AI-Assisted Assessment of Coding Practices in Modern Code Review. - Vijayvergiya, Manushree; Salawa, Malgorzata; Budiselic, Ivan; Zheng, Dan; Lamblin, Pascal; Ivankovic, Marko; Carin, Juanjo; Lewko, Mateusz; Andonov, Jovan; Petrovic, Goran; Tarlow, Daniel; Maniatis, Petros; et al. (2024)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. - Johnson, Daniel D.; Tarlow, Daniel; Duvenaud, David; Maddison, Chris J. (2024)

Resolving Code Review Comments with Machine Learning. - Frömmgen, Alexander; Austin, Jacob; Choy, Peter; Ghelani, Nimesh; Kharatyan, Lera; Surita, Gabriela; Khrapko, Elena; Lamblin, Pascal; Manzagol, Pierre-Antoine; Revaj, Marcus; Tabachnyk, Maxim; Tarlow, Daniel; et al. (2024)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. - Johnson, Daniel D.; Tarlow, Daniel; Duvenaud, David; Maddison, Chris J. (CoRR, 2024)

AI-Assisted Assessment of Coding Practices in Modern Code Review. - Vijayvergiya, Manushree; Salawa, Malgorzata; Budiselic, Ivan; Zheng, Dan; Lamblin, Pascal; Ivankovic, Marko; Carin, Juanjo; Lewko, Mateusz; Andonov, Jovan; Petrovic, Goran; Tarlow, Daniel; Maniatis, Petros; et al. (CoRR, 2024)

Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method. - Baluta, Teodora; Lamblin, Pascal; Tarlow, Daniel; Pedregosa, Fabian; Dziugaite, Gintare Karolina (CoRR, 2024)

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. - Bieber, David; Goel, Rishab; Zheng, Daniel; Larochelle, Hugo; Tarlow, Daniel (2023)

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. - Johnson, Daniel D.; Tarlow, Daniel; Walder, Christian (2023)

Repository-Level Prompt Generation for Large Language Models of Code. - Shrivastava, Disha; Larochelle, Hugo; Tarlow, Daniel (2023)

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. - Johnson, Daniel D.; Tarlow, Daniel; Walder, Christian (CoRR, 2023)

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. - Bieber, David; Goel, Rishab; Zheng, Daniel; Larochelle, Hugo; Tarlow, Daniel (CoRR, 2022)

Repository-Level Prompt Generation for Large Language Models of Code. - Shrivastava, Disha; Larochelle, Hugo; Tarlow, Daniel (CoRR, 2022)

Learning to Improve Code Efficiency. - Chen, Binghong; Tarlow, Daniel; Swersky, Kevin; Maas, Martin; Heiber, Pablo Ariel; Naik, Ashish; Hashemi, Milad; Ranganathan, Parthasarathy (CoRR, 2022)

A Library for Representing Python Programs as Graphs for Machine Learning. - Bieber, David; Shi, Kensen; Maniatis, Petros; Sutton, Charles; Hellendoorn, Vincent J.; Johnson, Daniel D.; Tarlow, Daniel (CoRR, 2022)

Learning to Combine Per-Example Solutions for Neural Program Synthesis. - Shrivastava, Disha; Larochelle, Hugo; Tarlow, Daniel (2021)

Structured Denoising Diffusion Models in Discrete State-Spaces. - Austin, Jacob; Johnson, Daniel D.; Ho, Jonathan; Tarlow, Daniel; Berg, Rianne van den (2021)

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair. - Chen, Zimin; Hellendoorn, Vincent J.; Lamblin, Pascal; Maniatis, Petros; Manzagol, Pierre-Antoine; Tarlow, Daniel; Moitra, Subhodeep (2021)

Learning Generalized Gumbel-max Causal Mechanisms. - Lorberbom, Guy; Johnson, Daniel D.; Maddison, Chris J.; Tarlow, Daniel; Hazan, Tamir (2021)

Learning to Extend Program Graphs to Work-in-Progress Code. - Li, Xuechen; Maddison, Chris J.; Tarlow, Daniel (CoRR, 2021)

Learning to Combine Per-Example Solutions for Neural Program Synthesis. - Shrivastava, Disha; Larochelle, Hugo; Tarlow, Daniel (CoRR, 2021)

Structured Denoising Diffusion Models in Discrete State-Spaces. - Austin, Jacob; Johnson, Daniel D.; Ho, Jonathan; Tarlow, Daniel; Berg, Rianne van den (CoRR, 2021)

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models. - Johnson, Daniel D.; Austin, Jacob; Berg, Rianne van den; Tarlow, Daniel (CoRR, 2021)

Learning Generalized Gumbel-max Causal Mechanisms. - Lorberbom, Guy; Johnson, Daniel D.; Maddison, Chris J.; Tarlow, Daniel; Hazan, Tamir (CoRR, 2021)

Learning Execution through Neural Code fusion. - Shi, Zhan; Swersky, Kevin; Tarlow, Daniel; Ranganathan, Parthasarathy; Hashemi, Milad (2020)

Learning to Fix Build Errors with Graph2Diff Neural Networks. - Tarlow, Daniel; Moitra, Subhodeep; Rice, Andrew; Chen, Zimin; Manzagol, Pierre-Antoine; Sutton, Charles; Aftandilian, Edward (2020)

Learning Graph Structure With A Finite-State Automaton Layer. - Johnson, Daniel D.; Larochelle, Hugo; Tarlow, Daniel (2020)

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. - Bieber, David; Sutton, Charles; Larochelle, Hugo; Tarlow, Daniel (2020)

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. - Lorberbom, Guy; Maddison, Chris J.; Heess, Nicolas; Hazan, Tamir; Tarlow, Daniel (2020)

Gradient Estimation with Stochastic Softmax Tricks. - Paulus, Max B.; Choi, Dami; Tarlow, Daniel; Krause, Andreas; Maddison, Chris J. (2020)

Tabular: Probabilistic Inference from the Spreadsheet. - Gordon, Andrew D.; Russo, Claudio V.; Szymczak, Marcin; Borgström, Johannes; Rolland, Nicolas; Graepel, Thore; Tarlow, Daniel (2020)

Gradient Estimation with Stochastic Softmax Tricks. - Paulus, Max B.; Choi, Dami; Tarlow, Daniel; Krause, Andreas; Maddison, Chris J. (CoRR, 2020)

Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs. - Ahrabian, Kian; Tarlow, Daniel; Cheng, Hehuimin; Guo, Jin L. C. (CoRR, 2020)

Learning Graph Structure With A Finite-State Automaton Layer. - Johnson, Daniel D.; Larochelle, Hugo; Tarlow, Daniel (CoRR, 2020)

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. - Bieber, David; Sutton, Charles; Larochelle, Hugo; Tarlow, Daniel (CoRR, 2020)

Neural Networks for Modeling Source Code Edits. - Zhao, Rui; Bieber, David; Swersky, Kevin; Tarlow, Daniel (CoRR, 2019)

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. - Lorberbom, Guy; Maddison, Chris J.; Heess, Nicolas; Hazan, Tamir; Tarlow, Daniel (CoRR, 2019)

Learning Execution through Neural Code Fusion. - Shi, Zhan; Swersky, Kevin; Tarlow, Daniel; Ranganathan, Parthasarathy; Hashemi, Milad (CoRR, 2019)

Fast Training of Sparse Graph Neural Networks on Dense Hardware. - Balog, Matej; Merriënboer, Bart van; Moitra, Subhodeep; Li, Yujia; Tarlow, Daniel (CoRR, 2019)

Learning to Fix Build Errors with Graph2Diff Neural Networks. - Tarlow, Daniel; Moitra, Subhodeep; Rice, Andrew; Chen, Zimin; Manzagol, Pierre-Antoine; Sutton, Charles; Aftandilian, Edward (CoRR, 2019)

Graph Partition Neural Networks for Semi-Supervised Classification. - Liao, Renjie; Brockschmidt, Marc; Tarlow, Daniel; Gaunt, Alexander L.; Urtasun, Raquel; Zemel, Richard S. (2018)

Graph Partition Neural Networks for Semi-Supervised Classification. - Liao, Renjie; Brockschmidt, Marc; Tarlow, Daniel; Gaunt, Alexander L.; Urtasun, Raquel; Zemel, Richard S. (CoRR, 2018)

Empirical Minimum Bayes Risk Prediction. - Premachandran, Vittal; Tarlow, Daniel; Yuille, Alan L.; Batra, Dhruv (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

DeepCoder: Learning to Write Programs. - Balog, Matej; Gaunt, Alexander L.; Brockschmidt, Marc; Nowozin, Sebastian; Tarlow, Daniel (2017)

Neural Functional Programming. - Feser, John K.; Brockschmidt, Marc; Gaunt, Alexander L.; Tarlow, Daniel (2017)

Lifelong Perceptual Programming By Example. - Gaunt, Alexander L.; Brockschmidt, Marc; Kushman, Nate; Tarlow, Daniel (2017)

Batch Policy Gradient Methods for Improving Neural Conversation Models. - Kandasamy, Kirthevasan; Bachrach, Yoram; Tomioka, Ryota; Tarlow, Daniel; Carter, David (2017)

Neural Program Lattices. - Li, Chengtao; Tarlow, Daniel; Gaunt, Alexander L.; Brockschmidt, Marc; Kushman, Nate (2017)

Differentiable Programs with Neural Libraries. - Gaunt, Alexander L.; Brockschmidt, Marc; Kushman, Nate; Tarlow, Daniel (2017)

Learning Shape Analysis. - Brockschmidt, Marc; Chen, Yuxin; Kohli, Pushmeet; Krishna, Siddharth; Tarlow, Daniel (2017)

Batch Policy Gradient Methods for Improving Neural Conversation Models. - Kandasamy, Kirthevasan; Bachrach, Yoram; Tomioka, Ryota; Tarlow, Daniel; Carter, David (CoRR, 2017)

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks. - Gaunt, Alex; Johnson, Matthew; Riechert, Maik; Tarlow, Daniel; Tomioka, Ryota; Vytiniotis, Dimitrios; Webster, Sam (CoRR, 2017)

Fits Like a Glove: Rapid and Reliable Hand Shape Personalization. - Tan, David Joseph; Cashman, Thomas J.; Taylor, Jonathan; Fitzgibbon, Andrew W.; Tarlow, Daniel; Khamis, Sameh; Izadi, Shahram; Shotton, Jamie (2016)

Gated Graph Sequence Neural Networks. - Li, Yujia; Tarlow, Daniel; Brockschmidt, Marc; Zemel, Richard S. (2016)

TerpreT: A Probabilistic Programming Language for Program Induction. - Gaunt, Alexander L.; Brockschmidt, Marc; Singh, Rishabh; Kushman, Nate; Kohli, Pushmeet; Taylor, Jonathan; Tarlow, Daniel (CoRR, 2016)

Neural Functional Programming. - Feser, John K.; Brockschmidt, Marc; Gaunt, Alexander L.; Tarlow, Daniel (CoRR, 2016)

DeepCoder: Learning to Write Programs. - Balog, Matej; Gaunt, Alexander L.; Brockschmidt, Marc; Nowozin, Sebastian; Tarlow, Daniel (CoRR, 2016)

Lifelong Perceptual Programming By Example. - Gaunt, Alexander L.; Brockschmidt, Marc; Kushman, Nate; Tarlow, Daniel (CoRR, 2016)

Summary - TerpreT: A Probabilistic Programming Language for Program Induction. - Gaunt, Alexander L.; Brockschmidt, Marc; Singh, Rishabh; Kushman, Nate; Kohli, Pushmeet; Taylor, Jonathan; Tarlow, Daniel (CoRR, 2016)

Consensus Message Passing for Layered Graphical Models. - Jampani, Varun; Eslami, S. M. Ali; Tarlow, Daniel; Kohli, Pushmeet; Winn, John M. (2015)

Probabilistic Programs as Spreadsheet Queries. - Gordon, Andrew D.; Russo, Claudio V.; Szymczak, Marcin; Borgström, Johannes; Rolland, Nicolas; Graepel, Thore; Tarlow, Daniel (2015)

Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs. - Ahmed, Faruk; Tarlow, Daniel; Batra, Dhruv (2015)

Bimodal Modelling of Source Code and Natural Language. - Allamanis, Miltiadis; Tarlow, Daniel; Gordon, Andrew D.; Wei, Yi (2015)

Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts. - Kim, Adrian; Jung, Kyomin; Lim, Yongsub; Tarlow, Daniel; Kohli, Pushmeet (2015)

Learning Structured Models with the AUC Loss and Its Generalizations. - Rosenfeld, Nir; Meshi, Ofer; Tarlow, Daniel; Globerson, Amir (2014)

Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters. - Premachandran, Vittal; Tarlow, Daniel; Batra, Dhruv (2014)

Structured Generative Models of Natural Source Code. - Maddison, Chris J.; Tarlow, Daniel (2014)

Just-In-Time Learning for Fast and Flexible Inference. - Eslami, S. M. Ali; Tarlow, Daniel; Kohli, Pushmeet; Winn, John M. (2014)

A* Sampling. - Maddison, Chris J.; Tarlow, Daniel; Minka, Tom (2014)

Structured Generative Models of Natural Source Code. - Maddison, Chris J.; Tarlow, Daniel (CoRR, 2014)

Consensus Message Passing for Layered Graphical Models. - Jampani, Varun; Eslami, S. M. Ali; Tarlow, Daniel; Kohli, Pushmeet; Winn, John M. (CoRR, 2014)

Candidate Constrained CRFs for Loss-Aware Structured Prediction. - Ahmed, Faruk; Tarlow, Daniel; Batra, Dhruv (CoRR, 2014)

Efficient Machine Learning with High Order and Combinatorial Structures. - Tarlow, Daniel (2013)

Exploring Compositional High Order Pattern Potentials for Structured Output Learning. - Li, Yujia; Tarlow, Daniel; Zemel, Richard S. (2013)

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. - Tarlow, Daniel; Swersky, Kevin; Charlin, Laurent; Sutskever, Ilya; Zemel, Richard S. (2013)

Learning to Pass Expectation Propagation Messages. - Heess, Nicolas; Tarlow, Daniel; Winn, John M. (2013)

Tighter Linear Program Relaxations for High Order Graphical Models. - Mezuman, Elad; Tarlow, Daniel; Globerson, Amir; Weiss, Yair (2013)

Tighter Linear Program Relaxations for High Order Graphical Models. - Mezuman, Elad; Tarlow, Daniel; Globerson, Amir; Weiss, Yair (CoRR, 2013)

Revisiting uncertainty in graph cut solutions. - Tarlow, Daniel; Adams, Ryan Prescott (2012)

Probabilistic n-Choose-k Models for Classification and Ranking. - Swersky, Kevin; Tarlow, Daniel; Adams, Ryan P.; Zemel, Richard S.; Frey, Brendan J. (2012)

Cardinality Restricted Boltzmann Machines. - Swersky, Kevin; Tarlow, Daniel; Sutskever, Ilya; Salakhutdinov, Ruslan; Zemel, Richard S.; Adams, Ryan P. (2012)

Fast Exact Inference for Recursive Cardinality Models. - Tarlow, Daniel; Swersky, Kevin; Zemel, Richard S.; Adams, Ryan Prescott; Frey, Brendan J. (2012)

Structured Output Learning with High Order Loss Functions. - Tarlow, Daniel; Zemel, Richard S. (2012)

Randomized Optimum Models for Structured Prediction. - Tarlow, Daniel; Adams, Ryan Prescott; Zemel, Richard S. (2012)

Flexible Priors for Exemplar-based Clustering - Tarlow, Daniel; Zemel, Richard S.; Frey, Brendan J. (CoRR, 2012)

Fast Exact Inference for Recursive Cardinality Models - Tarlow, Daniel; Swersky, Kevin; Zemel, Richard S.; Adams, Ryan Prescott; Frey, Brendan J. (CoRR, 2012)

Dynamic Tree Block Coordinate Ascent. - Tarlow, Daniel; Batra, Dhruv; Kohli, Pushmeet; Kolmogorov, Vladimir (2011)

Graph Cuts is a Max-Product Algorithm. - Tarlow, Daniel; Givoni, Inmar E.; Zemel, Richard S.; Frey, Brendan J. (2011)

Interpreting Graph Cuts as a Max-Product Algorithm - Tarlow, Daniel; Givoni, Inmar E.; Zemel, Richard S.; Frey, Brendan J. (CoRR, 2011)

Learning Articulated Structure and Motion. - Ross, David A.; Tarlow, Daniel; Zemel, Richard S. (Int. J. Comput. Vis., 2010)

HOP-MAP: Efficient Message Passing with High Order Potentials. - Tarlow, Daniel; Givoni, Inmar E.; Zemel, Richard S. (2010)

Unsupervised Learning of Skeletons from Motion. - Ross, David A.; Tarlow, Daniel; Zemel, Richard S. (2008)

Flexible Priors for Exemplar-based Clustering. - Tarlow, Daniel; Zemel, Richard S.; Frey, Brendan J. (2008)

Using Combinatorial Optimization within Max-Product Belief Propagation. - Duchi, John C.; Tarlow, Daniel; Elidan, Gal; Koller, Daphne (2006)