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05/17/2023
Equivariant Few-Shot Learning from Pretrained Models
Efficient transfer learning algorithms are key to the success of foundat...
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04/30/2023
Reliable Gradient-free and Likelihood-free Prompt Tuning
Due to privacy or commercial constraints, large pre-trained language mod...
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02/16/2023
Group Fairness with Uncertainty in Sensitive Attributes
We consider learning a fair predictive model when sensitive attributes a...
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01/15/2023
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
Automated AI classifiers should be able to defer the prediction to a hum...
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12/14/2022
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
It is known that neural networks have the problem of being over-confiden...
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12/13/2022
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
In consequential decision-making applications, mitigating unwanted biase...
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10/13/2022
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
We introduce equi-tuning, a novel fine-tuning method that transforms (po...
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08/26/2022
Causal Bandits for Linear Structural Equation Models
This paper studies the problem of designing an optimal sequence of inter...
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08/08/2022
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
The availability of reliable, high-resolution climate and weather data i...
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07/14/2022
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data
Training generative models that capture rich semantics of the data and i...
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11/15/2021
Scalable Intervention Target Estimation in Linear Models
This paper considers the problem of estimating the unknown intervention ...
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10/28/2021
Selective Regression Under Fairness Criteria
Selective regression allows abstention from prediction if the confidence...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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06/02/2021
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
In this paper, we describe an open source Python toolkit named Uncertain...
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06/01/2021
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Accurate quantification of model uncertainty has long been recognized as...
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04/20/2021
Detector-Free Weakly Supervised Grounding by Separation
Nowadays, there is an abundance of data involving images and surrounding...
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02/10/2021
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Temporal modelling is the key for efficient video action recognition. Wh...
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12/30/2020
A Maximal Correlation Approach to Imposing Fairness in Machine Learning
As machine learning algorithms grow in popularity and diversify to many ...
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11/15/2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Transparency of algorithmic systems entails exposing system properties t...
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10/25/2020
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods a...
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09/09/2020
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget
BigGAN is the state-of-the-art in high-resolution image generation, succ...
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09/05/2020
Optimizing Mode Connectivity via Neuron Alignment
The loss landscapes of deep neural networks are not well understood due ...
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07/31/2020
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Action recognition is an open and challenging problem in computer vision...
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07/17/2020
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Data augmentation is one of the most important tools in training modern ...
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06/10/2020
Fair Data Integration
The use of machine learning (ML) in high-stakes societal decisions has e...
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04/27/2020
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
The wide-spread adoption of representation learning technologies in clin...
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03/15/2020
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
In this paper, we propose a new few-shot learning method called StarNet,...
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03/14/2020
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
The field of Few-Shot Learning (FSL), or learning from very few (typical...
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02/10/2020
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
The hypothesis that sub-network initializations (lottery) exist within t...
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11/18/2019
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
More than 200 generic drugs approved by the U.S. Food and Drug Administr...
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10/29/2019
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Recent advances in computer vision and deep learning have led to breakth...
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09/09/2019
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
With rapid adoption of deep learning in high-regret applications, the qu...
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09/06/2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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05/29/2019
Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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11/30/2018
Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification ...
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11/13/2018
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fai...
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10/03/2018
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Fairness is an increasingly important concern as machine learning models...
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09/20/2018
Can Deep Clinical Models Handle Real-World Domain Shifts?
The hypothesis that computational models can be reliable enough to be ad...
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05/24/2018
Fairness GAN
In this paper, we introduce the Fairness GAN, an approach for generating...
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11/15/2017
Optimizing Kernel Machines using Deep Learning
Building highly non-linear and non-parametric models is central to sever...
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11/02/2017
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Disentangled representations, where the higher level data generative fac...
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05/24/2017
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Semi-supervised learning methods using Generative Adversarial Networks (...
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12/28/2016
A Deep Learning Approach To Multiple Kernel Fusion
Kernel fusion is a popular and effective approach for combining multiple...
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06/15/2016