Daniel Tarlow

93 papers A* 23A 11B 1Journal 39Unranked 17
YearRankTypeTitle / Venue / Authors
2024 — conf
AI-Assisted Assessment of Coding Practices in Modern Code Review.
AIware
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
2024 J jnl
AI-Assisted Assessment of Coding Practices in Modern Code Review.
CoRR
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
2024 A* conf
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.
ICML
Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
2024 J jnl
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.
CoRR
Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
2024 — conf
Resolving Code Review Comments with Machine Learning.
ICSE-SEIP
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
2024 J jnl
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method.
CoRR
Teodora Baluta, Pascal Lamblin, Daniel Tarlow, Fabian Pedregosa, Gintare Karolina Dziugaite
2023 A* conf
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents.
ICML
Daniel D. Johnson, Daniel Tarlow, Christian Walder
2023 J jnl
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents.
CoRR
Daniel D. Johnson, Daniel Tarlow, Christian Walder
2023 A* conf
Repository-Level Prompt Generation for Large Language Models of Code.
ICML
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
2023 A* conf
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions.
ICLR
David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
2022 J jnl
A Library for Representing Python Programs as Graphs for Machine Learning.
CoRR
David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow
2022 J jnl
Learning to Improve Code Efficiency.
CoRR
Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan
2022 J jnl
Repository-Level Prompt Generation for Large Language Models of Code.
CoRR
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
2022 J jnl
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions.
CoRR
David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
2021 J jnl
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models.
CoRR
Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow
2021 A* conf
Learning Generalized Gumbel-max Causal Mechanisms.
NeurIPS
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan
2021 J jnl
Learning Generalized Gumbel-max Causal Mechanisms.
CoRR
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan
2021 A* conf
Learning to Combine Per-Example Solutions for Neural Program Synthesis.
NeurIPS
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
2021 J jnl
Learning to Combine Per-Example Solutions for Neural Program Synthesis.
CoRR
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
2021 J jnl
Learning to Extend Program Graphs to Work-in-Progress Code.
CoRR
Xuechen Li, Chris J. Maddison, Daniel Tarlow
2021 A* conf
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair.
NeurIPS
Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
2021 A* conf
Structured Denoising Diffusion Models in Discrete State-Spaces.
NeurIPS
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg
2021 J jnl
Structured Denoising Diffusion Models in Discrete State-Spaces.
CoRR
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg
2020 A* conf
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.
NeurIPS
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
2020 A* conf
Gradient Estimation with Stochastic Softmax Tricks.
NeurIPS
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020 J jnl
Gradient Estimation with Stochastic Softmax Tricks.
CoRR
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020 A* conf
Learning Execution through Neural Code fusion.
ICLR
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2020 A* conf
Learning Graph Structure With A Finite-State Automaton Layer.
NeurIPS
Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
2020 J jnl
Learning Graph Structure With A Finite-State Automaton Layer.
CoRR
Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
2020 A* conf
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
NeurIPS
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
2020 J jnl
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
CoRR
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
2020 — conf
Learning to Fix Build Errors with Graph2Diff Neural Networks.
ICSE (Workshops)
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
2020 J jnl
Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs.
CoRR
Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng, Jin L. C. Guo
2020 — ch.
Tabular: Probabilistic Inference from the Spreadsheet.
Foundations of Probabilistic Programming
Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow
2019 J jnl
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.
CoRR
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
2019 J jnl
Fast Training of Sparse Graph Neural Networks on Dense Hardware.
CoRR
Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow
2019 J jnl
Learning Execution through Neural Code Fusion.
CoRR
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2019 J jnl
Learning to Fix Build Errors with Graph2Diff Neural Networks.
CoRR
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
2019 J jnl
Neural Networks for Modeling Source Code Edits.
CoRR
Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
2018 — conf
Graph Partition Neural Networks for Semi-Supervised Classification.
ICLR (Workshop)
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
2018 J jnl
Graph Partition Neural Networks for Semi-Supervised Classification.
CoRR
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
2017 J jnl
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks.
CoRR
Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
2017 — conf
Batch Policy Gradient Methods for Improving Neural Conversation Models.
ICLR (Poster)
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017 J jnl
Batch Policy Gradient Methods for Improving Neural Conversation Models.
CoRR
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017 — conf
DeepCoder: Learning to Write Programs.
ICLR (Poster)
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
2017 A* conf
Differentiable Programs with Neural Libraries.
ICML
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
2017 J jnl
Empirical Minimum Bayes Risk Prediction.
IEEE Trans. Pattern Anal. Mach. Intell.
Vittal Premachandran, Daniel Tarlow, Alan L. Yuille, Dhruv Batra
2017 B conf
Learning Shape Analysis.
SAS
Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow
2017 — conf
Lifelong Perceptual Programming By Example.
ICLR (Workshop)
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
2017 — conf
Neural Functional Programming.
ICLR (Workshop)
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
2017 — conf
Neural Program Lattices.
ICLR (Poster)
Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman
2016 J jnl
DeepCoder: Learning to Write Programs.
CoRR
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
2016 A* conf
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization.
CVPR
David Joseph Tan, Thomas J. Cashman, Jonathan Taylor, Andrew W. Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton
2016 A* conf
Gated Graph Sequence Neural Networks.
ICLR (Poster)
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel
2016 J jnl
Lifelong Perceptual Programming By Example.
CoRR
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
2016 J jnl
Neural Functional Programming.
CoRR
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
2016 J jnl
Summary - TerpreT: A Probabilistic Programming Language for Program Induction.
CoRR
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow
2016 J jnl
TerpreT: A Probabilistic Programming Language for Program Induction.
CoRR
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow
2015 A* conf
Bimodal Modelling of Source Code and Natural Language.
ICML
Miltiadis Allamanis, Daniel Tarlow, Andrew D. Gordon, Yi Wei
2015 A conf
Consensus Message Passing for Layered Graphical Models.
AISTATS
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2015 A conf
Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts.
UAI
Adrian Kim, Kyomin Jung, Yongsub Lim, Daniel Tarlow, Pushmeet Kohli
2015 A* conf
Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs.
ICCV
Faruk Ahmed, Daniel Tarlow, Dhruv Batra
2015 A conf
Probabilistic Programs as Spreadsheet Queries.
ESOP
Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow
2014 — conf
A* Sampling.
NIPS
Chris J. Maddison, Daniel Tarlow, Tom Minka
2014 J jnl
Candidate Constrained CRFs for Loss-Aware Structured Prediction.
CoRR
Faruk Ahmed, Daniel Tarlow, Dhruv Batra
2014 J jnl
Consensus Message Passing for Layered Graphical Models.
CoRR
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2014 A* conf
Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters.
CVPR
Vittal Premachandran, Daniel Tarlow, Dhruv Batra
2014 — conf
Just-In-Time Learning for Fast and Flexible Inference.
NIPS
S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2014 A conf
Learning Structured Models with the AUC Loss and Its Generalizations.
AISTATS
Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson
2014 A* conf
Structured Generative Models of Natural Source Code.
ICML
Chris J. Maddison, Daniel Tarlow
2014 J jnl
Structured Generative Models of Natural Source Code.
CoRR
Chris J. Maddison, Daniel Tarlow
2013 — —
Efficient Machine Learning with High Order and Combinatorial Structures.
Daniel Tarlow
2013 A* conf
Exploring Compositional High Order Pattern Potentials for Structured Output Learning.
CVPR
Yujia Li, Daniel Tarlow, Richard S. Zemel
2013 — conf
Learning to Pass Expectation Propagation Messages.
NIPS
Nicolas Heess, Daniel Tarlow, John M. Winn
2013 — conf
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning.
ICML (3)
Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
2013 A conf
Tighter Linear Program Relaxations for High Order Graphical Models.
UAI
Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss
2013 J jnl
Tighter Linear Program Relaxations for High Order Graphical Models.
CoRR
Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss
2012 — conf
Cardinality Restricted Boltzmann Machines.
NIPS
Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
2012 J jnl
Fast Exact Inference for Recursive Cardinality Models
CoRR
Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012 A conf
Fast Exact Inference for Recursive Cardinality Models.
UAI
Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012 J jnl
Flexible Priors for Exemplar-based Clustering
CoRR
Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
2012 — conf
Probabilistic n-Choose-k Models for Classification and Ranking.
NIPS
Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey
2012 A conf
Randomized Optimum Models for Structured Prediction.
AISTATS
Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel
2012 A* conf
Revisiting uncertainty in graph cut solutions.
CVPR
Daniel Tarlow, Ryan Prescott Adams
2012 A conf
Structured Output Learning with High Order Loss Functions.
AISTATS
Daniel Tarlow, Richard S. Zemel
2011 A* conf
Dynamic Tree Block Coordinate Ascent.
ICML
Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov
2011 A conf
Graph Cuts is a Max-Product Algorithm.
UAI
Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
2011 J jnl
Interpreting Graph Cuts as a Max-Product Algorithm
CoRR
Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
2010 A conf
HOP-MAP: Efficient Message Passing with High Order Potentials.
AISTATS
Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel
2010 J jnl
Learning Articulated Structure and Motion.
Int. J. Comput. Vis.
David A. Ross, Daniel Tarlow, Richard S. Zemel
2008 A conf
Flexible Priors for Exemplar-based Clustering.
UAI
Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
2008 — conf
Unsupervised Learning of Skeletons from Motion.
ECCV (3)
David A. Ross, Daniel Tarlow, Richard S. Zemel
2006 — conf
Using Combinatorial Optimization within Max-Product Belief Propagation.
NIPS
John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller