← Authors

Emma Brunskill

64 papers · 2009–2026 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (10) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (16) πŸ—ΊοΈ Taxonomy Completionist (22) 🏠 Conference Loyalist (27) 🌟 Keyword Trendsetter Combo (3) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion (5) πŸ† Grand Slam πŸ”¬ Deep Specialist (18) πŸ—ƒοΈ Keyword Collector (64) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (13) πŸš€ Conference Pioneer πŸ’Ž Century Club (63) ⚑ Prolific Year (7)

Conferences

NIPS (27) ICML (13) AAAI (5) AISTATS (5) IJCAI (5) UAI (3) ICLR (2) JMLR (2) EMNLP (1) MLHC (1)

Top co-authors

Andrea Zanette (9) Alessandro Lazaric (8) Yao Liu (7) Christoph Dann (7) Allen Nie (7) Yash Chandak (6) Yannis Flet-Berliac (5) Philip S. Thomas (5) Jonathan Lee (4) Finale Doshi-velez (4)

Research topics

Applications (1) Education (1)

Keywords

reinforcement learning (16) markov decision process (9) contextual bandit (9) off-policy evaluation (8) regret bound (8) sample complexity (7) importance sampling (6) offline reinforcement learning (6) policy learning (5) function approximation (5) policy optimization (5) online learning (4) pac learning (4) value iteration (3) pac bound (3) batch reinforcement learning (3) model selection (3) bellman error (3) temporal abstraction (3) model-based reinforcement learning (3)

Papers

Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study AAAI 2026
Cost-Aware Near-Optimal Policy Learning AAAI 2025
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators NIPS 2024
Adaptive Instrument Design for Indirect Experiments ICLR 2024
Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles EMNLP 2024
Supervised Pretraining Can Learn In-Context Reinforcement Learning NIPS 2023
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets NIPS 2023
Model-Based Offline Reinforcement Learning with Local Misspecification AAAI 2023
Experiment Planning with Function Approximation NIPS 2023
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization NIPS 2023
Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning AAAI 2022
Offline policy optimization with eligible actions UAI 2022
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits NIPS 2022
Off-Policy Evaluation for Action-Dependent Non-stationary Environments NIPS 2022
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data NIPS 2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning NIPS 2022
Giving Feedback on Interactive Student Programs with Meta-Exploration NIPS 2022
Online Model Selection for Reinforcement Learning with Function Approximation AISTATS 2021
Power Constrained Bandits MLHC 2021
Play to Grade: Testing Coding Games as Classifying Markov Decision Process NIPS 2021
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning NIPS 2021
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes NIPS 2021
Design of Experiments for Stochastic Contextual Linear Bandits NIPS 2021
Universal Off-Policy Evaluation NIPS 2021
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration AISTATS 2020
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning JMLR 2020
Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling ICML 2020
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration NIPS 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration NIPS 2020
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding NIPS 2020
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy AAAI 2020
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions ICML 2020
Sublinear Optimal Policy Value Estimation in Contextual Bandits AISTATS 2020
Offline Contextual Bandits with High Probability Fairness Guarantees NIPS 2019
Limiting Extrapolation in Linear Approximate Value Iteration NIPS 2019
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model NIPS 2019
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure ICLR 2019
Policy Certificates: Towards Accountable Reinforcement Learning ICML 2019
Combining parametric and nonparametric models for off-policy evaluation ICML 2019
Separating value functions across time-scales ICML 2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds ICML 2019
Fake It Till You Make It: Learning-Compatible Performance Support UAI 2019
Off-Policy Policy Gradient with Stationary Distribution Correction UAI 2019
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs ICML 2018
Representation Balancing MDPs for Off-policy Policy Evaluation NIPS 2018
Importance Sampling for Fair Policy Selection IJCAI 2018
Decoupling Gradient-Like Learning Rules from Representations ICML 2018
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NIPS 2017
Sample Efficient Policy Search for Optimal Stopping Domains IJCAI 2017
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation NIPS 2017
Regret Minimization in MDPs with Options without Prior Knowledge NIPS 2017
Trading off Rewards and Errors in Multi-Armed Bandits AISTATS 2017
Questimator: Generating Knowledge Assessments for Arbitrary Topics IJCAI 2016
Energetic Natural Gradient Descent ICML 2016
A PAC RL Algorithm for Episodic POMDPs AISTATS 2016
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning ICML 2016
Efficient Bayesian Clustering for Reinforcement Learning IJCAI 2016
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users IJCAI 2016
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning NIPS 2015
PAC-inspired Option Discovery in Lifelong Reinforcement Learning ICML 2014
Online Stochastic Optimization under Correlated Bandit Feedback ICML 2014
Sequential Transfer in Multi-armed Bandit with Finite Set of Models NIPS 2013
Provably Efficient Learning with Typed Parametric Models JMLR 2009