Search Results for author: Matej Balog

Found 10 papers, 5 papers with code

Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

no code implementations6 Nov 2023 Abbas Mehrabian, Ankit Anand, Hyunjik Kim, Nicolas Sonnerat, Matej Balog, Gheorghe Comanici, Tudor Berariu, Andrew Lee, Anian Ruoss, Anna Bulanova, Daniel Toyama, Sam Blackwell, Bernardino Romera Paredes, Petar Veličković, Laurent Orseau, Joonkyung Lee, Anurag Murty Naredla, Doina Precup, Adam Zsolt Wagner

This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.

Decision Making Graph Generation

Discovering faster matrix multiplication algorithms with reinforcement learning

2 code implementations Nature 2022 Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, Pushmeet Kohli

Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.

reinforcement-learning Reinforcement Learning (RL)

Neural Program Synthesis with a Differentiable Fixer

no code implementations19 Jun 2020 Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton

We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer.

Program Synthesis

Fast Training of Sparse Graph Neural Networks on Dense Hardware

no code implementations27 Jun 2019 Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow

Graph neural networks have become increasingly popular in recent years due to their ability to naturally encode relational input data and their ability to scale to large graphs by operating on a sparse representation of graph adjacency matrices.

Differentially Private Database Release via Kernel Mean Embeddings

1 code implementation ICML 2018 Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf

First, releasing (an estimate of) the kernel mean embedding of the data generating random variable instead of the database itself still allows third-parties to construct consistent estimators of a wide class of population statistics.

Lost Relatives of the Gumbel Trick

1 code implementation ICML 2017 Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller

We show how a subfamily of our new methods adapts to this setting, proving new upper and lower bounds on the log partition function and deriving a family of sequential samplers for the Gibbs distribution.

DeepCoder: Learning to Write Programs

3 code implementations7 Nov 2016 Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

Enumerative Search

The Mondrian Kernel

no code implementations16 Jun 2016 Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel.

The Mondrian Process for Machine Learning

1 code implementation18 Jul 2015 Matej Balog, Yee Whye Teh

We outline a slight adaptation of this algorithm to regression, as the remainder of the report uses regression as a case study of how Mondrian processes can be utilized in machine learning.

BIG-bench Machine Learning regression

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