Tarlow, Daniel

51 publications

ICLRW 2024 Experts Don't Cheat: Learning What You Don't Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

ICML 2024 Experts Don’t Cheat: Learning What You Don’t Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

NeurIPSW 2024 Unlearning In- vs. Out-of-Distribution Data in LLMs Under Gradient-Based Methods Teodora Baluta, Pascal Lamblin, Daniel Tarlow, Fabian Pedregosa, Gintare Karolina Dziugaite

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

ICML 2023 Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

ICLR 2023 Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions David Bieber, Rishab Goel, Dan Zheng, Hugo Larochelle, Daniel Tarlow

ICMLW 2022 Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

ICLRW 2022 Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions David Bieber, Rishab Goel, Dan Zheng, Hugo Larochelle, Daniel Tarlow

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

NeurIPS 2021 Learning Generalized Gumbel-Max Causal Mechanisms Guy Lorberbom, Daniel D. Johnson, Chris J Maddison, Daniel Tarlow, Tamir Hazan

NeurIPS 2021 Learning to Combine Per-Example Solutions for Neural Program Synthesis Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

NeurIPS 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

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

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

NeurIPS 2020 Gradient Estimation with Stochastic SoftMax Tricks Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J Maddison

ICLR 2020 Learning Execution Through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

NeurIPS 2020 Learning Graph Structure with a Finite-State Automaton Layer Daniel Johnson, Hugo Larochelle, Daniel Tarlow

NeurIPS 2020 Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles A. Sutton, Hugo Larochelle, Daniel Tarlow

NeurIPSW 2020 On-the-Fly Adaptation of Source Code Models Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

ICLR 2017 Batch Policy Gradient Methods for Improving Neural Conversation Models Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter

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

ICML 2017 Differentiable Programs with Neural Libraries Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

ICLR 2017 Lifelong Perceptual Programming by Example Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

ICLR 2017 Neural Functional Programming John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

ICLR 2017 Neural Program Lattices Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman

CVPR 2016 Fits like a Glove: Rapid and Reliable Hand Shape Personalization David Joseph Tan, Thomas Cashman, Jonathan Taylor, Andrew Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton

ICLR 2016 Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel

ICML 2015 Bimodal Modelling of Source Code and Natural Language Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei

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

UAI 2015 Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-Cuts Adrian Kim, Kyomin Jung, Yongsub Lim, Daniel Tarlow, Pushmeet Kohli

NeurIPS 2014 A* Sampling Chris J Maddison, Daniel Tarlow, Tom Minka

CVPR 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

NeurIPS 2014 Just-in-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

AISTATS 2014 Learning Structured Models with the AUC Loss and Its Generalizations Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson

ICML 2014 Structured Generative Models of Natural Source Code Chris Maddison, Daniel Tarlow

CVPR 2013 Exploring Compositional High Order Pattern Potentials for Structured Output Learning Yujia Li, Daniel Tarlow, Richard Zemel

NeurIPS 2013 Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn

ICML 2013 Stochastic K-Neighborhood Selection for Supervised and Unsupervised Learning Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Rich Zemel

UAI 2013 Tighter Linear Program Relaxations for High Order Graphical Models Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss

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

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

NeurIPS 2012 Probabilistic N-Choose-K Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams

AISTATS 2012 Randomized Optimum Models for Structured Prediction Daniel Tarlow, Ryan Adams, Richard Zemel

CVPR 2012 Revisiting Uncertainty in Graph Cut Solutions Daniel Tarlow, Ryan Prescott Adams

AISTATS 2012 Structured Output Learning with High Order Loss Functions Daniel Tarlow, Richard Zemel

ICML 2011 Dynamic Tree Block Coordinate Ascent Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov

UAI 2011 Graph Cuts Is a Max-Product Algorithm Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey

AISTATS 2010 HOP-MAP: Efficient Message Passing with High Order Potentials Daniel Tarlow, Inmar Givoni, Richard Zemel

UAI 2008 Flexible Priors for Exemplar-Based Clustering Daniel Tarlow, Richard S. Zemel, Brendan J. Frey

ECCV 2008 Unsupervised Learning of Skeletons from Motion David A. Ross, Daniel Tarlow, Richard S. Zemel

NeurIPS 2006 Using Combinatorial Optimization Within Max-Product Belief Propagation Daniel Tarlow, Gal Elidan, Daphne Koller, John C. Duchi