Author name cluster

Tomasz Kuśmierczyk

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAAI Conference 2025 Short Paper

Hypernetwork Approach to Bayesian MAML (Student Abstract)

Piotr Borycki
Piotr Kubacki
Marcin Przewięźlikowski
Tomasz Kuśmierczyk
Jacek Tabor
Przemysław Spurek

The main goal of Few-Shot learning algorithms is to enable learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). In this paper, we propose a novel framework for Bayesian MAML called BH-MAML, which employs Hypernetworks for weight updates. It learns the universal weights point-wise, but a probabilistic structure is added when adapted for specific tasks. In such a framework, we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data

Patryk Marszałek
Tomasz Kuśmierczyk
Witold Wydmański
Jacek Tabor
Marek Śmieja

Clustering tabular data remains a significant open challenge in data analysis and machine learning. Unlike for image data, similarity between tabular records often varies across datasets, making the definition of clusters highly dataset-dependent. Furthermore, the absence of supervised signals complicates hyperparameter tuning in deep learning clustering methods, frequently resulting in unstable performance. To address these issues and minimize the need for per-dataset tuning, we adopt an emerging approach in deep learning: zero-shot learning. We propose ZEUS, a self-contained model capable of clustering new datasets without any additional training or fine-tuning. It operates by decomposing complex datasets into meaningful components that can then be clustered effectively. Thanks to pre-training on synthetic datasets generated from a latent-variable prior, it generalizes across various datasets without requiring user intervention. To the best of our knowledge, ZEUS is the first zero-shot method capable of generating embeddings for tabular data in a fully unsupervised manner. Experimental results demonstrate that it performs on par with or better than traditional clustering algorithms and recent deep learning-based methods, while being significantly faster and more user-friendly.

PDF Details

JMLR Journal 2023 Journal Article

Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

Eliezer de Souza da Silva
Tomasz Kuśmierczyk
Marcelo Hartmann
Arto Klami

The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters typically selected through Bayesian optimization or cross-validation. This requires repeated, costly, posterior inference. We provide an alternative for selecting good priors without carrying out posterior inference, building on the prior predictive distribution that marginalizes the model parameters. We estimate virtual statistics for data generated by the prior predictive distribution and then optimize over the hyperparameters to learn those for which the virtual statistics match the target values provided by the user or estimated from (a subset of) the observed data. We apply the principle for probabilistic matrix factorization, for which good solutions for prior selection have been missing. We show that for Poisson factorization models we can analytically determine the hyperparameters, including the number of factors, that best replicate the target statistics, and we empirically study the sensitivity of the approach for the model mismatch. We also present a model-independent procedure that determines the hyperparameters for general models by stochastic optimization and demonstrate this extension in the context of hierarchical matrix factorization models. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

PDF Details

AAAI Conference 2020 Conference Paper

Correcting Predictions for Approximate Bayesian Inference

Tomasz Kuśmierczyk
Joseph Sakaya
Arto Klami

Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, conﬁrming its potential.

PDF Details

NeurIPS Conference 2019 Conference Paper

Variational Bayesian Decision-making for Continuous Utilities

Tomasz Kuśmierczyk
Joseph Sakaya
Arto Klami

Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task into account while performing the inference allows for calibrating the posterior approximation to maximize the utility. We present an automatic pipeline that co-opts continuous utilities into variational inference algorithms to account for decision-making. We provide practical strategies for approximating and maximizing the gain, and empirically demonstrate consistent improvement when calibrating approximations for specific utilities.

PDF Details

AAAI Conference 2018 Conference Paper

On Validation and Predictability of Digital Badges’ Influence on Individual Users

Tomasz Kuśmierczyk
Kjetil Nørvåg

Badges are a common, and sometimes the only, method of incentivizing users to perform certain actions on online sites. However, due to many competing factors in- ﬂuencing user temporal dynamics, it is difﬁcult to determine whether the badge had (or will have) the intended effect or not. In this paper, we introduce two complementary approaches for determining badge inﬂuence on users. In the ﬁrst one, we cluster users’ temporal traces (represented with Poisson processes) and apply covariates (user features) to regularize results. In the second approach, we ﬁrst classify users’ temporal traces with a novel statistical framework, and then we reﬁne the classiﬁcation results with a semi-supervised clustering of covariates. Outcomes obtained from an evaluation on synthetic datasets and experiments on two badges from a popular Q&A platform conﬁrm that it is possible to validate, characterize and to some extent predict users affected by the badge.

PDF Details