Tom Schaul
22 papers · 2010–2025 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (29) π Academic Marathon Β· Full π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π
Triple Crown
π¬
Deep Specialist
(11)
π
Keyword Trendsetter Combo
(3)
π§¬
Topic Evolution
π
Keyword Champion
π§
Keyword Pioneer
π
Conference Pioneer
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(86)
π₯
Unstoppable Β· Bronze
β
The Questioner
Top co-authors
Research topics
Reinforcement Learning
(14)
Machine Learning
(13)
Methods
(13)
Artificial Intelligence
(10)
Core AI
(7)
Learning Types
(6)
Applications
(5)
Optimization & Theory
(5)
Learning Paradigms
(4)
Deep Learning
(3)
Architectures
(2)
Core Methods
(2)
Mathematics & Optimization
(1)
Natural Language Processing
(1)
Application Areas
(1)
Keywords
reinforcement learning
(7)
transfer learning
(3)
value function
(3)
successor feature
(3)
deep reinforcement learning
(2)
sparse reward
(2)
policy improvement
(2)
black-box optimization
(2)
policy gradient
(2)
function approximation
(2)
neural network architecture
(2)
value function approximator
(2)
deep learning
(1)
supervised learning
(1)
policy optimization
(1)
in-context learning
(1)
epistemic uncertainty
(1)
stochastic gradient descent
(1)
stochastic optimization
(1)
bellman equation
(1)
Papers
AuPair: Golden Example Pairs for Code Repair
ICML 2025
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
ICML 2024
Discovering Evolution Strategies via Meta-Black-Box Optimization
ICLR 2023
Scaling Goal-based Exploration via Pruning Proto-goals
IJCAI 2023
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
ICML 2022
The Phenomenon of Policy Churn
NeurIPS 2022
When should agents explore?
ICLR 2022
Conditional Importance Sampling for Off-Policy Learning
AISTATS 2020
Universal Successor Features Approximators
ICLR 2019
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
ICML 2018
FeUdal Networks for Hierarchical Reinforcement Learning
ICML 2017
Natural Value Approximators: Learning when to Trust Past Estimates
NeurIPS 2017
Successor Features for Transfer in Reinforcement Learning
NeurIPS 2017
The Predictron: End-To-End Learning and Planning
ICML 2017
Dueling Network Architectures for Deep Reinforcement Learning
ICML 2016
Learning to learn by gradient descent by gradient descent
NeurIPS 2016
Unifying Count-Based Exploration and Intrinsic Motivation
NeurIPS 2016
Universal Value Function Approximators
ICML 2015
Natural Evolution Strategies
JMLR 2014
Better Generalization with Forecasts
IJCAI 2013
No more pesky learning rates
ICML 2013
PyBrain
JMLR 2010