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05/31/2023
Constrained Causal Bayesian Optimization
We propose constrained causal Bayesian optimization (cCBO), an approach ...
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02/27/2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
The ability to generate privacy-preserving synthetic versions of sensiti...
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11/09/2022
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
We introduce a challenging decision-making task that we call active acqu...
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06/23/2022
Affinity-Aware Graph Networks
Graph Neural Networks (GNNs) have emerged as a powerful technique for le...
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10/21/2021
A Fine-Grained Analysis on Distribution Shift
Robustness to distribution shifts is critical for deploying machine lear...
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10/21/2021
Algorithmic Amplification of Politics on Twitter
Content on Twitter's home timeline is selected and ordered by personaliz...
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07/21/2021
Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
Large Neighborhood Search (LNS) is a combinatorial optimization heuristi...
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12/23/2020
Solving Mixed Integer Programs Using Neural Networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...
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08/03/2020
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Recommender systems trained in a continuous learning fashion are plagued...
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07/28/2020
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
Deep Neural Networks (DNNs) with sparse input features have been widely ...
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04/28/2020
Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network pl...
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07/15/2019
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
One of the challenges in display advertising is that the distribution of...
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06/05/2018
Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Graph convolutional networks (GCNs) allow to apply traditional convoluti...
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06/05/2018
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Graphs are widely used as a natural framework that captures interactions...
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11/18/2017
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for effici...
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03/29/2017
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate represent...
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03/08/2017
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease...
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03/07/2017
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several c...
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11/15/2016