← Authors

Daniel Tarlow

30 papers · 2006–2024 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) πŸ—ΊοΈ Taxonomy Completionist (11) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (18) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion (2) πŸ’Ž Century Club (30) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner πŸ”₯ Unstoppable (6) πŸ—ƒοΈ Keyword Collector (144)

Conferences

NIPS (14) ICML (7) AISTATS (4) CVPR (3) ICLR (2)

Top co-authors

Hugo Larochelle (5) Daniel D. Johnson (4) Kevin Swersky (4) Richard Zemel (4) Chris J Maddison (4) John Winn (3) Tamir Hazan (2) Richard S. Zemel (2) Ryan P. Adams (2) Guy Lorberbom (2)

Keywords

message passing (4) code generation (3) graph neural network (3) graphical model (3) source code (3) variational inference (3) program understanding (2) high order potential (2) structured output learning (2) combinatorial optimization (2) generative model (2) map inference (2) structured prediction (2) large language model (2) image segmentation (2) probabilistic inference (2) expectation propagation (2) markov random field (2) feature extraction (1) transfer learning (1)

Papers

Experts Don’t Cheat: Learning What You Don’t Know By Predicting Pairs ICML 2024
Repository-Level Prompt Generation for Large Language Models of Code ICML 2023
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents ICML 2023
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions ICLR 2023
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair NIPS 2021
Learning to Combine Per-Example Solutions for Neural Program Synthesis NIPS 2021
Structured Denoising Diffusion Models in Discrete State-Spaces NIPS 2021
Learning Generalized Gumbel-max Causal Mechanisms NIPS 2021
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks NIPS 2020
LEARNING EXECUTION THROUGH NEURAL CODE FUSION ICLR 2020
Learning Graph Structure With A Finite-State Automaton Layer NIPS 2020
Gradient Estimation with Stochastic Softmax Tricks NIPS 2020
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces NIPS 2020
Differentiable Programs with Neural Libraries ICML 2017
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization CVPR 2016
Consensus Message Passing for Layered Graphical Models AISTATS 2015
Bimodal Modelling of Source Code and Natural Language ICML 2015
Structured Generative Models of Natural Source Code ICML 2014
A* Sampling NIPS 2014
Just-In-Time Learning for Fast and Flexible Inference NIPS 2014
Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters CVPR 2014
Exploring Compositional High Order Pattern Potentials for Structured Output Learning CVPR 2013
Learning to Pass Expectation Propagation Messages NIPS 2013
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning ICML 2013
Structured Output Learning with High Order Loss Functions AISTATS 2012
Probabilistic n-Choose-k Models for Classification and Ranking NIPS 2012
Cardinality Restricted Boltzmann Machines NIPS 2012
Randomized Optimum Models for Structured Prediction AISTATS 2012
HOP-MAP: Efficient Message Passing with High Order Potentials AISTATS 2010
Using Combinatorial Optimization within Max-Product Belief Propagation NIPS 2006