← Authors

Chris J Maddison

16 papers · 2013–2024 · 1 conference · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (47) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (11) πŸ† Keyword Champion πŸ’Ž Century Club (16) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (97)

Conferences

NIPS (16)

Top co-authors

Chris J. Maddison (5) Daniel Tarlow (4) George Tucker (2) Karen Ullrich (2) John Lawson (2) Nicolas Heess (2) Andriy Mnih (2) Guy Lorberbom (2) Tamir Hazan (2) Leonardo Cotta (2)

Research topics

Probability (1)

Keywords

latent variable model (2) generative model (2) variance reduction (2) learning theory (1) variational inference (1) convex optimization (1) binary classification (1) natural language processing (1) text mining (1) attention mechanism (1) data augmentation (1) sample complexity (1) causal effect estimation (1) universal approximation (1) gradient estimation (1) continuous optimization (1) empirical risk minimization (1) marginal likelihood (1) covariance matrix (1) variational inequality (1)

Papers

On the Efficiency of ERM in Feature Learning NIPS 2024
End-To-End Causal Effect Estimation from Unstructured Natural Language Data NIPS 2024
Observational Scaling Laws and the Predictability of Langauge Model Performance NIPS 2024
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit NIPS 2023
Probabilistic Invariant Learning with Randomized Linear Classifiers NIPS 2023
MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy NIPS 2023
Lossy Compression for Lossless Prediction NIPS 2021
Learning Generalized Gumbel-max Causal Mechanisms NIPS 2021
Gradient Estimation with Stochastic Softmax Tricks NIPS 2020
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces NIPS 2020
Continuous Hierarchical Representations with PoincarΓ© Variational Auto-Encoders NIPS 2019
Hamiltonian descent for composite objectives NIPS 2019
Filtering Variational Objectives NIPS 2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NIPS 2017
A* Sampling NIPS 2014
Annealing between distributions by averaging moments NIPS 2013