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09/21/2023
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Structure learning is the crux of causal inference. Notably, causal disc...
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08/09/2023
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Consider a setting where multiple parties holding sensitive data aim to ...
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06/19/2023
Practical Equivariances via Relational Conditional Neural Processes
Conditional Neural Processes (CNPs) are a class of metalearning models p...
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06/05/2023
Input gradient diversity for neural network ensembles
Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robu...
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05/25/2023
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian co...
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05/23/2023
Cost-aware learning of relevant contextual variables within Bayesian optimization
Contextual Bayesian Optimization (CBO) is a powerful framework for optim...
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05/19/2023
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
Temporally indexed data are essential in a wide range of fields and of i...
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04/11/2023
Characterizing personalized effects of family information on disease risk using graph representation learning
Family history is considered a risk factor for many diseases because it ...
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03/03/2023
Online simulator-based experimental design for cognitive model selection
The problem of model selection with a limited number of experimental tri...
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02/07/2023
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept for many multiagent settings is the Stackelbe...
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01/27/2023
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance betwe...
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11/29/2022
Differentiable User Models
Probabilistic user modeling is essential for building collaborative AI s...
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10/28/2022
DPVIm: Differentially Private Variational Inference Improved
Differentially private (DP) release of multidimensional statistics typic...
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10/25/2022
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Bayesian optimization (BO) is a powerful framework for optimizing black-...
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10/12/2022
Modular Flows: Differential Molecular Generation
Generating new molecules is fundamental to advancing critical applicatio...
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09/29/2022
Provably expressive temporal graph networks
Temporal graph networks (TGNs) have gained prominence as models for embe...
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08/18/2022
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
Bayesian optimization (BO) is a well-established method to optimize blac...
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06/23/2022
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
Predictive maintenance (PdM) is the task of scheduling maintenance opera...
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06/06/2022
Tackling covariate shift with node-based Bayesian neural networks
Bayesian neural networks (BNNs) promise improved generalization under co...
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05/28/2022
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
While generation of synthetic data under differential privacy (DP) has r...
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04/03/2022
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs
Centaurs are half-human, half-AI decision-makers where the AI's goal is ...
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02/22/2022
Parallel MCMC Without Embarrassing Failures
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits paralle...
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02/15/2022
Zero-Shot Assistance in Novel Decision Problems
We consider the problem of creating assistants that can help agents - of...
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01/31/2022
Deconfounded Representation Similarity for Comparison of Neural Networks
Similarity metrics such as representational similarity analysis (RSA) an...
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01/28/2022
Approximate Bayesian Computation with Domain Expert in the Loop
Approximate Bayesian computation (ABC) is a popular likelihood-free infe...
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12/23/2021
ABC of the Future
Approximate Bayesian computation (ABC) has advanced in two decades from ...
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12/01/2021
Prior knowledge elicitation: The past, present, and future
Specification of the prior distribution for a Bayesian model is a centra...
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11/16/2021
Non-separable Spatio-temporal Graph Kernels via SPDEs
Gaussian processes (GPs) provide a principled and direct approach for in...
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11/02/2021
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
We introduce a method for inferring and predicting latent states in the ...
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10/27/2021
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a techn...
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10/07/2021
De-randomizing MCMC dynamics with the diffusion Stein operator
Approximate Bayesian inference estimates descriptors of an intractable t...
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10/04/2021
Learning to Assist Agents by Observing Them
The ability of an AI agent to assist other agents, such as humans, is an...
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10/04/2021
Behaviour-conditioned policies for cooperative reinforcement learning tasks
The cooperation among AI systems, and between AI systems and humans is b...
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08/18/2021
Stochastic Cluster Embedding
Neighbor Embedding (NE) that aims to preserve pairwise similarities betw...
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07/22/2021
Toward AI Assistants That Let Designers Design
AI for supporting designers needs to be rethought. It should aim to coop...
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06/21/2021
Bayesian inference of ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of u...
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06/08/2021
Targeted Active Learning for Bayesian Decision-Making
Active learning is usually applied to acquire labels of informative data...
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05/25/2021
Affine Transport for Sim-to-Real Domain Adaptation
Sample-efficient domain adaptation is an open problem in robotics. In th...
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03/31/2021
DIVERSE: bayesian Data IntegratiVE learning for precise drug ResponSE prediction
Detecting predictive biomarkers from multi-omics data is important for p...
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03/22/2021
d3p – A Python Package for Differentially-Private Probabilistic Programming
We present d3p, a software package designed to help fielding runtime eff...
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02/23/2021
Decision Rule Elicitation for Domain Adaptation
Human-in-the-loop machine learning is widely used in artificial intellig...
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11/02/2020
Sample-efficient reinforcement learning using deep Gaussian processes
Reinforcement learning provides a framework for learning to control whic...
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11/01/2020
Differentially Private Bayesian Inference for Generalized Linear Models
The framework of differential privacy (DP) upper bounds the information ...
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10/26/2020
Scalable Bayesian neural networks by layer-wise input augmentation
We introduce implicit Bayesian neural networks, a simple and scalable ap...
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10/22/2020
Rethinking pooling in graph neural networks
Graph pooling is a central component of a myriad of graph neural network...
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10/19/2020
Bayesian Inference for Optimal Transport with Stochastic Cost
In machine learning and computer vision, optimal transport has had signi...
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10/19/2020
Privacy-preserving Data Sharing on Vertically Partitioned Data
In this work, we present a method for differentially private data sharin...
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09/14/2020
Teaching to Learn: Sequential Teaching of Agents with Inner States
In sequential machine teaching, a teacher's objective is to provide the ...
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07/10/2020
Differentially private cross-silo federated learning
Strict privacy is of paramount importance in distributed machine learnin...
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06/18/2020
