no code implementations • 22 Aug 2014 • Zhensong Qian, Oliver Schulte, Yan Sun
With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases.
no code implementations • 28 Oct 2014 • Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick, Xiaoqian Yin, Yan Sun
We describe an approach for learning both the RDN's structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN.
no code implementations • 2 Jul 2015 • Oliver Schulte, Zhensong Qian
To support our position, we have developed the FACTORBASE system, which uses SQL as a high-level scripting language for statistical-relational learning of a graphical model structure.
no code implementations • 10 Aug 2015 • Oliver Schulte, Zhensong Qian
A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database.
no code implementations • 10 Nov 2015 • Jan Motl, Oliver Schulte
The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data.
1 code implementation • 23 Feb 2018 • Oliver Schulte, Yejia Liu, Chao Li
Successful previous approaches have built a predictive model based on player features, or derived performance predictions from the observed performance of comparable players in a cohort.
no code implementations • 26 May 2018 • Guiliang Liu, Oliver Schulte
To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions.
no code implementations • 1 Jul 2018 • Fatemeh Riahi, Oliver Schulte
The metric is based on the likelihood ratio of two parameter vectors: One that represents the population associations, and another that represents the individual associations.
no code implementations • 2 Jul 2018 • Manfred Jaeger, Oliver Schulte
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size.
no code implementations • 16 Jul 2018 • Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li
An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment.
no code implementations • 23 Apr 2020 • Manfred Jaeger, Oliver Schulte
As a by-product we also obtain a characterization for when a given distribution over size-$k$ structures is the statistical frequency distribution of size-$k$ sub-structures in much larger size-$n$ structures.
1 code implementation • 4 Jun 2020 • Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics.
no code implementations • NeurIPS 2020 • Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan
This paper develops a new approach for agent representations, based on a Markov game model, that is tailored towards applications in professional ice hockey.
no code implementations • 29 Jun 2021 • Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte, Parmis Nadaf, Ke Li, Xia Hu
Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.
1 code implementation • 9 Sep 2021 • Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables.
no code implementations • ICLR 2022 • Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart
Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs.
Distributional Reinforcement Learning reinforcement-learning +1
no code implementations • 19 Oct 2021 • Richard Mar, Oliver Schulte
As with propositional (non-relational) graphical models, the major scalability bottleneck for model discovery is computing instantiation counts: the number of times a relational pattern is instantiated in a database.
no code implementations • NeurIPS 2021 • Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart
We propose a Represent And Mimic (RAMi) framework for training 1) an identifiable latent representation to capture the independent factors of variation for the objects and 2) a mimic tree that extracts the causal impact of the latent features on DRL action values.
1 code implementation • 30 Oct 2022 • Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li
We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs.
no code implementations • 15 Feb 2023 • Oliver Schulte
This note describes a new approach to classifying graphs that leverages graph generative models (GGM).
1 code implementation • 17 Feb 2023 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley
First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment.
no code implementations • 1 May 2023 • Oliver Schulte
Estimating the expected value of a graph statistic is an important inference task for using and learning graph models.
no code implementations • 16 Feb 2024 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning.
no code implementations • 7 Mar 2024 • Oliver Schulte, Pascal Poupart
Our main argument is that in online learning, conditional probabilities are causal, and therefore offline RL is the setting where causal learning has the most potential to make a difference.