Daniel Tarlow

According to our database, Daniel Tarlow authored at least 67 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Resolving Code Review Comments with Machine Learning.

Alexander Frömmgen, Jacob Austin, Peter Choy, Nimesh Ghelani, Lera Kharatyan, Gabriela Surita, Elena Khrapko, Pascal Lamblin, Pierre-Antoine Manzagol, Marcus Revaj, Maxim Tabachnyk, Daniel Tarlow, Kevin Villela, Daniel Zheng, Satish Chandra, Petros Maniatis

Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice, 2024

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.

Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

Proceedings of the Forty-first International Conference on Machine Learning, 2024

AI-Assisted Assessment of Coding Practices in Modern Code Review.

Manushree Vijayvergiya, Malgorzata Salawa, Ivan Budiselic, Dan Zheng, Pascal Lamblin, Marko Ivankovic, Juanjo Carin, Mateusz Lewko, Jovan Andonov, Goran Petrovic, Daniel Tarlow, Petros Maniatis, René Just

Proceedings of the 1st ACM International Conference on AI-Powered Software, 2024

2023

Repository-Level Prompt Generation for Large Language Models of Code.

Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

Proceedings of the International Conference on Machine Learning, 2023

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents.

Daniel D. Johnson, Daniel Tarlow, Christian Walder

Proceedings of the International Conference on Machine Learning, 2023

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions.

David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Library for Representing Python Programs as Graphs for Machine Learning.

David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow

CoRR, 2022

Learning to Improve Code Efficiency.

Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

CoRR, 2022

2021

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models.

Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

CoRR, 2021

Learning to Extend Program Graphs to Work-in-Progress Code.

Xuechen Li, Chris J. Maddison, Daniel Tarlow

CoRR, 2021

Learning to Combine Per-Example Solutions for Neural Program Synthesis.

Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Generalized Gumbel-max Causal Mechanisms.

Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair.

Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Structured Denoising Diffusion Models in Discrete State-Spaces.

Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs.

Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng, Jin L. C. Guo

CoRR, 2020

Gradient Estimation with Stochastic Softmax Tricks.

Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.

Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.

David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Graph Structure With A Finite-State Automaton Layer.

Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Fix Build Errors with Graph2Diff Neural Networks.

Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian

Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Workshops, Seoul, Republic of Korea, 27 June, 2020

Learning Execution through Neural Code fusion.

Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Fast Training of Sparse Graph Neural Networks on Dense Hardware.

Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow

CoRR, 2019

Neural Networks for Modeling Source Code Edits.

Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow

CoRR, 2019

2018

Graph Partition Neural Networks for Semi-Supervised Classification.

Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Empirical Minimum Bayes Risk Prediction.

Vittal Premachandran, Daniel Tarlow, Alan L. Yuille, Dhruv Batra

IEEE Trans. Pattern Anal. Mach. Intell., 2017

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks.

Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster

CoRR, 2017

Learning Shape Analysis.

Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow

Proceedings of the Static Analysis - 24th International Symposium, 2017

Differentiable Programs with Neural Libraries.

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

Proceedings of the 34th International Conference on Machine Learning, 2017

Neural Program Lattices.

Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman

Proceedings of the 5th International Conference on Learning Representations, 2017

Batch Policy Gradient Methods for Improving Neural Conversation Models.

Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter

Proceedings of the 5th International Conference on Learning Representations, 2017

Lifelong Perceptual Programming By Example.

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

Neural Functional Programming.

John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

DeepCoder: Learning to Write Programs.

Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Gated Graph Sequence Neural Networks.

Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel

Proceedings of the 4th International Conference on Learning Representations, 2016

Summary - TerpreT: A Probabilistic Programming Language for Program Induction.

Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

CoRR, 2016

TerpreT: A Probabilistic Programming Language for Program Induction.

Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

CoRR, 2016

Fits Like a Glove: Rapid and Reliable Hand Shape Personalization.

David Joseph Tan, Thomas J. Cashman, Jonathan Taylor, Andrew W. Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts.

Adrian Kim, Kyomin Jung, Yongsub Lim, Daniel Tarlow, Pushmeet Kohli

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Bimodal Modelling of Source Code and Natural Language.

Miltiadis Allamanis, Daniel Tarlow, Andrew D. Gordon, Yi Wei

Proceedings of the 32nd International Conference on Machine Learning, 2015

Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs.

Faruk Ahmed, Daniel Tarlow, Dhruv Batra

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Probabilistic Programs as Spreadsheet Queries.

Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow

Proceedings of the Programming Languages and Systems, 2015

Consensus Message Passing for Layered Graphical Models.

Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Candidate Constrained CRFs for Loss-Aware Structured Prediction.

Faruk Ahmed, Daniel Tarlow, Dhruv Batra

CoRR, 2014

A* Sampling.

Chris J. Maddison, Daniel Tarlow, Tom Minka

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Just-In-Time Learning for Fast and Flexible Inference.

S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Structured Generative Models of Natural Source Code.

Chris J. Maddison, Daniel Tarlow

Proceedings of the 31th International Conference on Machine Learning, 2014

Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters.

Vittal Premachandran, Daniel Tarlow, Dhruv Batra

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Learning Structured Models with the AUC Loss and Its Generalizations.

Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Efficient Machine Learning with High Order and Combinatorial Structures.

Daniel Tarlow

PhD thesis, 2013

Tighter Linear Program Relaxations for High Order Graphical Models.

Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Learning to Pass Expectation Propagation Messages.

Nicolas Heess, Daniel Tarlow, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning.

Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel

Proceedings of the 30th International Conference on Machine Learning, 2013

Exploring Compositional High Order Pattern Potentials for Structured Output Learning.

Yujia Li, Daniel Tarlow, Richard S. Zemel

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Structured Output Learning with High Order Loss Functions.

Daniel Tarlow, Richard S. Zemel

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Randomized Optimum Models for Structured Prediction.

Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Fast Exact Inference for Recursive Cardinality Models.

Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Cardinality Restricted Boltzmann Machines.

Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Probabilistic n-Choose-k Models for Classification and Ranking.

Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Revisiting uncertainty in graph cut solutions.

Daniel Tarlow, Ryan Prescott Adams

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011

Interpreting Graph Cuts as a Max-Product Algorithm

Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey

CoRR, 2011

Graph Cuts is a Max-Product Algorithm.

Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey

Proceedings of the UAI 2011, 2011

Dynamic Tree Block Coordinate Ascent.

Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

HOP-MAP: Efficient Message Passing with High Order Potentials.

Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning Articulated Structure and Motion.

David A. Ross, Daniel Tarlow, Richard S. Zemel

Int. J. Comput. Vis., 2010

2008

Flexible Priors for Exemplar-based Clustering.

Daniel Tarlow, Richard S. Zemel, Brendan J. Frey

Proceedings of the UAI 2008, 2008

Unsupervised Learning of Skeletons from Motion.

David A. Ross, Daniel Tarlow, Richard S. Zemel

Proceedings of the Computer Vision, 2008

2006

Using Combinatorial Optimization within Max-Product Belief Propagation.

John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 19, 2006