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05/30/2023
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Large language models (LLMs) specializing in natural language generation...
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05/27/2023
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
The explicit incorporation of task-specific inductive biases through sym...
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02/02/2023
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
Many real-world multi-label prediction problems involve set-valued predi...
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05/25/2022
Conformal Prediction Intervals with Temporal Dependence
Cross-sectional prediction is common in many domains such as healthcare,...
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05/20/2022
Conformal Prediction with Temporal Quantile Adjustments
We develop Temporal Quantile Adjustment (TQA), a general method to const...
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02/15/2022
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Deep neural network (DNN) classifiers are often overconfident, producing...
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10/14/2021
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Equivariance has emerged as a desirable property of representations of o...
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06/01/2021
Locally Valid and Discriminative Confidence Intervals for Deep Learning Models
Crucial for building trust in deep learning models for critical real-wor...
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02/25/2021
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a...
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12/08/2020
Rotation-Invariant Autoencoders for Signals on Spheres
Omnidirectional images and spherical representations of 3D shapes cannot...
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06/05/2020
The Expected Jacobian Outerproduct: Theory and Empirics
The expected gradient outerproduct (EGOP) of an unknown regression funct...
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10/10/2019
Asymmetric Multiresolution Matrix Factorization
Multiresolution Matrix Factorization (MMF) was recently introduced as an...
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09/10/2019
Deep Learning for Automated Classification and Characterization of Amorphous Materials
It is difficult to quantify structure-property relationships and to iden...
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08/30/2018
Discriminative Learning of Similarity and Group Equivariant Representations
One of the most fundamental problems in machine learning is to compare e...
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06/24/2018
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Recent work by Cohen et al. has achieved state-of-the-art results for le...
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02/11/2018
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Convolutional neural networks have been extremely successful in the imag...
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01/07/2018