Search Results for author: Oliver Schulte

Found 18 papers, 3 papers with code

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

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

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

NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge

1 code implementation9 Sep 2021 Xiangyu Sun, Guiliang Liu, Pascal Poupart, Oliver Schulte

We propose a score-based DAG structure learning method for time-series data that captures linear, nonlinear, lagged and instantaneous relations among variables while ensuring acyclicity throughout the entire graph.

Time Series

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

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

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

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

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

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.

Outlier Detection

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

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.

The CTU Prague Relational Learning Repository

no code implementations10 Nov 2015 Jan Motl, Oliver Schulte

A searchable meta-database provides metadata (e. g., the number of tables in the database, the number of rows and columns in the tables, the number of foreign key constraints between tables).

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.

Benchmark Model Discovery +1

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.

Benchmark Relational Reasoning

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.

Benchmark

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

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