Search Results for author: Oliver Schulte

Found 24 papers, 5 papers with code

NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge

1 code implementation9 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.

Time Series Time Series Analysis

Model Trees for Identifying Exceptional Players in the NHL Draft

1 code implementation23 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.

Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders

1 code implementation30 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.

Graph Generation

Cracking the Black Box: Distilling Deep Sports Analytics

1 code implementation4 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.

Sports Analytics

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting

1 code implementation17 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.

Motion Forecasting Representation Learning +2

Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

no code implementations26 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.

reinforcement-learning Reinforcement Learning (RL)

The CTU Prague Relational Learning Repository

no code implementations10 Nov 2015 Jan Motl, Oliver Schulte

The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data.

BIG-bench Machine Learning Relational Reasoning

FactorBase: SQL for Learning A Multi-Relational Graphical Model

no code implementations10 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.

Management Model Discovery +2

SQL for SRL: Structure Learning Inside a Database System

no code implementations2 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.

Management Philosophy +2

Fast Learning of Relational Dependency Networks

no code implementations28 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.

Computing Multi-Relational Sufficient Statistics for Large Databases

no code implementations22 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.

feature selection

Inference, Learning, and Population Size: Projectivity for SRL Models

no code implementations2 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.

Model-based Exception Mining for Object-Relational Data

no code implementations1 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.

Object Outlier Detection

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

no code implementations16 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.

reinforcement-learning Reinforcement Learning (RL)

A Complete Characterization of Projectivity for Statistical Relational Models

no code implementations23 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.

Relational Reasoning

Learning Agent Representations for Ice Hockey

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.

Sports Analytics

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

no code implementations29 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.

Graph Representation Learning

Distributional Reinforcement Learning with Monotonic Splines

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

Pre and Post Counting for Scalable Statistical-Relational Model Discovery

no code implementations19 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.

Model Discovery Relational Reasoning

Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL)

From Graph Generation to Graph Classification

no code implementations15 Feb 2023 Oliver Schulte

This note describes a new approach to classifying graphs that leverages graph generative models (GGM).

Graph Classification Graph Generation

Computing Expected Motif Counts for Exchangeable Graph Generative Models

no code implementations1 May 2023 Oliver Schulte

Estimating the expected value of a graph statistic is an important inference task for using and learning graph models.

Vocal Bursts Type Prediction

Disentanglement in Implicit Causal Models via Switch Variable

no code implementations16 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.

Disentanglement

Why Online Reinforcement Learning is Causal

no code implementations7 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.

counterfactual Offline RL +2

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