b'Samuel Kaski'

research

∙ 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...

0 Tiago da Silva, et al. ∙

research

∙ 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 ...

0 Lukas Prediger, et al. ∙

research

∙ 06/19/2023

Practical Equivariances via Relational Conditional Neural Processes

Conditional Neural Processes (CNPs) are a class of metalearning models p...

0 Daolang Huang, et al. ∙

research

∙ 06/05/2023

Input gradient diversity for neural network ensembles

Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robu...

0 Trung Trinh, et al. ∙

research

∙ 05/25/2023

Learning Robust Statistics for Simulation-based Inference under Model Misspecification

Simulation-based inference (SBI) methods such as approximate Bayesian co...

0 Daolang Huang, et al. ∙

research

∙ 05/23/2023

Cost-aware learning of relevant contextual variables within Bayesian optimization

Contextual Bayesian Optimization (CBO) is a powerful framework for optim...

0 Julien Martinelli, et al. ∙

research

∙ 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...

0 Alexander Nikitin, et al. ∙

research

∙ 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 ...

0 Sophie Wharrie, et al. ∙

research

∙ 03/03/2023

Online simulator-based experimental design for cognitive model selection

The problem of model selection with a limited number of experimental tri...

0 Alexander Aushev, et al. ∙

research

∙ 02/07/2023

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

A natural solution concept for many multiagent settings is the Stackelbe...

0 Robert Loftin, et al. ∙

research

∙ 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...

0 Ayush Bharti, et al. ∙

research

∙ 11/29/2022

Differentiable User Models

Probabilistic user modeling is essential for building collaborative AI s...

0 Alex Hämäläinen, et al. ∙

research

∙ 10/28/2022

DPVIm: Differentially Private Variational Inference Improved

Differentially private (DP) release of multidimensional statistics typic...

0 Joonas Jälkö, et al. ∙

research

∙ 10/25/2022

Multi-Fidelity Bayesian Optimization with Unreliable Information Sources

Bayesian optimization (BO) is a powerful framework for optimizing black-...

0 Petrus Mikkola, et al. ∙

research

∙ 10/12/2022

Modular Flows: Differential Molecular Generation

Generating new molecules is fundamental to advancing critical applicatio...

0 Yogesh Verma, et al. ∙

research

∙ 09/29/2022

Provably expressive temporal graph networks

Temporal graph networks (TGNs) have gained prominence as models for embe...

0 Amauri H. Souza, et al. ∙

research

∙ 08/18/2022

Bayesian Optimization Augmented with Actively Elicited Expert Knowledge

Bayesian optimization (BO) is a well-established method to optimize blac...

0 Daolang Huang, et al. ∙

research

∙ 06/23/2022

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

Predictive maintenance (PdM) is the task of scheduling maintenance opera...

0 Alexander Nikitin, et al. ∙

research

∙ 06/06/2022

Tackling covariate shift with node-based Bayesian neural networks

Bayesian neural networks (BNNs) promise improved generalization under co...

0 Trung Trinh, et al. ∙

research

∙ 05/28/2022

Noise-Aware Statistical Inference with Differentially Private Synthetic Data

While generation of synthetic data under differential privacy (DP) has r...

0 Ossi Räisä, et al. ∙

research

∙ 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 ...

0 Mustafa Mert Çelikok, et al. ∙

research

∙ 02/22/2022

Parallel MCMC Without Embarrassing Failures

Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits paralle...

0 Daniel Augusto de Souza, et al. ∙

research

∙ 02/15/2022

Zero-Shot Assistance in Novel Decision Problems

We consider the problem of creating assistants that can help agents - of...

0 Sebastiaan De Peuter, et al. ∙

research

∙ 01/31/2022

Deconfounded Representation Similarity for Comparison of Neural Networks

Similarity metrics such as representational similarity analysis (RSA) an...

9 Tianyu Cui, et al. ∙

research

∙ 01/28/2022

Approximate Bayesian Computation with Domain Expert in the Loop

Approximate Bayesian computation (ABC) is a popular likelihood-free infe...

0 Ayush Bharti, et al. ∙

research

∙ 12/23/2021

ABC of the Future

Approximate Bayesian computation (ABC) has advanced in two decades from ...

0 Henri Pesonen, et al. ∙

research

∙ 12/01/2021

Prior knowledge elicitation: The past, present, and future

Specification of the prior distribution for a Bayesian model is a centra...

0 Petrus Mikkola, et al. ∙

research

∙ 11/16/2021

Non-separable Spatio-temporal Graph Kernels via SPDEs

Gaussian processes (GPs) provide a principled and direct approach for in...

0 Alexander Nikitin, et al. ∙

research

∙ 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 ...

10 Alexander Aushev, et al. ∙

research

∙ 10/27/2021

Locally Differentially Private Bayesian Inference

In recent years, local differential privacy (LDP) has emerged as a techn...

0 Tejas Kulkarni, et al. ∙

research

∙ 10/07/2021

De-randomizing MCMC dynamics with the diffusion Stein operator

Approximate Bayesian inference estimates descriptors of an intractable t...

0 Zheyang Shen, et al. ∙

research

∙ 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...

0 Antti Keurulainen, et al. ∙

research

∙ 10/04/2021

Behaviour-conditioned policies for cooperative reinforcement learning tasks

The cooperation among AI systems, and between AI systems and humans is b...

0 Antti Keurulainen, et al. ∙

research

∙ 08/18/2021

Stochastic Cluster Embedding

Neighbor Embedding (NE) that aims to preserve pairwise similarities betw...

21 Zhirong Yang, et al. ∙

research

∙ 07/22/2021

Toward AI Assistants That Let Designers Design

AI for supporting designers needs to be rethought. It should aim to coop...

0 Sebastiaan De Peuter, et al. ∙

research

∙ 06/21/2021

Bayesian inference of ODEs with Gaussian processes

Recent machine learning advances have proposed black-box estimation of u...

24 Pashupati Hegde, et al. ∙

research

∙ 06/08/2021

Targeted Active Learning for Bayesian Decision-Making

Active learning is usually applied to acquire labels of informative data...

0 Louis Filstroff, et al. ∙

research

∙ 05/25/2021

Affine Transport for Sim-to-Real Domain Adaptation

Sample-efficient domain adaptation is an open problem in robotics. In th...

0 Anton Mallasto, et al. ∙

research

∙ 03/31/2021

DIVERSE: bayesian Data IntegratiVE learning for precise drug ResponSE prediction

Detecting predictive biomarkers from multi-omics data is important for p...

0 Betül Güvenç Paltun, et al. ∙

research

∙ 03/22/2021

d3p – A Python Package for Differentially-Private Probabilistic Programming

We present d3p, a software package designed to help fielding runtime eff...

0 Lukas Prediger, et al. ∙

research

∙ 02/23/2021

Decision Rule Elicitation for Domain Adaptation

Human-in-the-loop machine learning is widely used in artificial intellig...

0 Alexander Nikitin, et al. ∙

research

∙ 11/02/2020

Sample-efficient reinforcement learning using deep Gaussian processes

Reinforcement learning provides a framework for learning to control whic...

0 Charles Gadd, et al. ∙

research

∙ 11/01/2020

Differentially Private Bayesian Inference for Generalized Linear Models

The framework of differential privacy (DP) upper bounds the information ...

0 Tejas Kulkarni, et al. ∙

research

∙ 10/26/2020

Scalable Bayesian neural networks by layer-wise input augmentation

We introduce implicit Bayesian neural networks, a simple and scalable ap...

0 Trung Trinh, et al. ∙

research

∙ 10/22/2020

Rethinking pooling in graph neural networks

Graph pooling is a central component of a myriad of graph neural network...

0 Diego Mesquita, et al. ∙

research

∙ 10/19/2020

Bayesian Inference for Optimal Transport with Stochastic Cost

In machine learning and computer vision, optimal transport has had signi...

0 Anton Mallasto, et al. ∙

research

∙ 10/19/2020

Privacy-preserving Data Sharing on Vertically Partitioned Data

In this work, we present a method for differentially private data sharin...

0 Razane Tajeddine, et al. ∙

research

∙ 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 ...

10 Mustafa Mert Çelikok, et al. ∙

research

∙ 07/10/2020

Differentially private cross-silo federated learning

Strict privacy is of paramount importance in distributed machine learnin...

9 Mikko A. Heikkilä, et al. ∙

research

∙ 06/18/2020

Likelihood-Free Inference with Deep Gaussian Processes

In recent years, surrogate models have been successfully used in likelih...

14 Alexander Aushev, et al. ∙

Samuel Kaski

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