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06/19/2023
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts
Effective machine learning models learn both robust features that direct...
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06/08/2023
Conservative Prediction via Data-Driven Confidence Minimization
Errors of machine learning models are costly, especially in safety-criti...
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02/10/2023
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features
Conventional approaches to robustness try to learn a model based on caus...
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01/26/2023
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
The fluency and factual knowledge of large language models (LLMs) height...
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11/25/2022
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Distribution shift occurs when the test distribution differs from the tr...
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10/20/2022
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
A common approach to transfer learning under distribution shift is to fi...
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10/12/2022
On Divergence Measures for Bayesian Pseudocoresets
A Bayesian pseudocoreset is a small synthetic dataset for which the post...
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02/07/2022
Diversify and Disambiguate: Learning From Underspecified Data
Many datasets are underspecified, which means there are several equally ...
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10/27/2021
Diversity Matters When Learning From Ensembles
Deep ensembles excel in large-scale image classification tasks both in t...
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07/05/2021
On The Distribution of Penultimate Activations of Classification Networks
This paper studies probability distributions of penultimate activations ...
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10/29/2020
Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
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08/07/2020
Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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08/07/2020
Neural Complexity Measures
While various complexity measures for diverse model classes have been pr...
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09/30/2019
Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
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05/28/2019
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
Without relevant human priors, neural networks may learn uninterpretable...
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05/28/2019
Discrete Infomax Codes for Meta-Learning
Learning compact discrete representations of data is itself a key task i...
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10/01/2018
Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
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01/17/2018