Search Results for author: Samuel Kaski

Found 100 papers, 35 papers with code

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

1 code implementation23 Jun 2022 Alexander Nikitin, Samuel Kaski

We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers).

Active Learning

Tackling covariate shift with node-based Bayesian neural networks

1 code implementation6 Jun 2022 Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski

In this paper, we interpret these latent noise variables as implicit representations of simple and domain-agnostic data perturbations during training, producing BNNs that perform well under covariate shift due to input corruptions.

Image Classification

Parallel MCMC Without Embarrassing Failures

1 code implementation22 Feb 2022 Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi

While efficient, this framework is very sensitive to the quality of subposterior sampling.

Active Learning Bayesian Inference

Zero-Shot Assistance in Novel Decision Problems

1 code implementation15 Feb 2022 Sebastiaan De Peuter, Samuel Kaski

We consider the problem of creating assistants that can help agents - often humans - solve novel sequential decision problems, assuming the agent is not able to specify the reward function explicitly to the assistant.

Decision Making

Deconfounded Representation Similarity for Comparison of Neural Networks

no code implementations31 Jan 2022 Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski

Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.

Transfer Learning

Approximate Bayesian Computation with Domain Expert in the Loop

1 code implementation28 Jan 2022 Ayush Bharti, Louis Filstroff, Samuel Kaski

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions.

Active Learning Dimensionality Reduction

Non-separable Spatio-temporal Graph Kernels via SPDEs

no code implementations16 Nov 2021 Alexander Nikitin, ST John, Arno Solin, Samuel Kaski

Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs.

Gaussian Processes

Likelihood-Free Inference in State-Space Models with Unknown Dynamics

1 code implementation NeurIPS Workshop Deep_Invers 2021 Alexander Aushev, Thong Tran, Henri Pesonen, Andrew Howes, Samuel Kaski

We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.

Locally Differentially Private Bayesian Inference

no code implementations27 Oct 2021 tejas kulkarni, Joonas Jälkö, Samuel Kaski, Antti Honkela

In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustworthy.

Bayesian Inference Privacy Preserving

De-randomizing MCMC dynamics with the diffusion Stein operator

no code implementations NeurIPS 2021 Zheyang Shen, Markus Heinonen, Samuel Kaski

Parallel to LD, Stein variational gradient descent (SVGD) similarly minimizes the KL, albeit endowed with a novel Stein-Wasserstein distance, by deterministically transporting a set of particle samples, thus de-randomizes the stochastic diffusion process.

Bayesian Inference

Behaviour-conditioned policies for cooperative reinforcement learning tasks

no code implementations4 Oct 2021 Antti Keurulainen, Isak Westerlund, Ariel Kwiatkowski, Samuel Kaski, Alexander Ilin

We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner.

Meta-Learning reinforcement-learning

Learning to Assist Agents by Observing Them

no code implementations4 Oct 2021 Antti Keurulainen, Isak Westerlund, Samuel Kaski, Alexander Ilin

On the other hand, offline data about the behavior of the assisted agent might be available, but is non-trivial to take advantage of by methods such as offline reinforcement learning.


Stochastic Cluster Embedding

1 code implementation18 Aug 2021 Zhirong Yang, Yuwei Chen, Denis Sedov, Samuel Kaski, Jukka Corander

In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE.

Data Visualization

Variational multiple shooting for Bayesian ODEs with Gaussian processes

no code implementations21 Jun 2021 Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.

Bayesian Inference Gaussian Processes +1

Targeted Active Learning for Bayesian Decision-Making

no code implementations8 Jun 2021 Louis Filstroff, Iiris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylmäoja, Samuel Kaski

Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.

Active Learning Decision Making

Affine Transport for Sim-to-Real Domain Adaptation

no code implementations25 May 2021 Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki

In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.

Domain Adaptation OpenAI Gym

DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction

no code implementations31 Mar 2021 Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka

More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses.

D3p -- A Python Package for Differentially-Private Probabilistic Programming

2 code implementations22 Mar 2021 Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela

We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees.

Probabilistic Programming Variational Inference

Decision Rule Elicitation for Domain Adaptation

no code implementations23 Feb 2021 Alexander Nikitin, Samuel Kaski

Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted results are to the target.

Decision Making Domain Adaptation

Differentially Private Bayesian Inference for Generalized Linear Models

no code implementations1 Nov 2020 tejas kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets.

Bayesian Inference

Scalable Bayesian neural networks by layer-wise input augmentation

1 code implementation26 Oct 2020 Trung Trinh, Samuel Kaski, Markus Heinonen

We introduce implicit Bayesian neural networks, a simple and scalable approach for uncertainty representation in deep learning.

Image Classification

Rethinking pooling in graph neural networks

1 code implementation NeurIPS 2020 Diego Mesquita, Amauri H. Souza, Samuel Kaski

In this paper, we build upon representative GNNs and introduce variants that challenge the need for locality-preserving representations, either using randomization or clustering on the complement graph.

Privacy-preserving Data Sharing on Vertically Partitioned Data

no code implementations19 Oct 2020 Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela

In this work, we present a method for differentially private data sharing by training a mixture model on vertically partitioned data, where each party holds different features for the same set of individuals.

Privacy Preserving Variational Inference

Bayesian Inference for Optimal Transport with Stochastic Cost

no code implementations19 Oct 2020 Anton Mallasto, Markus Heinonen, Samuel Kaski

In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic data objects, that can be cast as probability measures.

Bayesian Inference Computer Vision

Teaching to Learn: Sequential Teaching of Agents with Inner States

no code implementations14 Sep 2020 Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski

In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model.


Differentially private cross-silo federated learning

1 code implementation10 Jul 2020 Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, Samuel Kaski, Antti Honkela

In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting.

Federated Learning

Likelihood-Free Inference with Deep Gaussian Processes

1 code implementation18 Jun 2020 Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski

In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.

Gaussian Processes

Human Strategic Steering Improves Performance of Interactive Optimization

1 code implementation4 May 2020 Fabio Colella, Pedram Daee, Jussi Jokinen, Antti Oulasvirta, Samuel Kaski

To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function.

Recommendation Systems

Federated Stochastic Gradient Langevin Dynamics

no code implementations23 Apr 2020 Khaoula El Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski

We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD).

Federated Learning Metric Learning

A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication

no code implementations6 Apr 2020 Tom Vander Aa, Xiangju Qin, Paul Blomstedt, Roel Wuyts, Wilfried Verachtert, Samuel Kaski

We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.

Recommendation Systems

Correlated Feature Selection with Extended Exclusive Group Lasso

no code implementations27 Feb 2020 Yuxin Sun, Benny Chain, Samuel Kaski, John Shawe-Taylor

In many high dimensional classification or regression problems set in a biological context, the complete identification of the set of informative features is often as important as predictive accuracy, since this can provide mechanistic insight and conceptual understanding.

feature selection

Informative Bayesian Neural Network Priors for Weak Signals

no code implementations24 Feb 2020 Tianyu Cui, Aki Havulinna, Pekka Marttinen, Samuel Kaski

Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.

Split-BOLFI for for misspecification-robust likelihood free inference in high dimensions

no code implementations21 Feb 2020 Owen Thomas, Henri Pesonen, Raquel Sá-Leão, Hermínia de Lencastre, Samuel Kaski, Jukka Corander

To advance the possibilities for performing likelihood-free inference in high-dimensional parameter spaces, here we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space.

Bayesian Optimisation Efficient Exploration

Projective Preferential Bayesian Optimization

1 code implementation ICML 2020 Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski

This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface.

Interactive AI with a Theory of Mind

no code implementations1 Dec 2019 Mustafa Mert Çelikok, Tomi Peltola, Pedram Daee, Samuel Kaski

Understanding each other is the key to success in collaboration.

Probabilistic Formulation of the Take The Best Heuristic

1 code implementation1 Nov 2019 Tomi Peltola, Jussi Jokinen, Samuel Kaski

The strengths of the probabilistic formulation, in addition to providing a bounded rational account of the learning of the heuristic, include natural extensibility with additional cognitively plausible constraints and prior information, and the possibility to embed the heuristic as a subpart of a larger probabilistic model.

Decision Making

A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework

1 code implementation21 Oct 2019 Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski

Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior.

Interpretable Machine Learning

Scalable Probabilistic Matrix Factorization with Graph-Based Priors

1 code implementation25 Aug 2019 Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka, Samuel Kaski

The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros.

 Ranked #1 on Recommendation Systems on MovieLens 20M (RMSE metric, using extra training data)

Matrix Completion Recommendation Systems

Scalable Bayesian Non-linear Matrix Completion

no code implementations31 Jul 2019 Xiangju Qin, Paul Blomstedt, Samuel Kaski

Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed.

Collaborative Filtering Matrix Completion +1

Learning spectrograms with convolutional spectral kernels

no code implementations23 May 2019 Zheyang Shen, Markus Heinonen, Samuel Kaski

We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions.

Gaussian Processes

Active Learning for Decision-Making from Imbalanced Observational Data

1 code implementation10 Apr 2019 Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski

Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).

Active Learning Decision Making

Embarrassingly parallel MCMC using deep invertible transformations

no code implementations11 Mar 2019 Diego Mesquita, Paul Blomstedt, Samuel Kaski

While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge.

Bayesian Inference

Representation Transfer for Differentially Private Drug Sensitivity Prediction

no code implementations29 Jan 2019 Teppo Niinimäki, Mikko Heikkilä, Antti Honkela, Samuel Kaski

Differentially private learning with genomic data is challenging because it is more difficult to guarantee the privacy in high dimensions.

Cancer type classification Dimensionality Reduction +1

Learning Global Pairwise Interactions with Bayesian Neural Networks

1 code implementation24 Jan 2019 Tianyu Cui, Pekka Marttinen, Samuel Kaski

Estimating global pairwise interaction effects, i. e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications.

Neural Non-Stationary Spectral Kernel

1 code implementation27 Nov 2018 Sami Remes, Markus Heinonen, Samuel Kaski

Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data.

Gaussian Processes

Approximate Bayesian Computation via Population Monte Carlo and Classification

no code implementations29 Oct 2018 Charlie Rogers-Smith, Henri Pesonen, Samuel Kaski

Approximate Bayesian computation (ABC) methods can be used to sample from posterior distributions when the likelihood function is unavailable or intractable, as is often the case in biological systems.

Classification General Classification

Harmonizable mixture kernels with variational Fourier features

no code implementations10 Oct 2018 Zheyang Shen, Markus Heinonen, Samuel Kaski

The expressive power of Gaussian processes depends heavily on the choice of kernel.

Gaussian Processes

Deep learning with differential Gaussian process flows

1 code implementation9 Oct 2018 Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function.

Gaussian Processes General Classification

Deep convolutional Gaussian processes

1 code implementation6 Oct 2018 Kenneth Blomqvist, Samuel Kaski, Markus Heinonen

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure.

Classification Gaussian Processes +2

Machine Teaching of Active Sequential Learners

1 code implementation NeurIPS 2019 Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski

We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses.

Multi-Armed Bandits Probabilistic Programming

Bayesian Metabolic Flux Analysis reveals intracellular flux couplings

1 code implementation18 Apr 2018 Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki

Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.

Variational zero-inflated Gaussian processes with sparse kernels

1 code implementation13 Mar 2018 Pashupati Hegde, Markus Heinonen, Samuel Kaski

We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent.

Gaussian Processes Variational Inference

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

1 code implementation13 Oct 2017 Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski

In human-in-the-loop machine learning, the user provides information beyond that in the training data.

Sentiment Analysis

Non-Stationary Spectral Kernels

1 code implementation NeurIPS 2017 Sami Remes, Markus Heinonen, Samuel Kaski

We propose non-stationary spectral kernels for Gaussian process regression.

Time Series

Inverse Reinforcement Learning from Summary Data

no code implementations28 Mar 2017 Antti Kangasrääsiö, Samuel Kaski

Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting reinforcement learning models to behavioral data.


Differentially Private Bayesian Learning on Distributed Data

1 code implementation NeurIPS 2017 Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects.

Bayesian Inference

Distributed Bayesian Matrix Factorization with Limited Communication

no code implementations2 Mar 2017 Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski

Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.

Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation

no code implementations21 Feb 2017 Makoto Yamada, Song Liu, Samuel Kaski

We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features.

Density Ratio Estimation Outlier Detection +1

Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction

1 code implementation10 Dec 2016 Pedram Daee, Tomi Peltola, Marta Soare, Samuel Kaski

Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging.

Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction

no code implementations8 Dec 2016 Homayun Afrabandpey, Tomi Peltola, Samuel Kaski

The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters.

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

no code implementations7 Dec 2016 Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-Ud-Din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski

The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.

Small Data Image Classification

Inferring Cognitive Models from Data using Approximate Bayesian Computation

no code implementations2 Dec 2016 Antti Kangasrääsiö, Kumaripaba Athukorala, Andrew Howes, Jukka Corander, Samuel Kaski, Antti Oulasvirta

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.

Likelihood-free inference by ratio estimation

1 code implementation30 Nov 2016 Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U. Gutmann

The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.

Efficient differentially private learning improves drug sensitivity prediction

no code implementations7 Jun 2016 Antti Honkela, Mrinal Das, Arttu Nieminen, Onur Dikmen, Samuel Kaski

Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised.

Regression with n$\to$1 by Expert Knowledge Elicitation

no code implementations20 May 2016 Marta Soare, Muhammad Ammad-Ud-Din, Samuel Kaski

We consider regression under the "extremely small $n$ large $p$" condition, where the number of samples $n$ is so small compared to the dimensionality $p$ that predictors cannot be estimated without prior knowledge.

Bayesian inference in hierarchical models by combining independent posteriors

no code implementations30 Mar 2016 Ritabrata Dutta, Paul Blomstedt, Samuel Kaski

Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.

Bayesian Inference

Localized Lasso for High-Dimensional Regression

no code implementations22 Mar 2016 Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski

We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.

Multi-view Kernel Completion

2 code implementations8 Feb 2016 Sahely Bhadra, Samuel Kaski, Juho Rousu

In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels.

Sparse group factor analysis for biclustering of multiple data sources

no code implementations29 Dec 2015 Kerstin Bunte, Eemeli Leppäaho, Inka Saarinen, Samuel Kaski

Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments.

Visualizations Relevant to The User By Multi-View Latent Variable Factorization

no code implementations24 Dec 2015 Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski

The factorization is a generative model in which the display is parameterized as a part of the factorization and the other factors explain away the aspects not expressible in a two-dimensional display.

Data Visualization

Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

no code implementations17 Dec 2015 Sami Remes, Tommi Mononen, Samuel Kaski

We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA).

Classification EEG +2

Learning Structures of Bayesian Networks for Variable Groups

no code implementations31 Aug 2015 Pekka Parviainen, Samuel Kaski

We show that for dependency structures between groups to be expressible exactly, the data have to satisfy the so-called groupwise faithfulness assumption.

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

no code implementations18 Aug 2015 Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.


Convex Factorization Machine for Regression

1 code implementation4 Jul 2015 Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang

We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).

Modelling-based experiment retrieval: A case study with gene expression clustering

no code implementations19 May 2015 Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma, Samuel Kaski

For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.

Classification and Bayesian Optimization for Likelihood-Free Inference

no code implementations19 Feb 2015 Michael U. Gutmann, Jukka Corander, Ritabrata Dutta, Samuel Kaski

This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.

Classification General Classification

Bayesian multi-tensor factorization

no code implementations15 Dec 2014 Suleiman A. Khan, Eemeli Leppäaho, Samuel Kaski

We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors.


Group Factor Analysis

no code implementations21 Nov 2014 Arto Klami, Seppo Virtanen, Eemeli Leppäaho, Samuel Kaski

Factor analysis provides linear factors that describe relationships between individual variables of a data set.

Variational Inference

Multiple Output Regression with Latent Noise

no code implementations27 Oct 2014 Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski

In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal.

Time Series

PinView: Implicit Feedback in Content-Based Image Retrieval

no code implementations2 Oct 2014 Zakria Hussain, Arto Klami, Jussi Kujala, Alex P. Leung, Kitsuchart Pasupa, Peter Auer, Samuel Kaski, Jorma Laaksonen, John Shawe-Taylor

It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user.

Content-Based Image Retrieval online learning

Likelihood-free inference via classification

no code implementations18 Jul 2014 Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.

Bayesian Inference Classification +1

Toward computational cumulative biology by combining models of biological datasets

no code implementations1 Apr 2014 Ali Faisal, Jaakko Peltonen, Elisabeth Georgii, Johan Rung, Samuel Kaski

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative.

Retrieval of Experiments by Efficient Estimation of Marginal Likelihood

no code implementations19 Feb 2014 Sohan Seth, John Shawe-Taylor, Samuel Kaski

To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements.

Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis

no code implementations30 Dec 2013 Suleiman A. Khan, Seppo Virtanen, Olli P Kallioniemi, Krister Wennerberg, Antti Poso, Samuel Kaski

Results: In this paper, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on ge-nome-wide gene expression across several cancer cell lines (CMap database).

Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects

no code implementations16 Oct 2013 Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski

To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model.

Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space

no code implementations8 Oct 2013 Ritabrata Dutta, Sohan Seth, Samuel Kaski

We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data.

Kernelized Bayesian Matrix Factorization

no code implementations6 Nov 2012 Mehmet Gönen, Suleiman A. Khan, Samuel Kaski

Our algorithm obtains the lowest Hamming loss values on 10 out of 14 multilabel classification data sets compared to five state-of-the-art multilabel learning algorithms.

Recommendation Systems

RPA: Probabilistic analysis of probe performance and robust summarization

no code implementations22 Sep 2011 Leo Lahti, Laura L. Elo, Tero Aittokallio, Samuel Kaski

Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood.

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