10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS735 papers
- Massachusetts Institute of TechnologyUS455 papers
- University of OxfordGB379 papers
- University of ChicagoUS368 papers
- University of MichiganUS356 papers
- Lawrence Berkeley National LaboratoryUS354 papers
- The Ohio State UniversityUS350 papers
- Yale UniversityUS348 papers
- University of PittsburghUS317 papers
- Argonne National LaboratoryUS316 papers
- University College LondonGB312 papers
- University of PisaIT310 papers
44 papers · 2 filters
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
Wei Qiu, Yangsibo Huang, Quanzheng Li
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific…
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan Murphy
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning…
Deep Archimedean Copulas
Chun Kai Ling, Fei Fang, J. Zico Kolter
A central problem in machine learning and statistics is to model joint densities of random variables from data. Copulas are joint cumulative distribution functions with uniform mar…
Learning Interpretable Concept-Based Models with Human Feedback
Isaac Lage, Finale Doshi-Velez
Machine learning models that first learn a representation of a domain in terms of human-understandable concepts, then use it to make predictions, have been proposed to facilitate i…
Language-Mediated, Object-Centric Representation Learning
Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman +1
We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LO…
Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold +2
Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis…
Analyzing the Machine Learning Conference Review Process
David Tran, Alex Valtchanov, Keshav Ganapathy +4
Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the mac…
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
Yingying Li, Na Li
We consider online convex optimization with time-varying stage costs and additional switching costs. Since the switching costs introduce coupling across all stages, multi-step-ahea…
Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
Zhaowei She, Zilong Wang, Turgay Ayer +2
Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accur…
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
Sean McGrath, Parth Mehta, Alexandra Zytek +2
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can…
Learning Models for Actionable Recourse
Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for gen…
Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi +2
Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a partic…
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
Kaivalya Rawal, Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affect…
An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process
David Tran, Alex Valtchanov, Keshav Ganapathy +4
Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the mac…
PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash, Purang Abolmaesumi, Polina Golland +3
Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling…
For self-supervised learning, Rationality implies generalization, provably
Yamini Bansal, Gal Kaplun, Boaz Barak
We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation of the training data, and then f…
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
Esther Rolf, Jonathan Proctor, Tamma Carleton +5
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-po…
Depth-Width Trade-offs for Neural Networks via Topological Entropy
Kaifeng Bu, Yaobo Zhang, Qingxian Luo
One of the central problems in the study of deep learning theory is to understand how the structure properties, such as depth, width and the number of nodes, affect the expressivit…
Distributional Generalization: A New Kind of Generalization
Preetum Nakkiran, Yamini Bansal
We introduce a new notion of generalization -- Distributional Generalization -- which roughly states that outputs of a classifier at train and test time are close *as distributions…
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng, Cynthia Dwork, Jialiang Wang +1
Robust optimization has been widely used in nowadays data science, especially in adversarial training. However, little research has been done to quantify how robust optimization ch…
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data
David Bellamy, Leo Celi, Andrew L. Beam
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled oth…
Toward Better Generalization Bounds with Locally Elastic Stability
Zhun Deng, Hangfeng He, Weijie J. Su
Algorithmic stability is a key characteristic to ensure the generalization ability of a learning algorithm. Among different notions of stability, \emph{uniform stability} is arguab…
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
Colby Banbury, Chuteng Zhou, Igor Fedorov +6
Executing machine learning workloads locally on resource constrained microcontrollers (MCUs) promises to drastically expand the application space of IoT. However, so-called TinyML…
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Wanqian Yang, Lars Lorch, Moritz A. Graule +2
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and f…