166 citations · 595 across the 71 of their papers we have counts for
15 papers · 1 filter
Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries
Matteo Sesia, Stefano Favaro, Edgar Dobriban
This paper develops conformal inference methods to construct a confidence interval for the frequency of a queried object in a very large discrete data set, based on a sketch with a…
Doubly Robust Proximal Synthetic Controls
Hongxiang Qiu, Xu Shi, Wang Miao +2
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treate…
PAC Prediction Sets for Meta-Learning
Sangdon Park, Edgar Dobriban, Insup Lee +1
Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in…
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
Shuo Li, Xiayan Ji, Edgar Dobriban +2
Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefi…
Fair Bayes-Optimal Classifiers Under Predictive Parity
Xianli Zeng, Edgar Dobriban, Guang Cheng
Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based me…
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
Druv Pai, Michael Psenka, Chih-Yuan Chiu +3
We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear sub…
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
Yinshuang Xu, Jiahui Lei, Edgar Dobriban +1
We introduce a unified framework for group equivariant networks on homogeneous spaces derived from a Fourier perspective. We consider tensor-valued feature fields, before and after…
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning
Souradeep Dutta, Yahan Yang, Elena Bernardis +2
The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness…
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Xinmeng Huang, Donghwan Lee, Edgar Dobriban +1
In modern machine learning, users often have to collaborate to learn the distribution of the data. Communication can be a significant bottleneck. Prior work has studied homogeneous…
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban +1
We propose a method for 3D shape reconstruction from unoriented point clouds. Our method consists of a novel SE(3)-equivariant coordinate-based network (TF-ONet), that parametrizes…
Prediction Sets Adaptive to Unknown Covariate Shift
Hongxiang Qiu, Edgar Dobriban, Eric Tchetgen Tchetgen
Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructin…
T-Cal: An optimal test for the calibration of predictive models
Donghwan Lee, Xinmeng Huang, Hamed Hassani +1
The prediction accuracy of machine learning methods is steadily increasing, but the calibration of their uncertainty predictions poses a significant challenge. Numerous works focus…
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani +4
Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging…
Bayes-Optimal Classifiers under Group Fairness
Xianli Zeng, Edgar Dobriban, Guang Cheng
Machine learning algorithms are becoming integrated into more and more high-stakes decision-making processes, such as in social welfare issues. Due to the need of mitigating the po…
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy +4
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence…