton Wang, Cynthia Rudin

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.

no code implementations • 25 Jun 2015 • Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola

We present a framework for clustering with cluster-specific feature selection.

1 code implementation • 21 May 2015 • Stefano Tracà, Cynthia Rudin, Weiyu Yan

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.

no code implementations • 28 Apr 2015 • Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

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.

1 code implementation • 27 Apr 2015 • Fulton Wang, Tyler H. McCormick, Cynthia Rudin, John Gore

We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event.

no code implementations • 26 Mar 2015 • Jiaming Zeng, Berk Ustun, Cynthia Rudin

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.

no code implementations • NeurIPS 2014 • Been Kim, Cynthia Rudin, Julie Shah

We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering.

2 code implementations • 15 Feb 2015 • Berk Ustun, Cynthia Rudin

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.

no code implementations • 21 Nov 2014 • Fulton Wang, Cynthia Rudin

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.

1 code implementation • 4 Jul 2014 • Theja Tulabandhula, Cynthia Rudin

Our goal is to build robust optimization problems for making decisions based on complex data from the past.

1 code implementation • 30 May 2014 • Theja Tulabandhula, Cynthia Rudin

In this paper, we consider a supervised learning setting where side knowledge is provided about the labels of unlabeled examples.

no code implementations • 16 May 2014 • Berk Ustun, Cynthia Rudin

We present an integer programming framework to build accurate and interpretable discrete linear classification models.

1 code implementation • 13 Mar 2014 • Siong Thye Goh, Cynthia Rudin

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.

no code implementations • 2 Jul 2013 • Jonathan H. Huggins, Cynthia Rudin

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.''

no code implementations • 27 Jun 2013 • Berk Ustun, Stefano Tracà, Cynthia Rudin

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.

no code implementations • 25 Jun 2013 • Berk Ustun, Stefano Traca, Cynthia Rudin

We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification.

no code implementations • 8 Jun 2013 • Been Kim, Cynthia Rudin

Most people participate in meetings almost every day, multiple times a day.

no code implementations • 3 Dec 2011 • Theja Tulabandhula, Cynthia Rudin

This work proposes a way to align statistical modeling with decision making.

no code implementations • 27 Apr 2011 • Theja Tulabandhula, Cynthia Rudin

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.

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