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Doina Precup

114 papers · 2008–2026 · 16 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (39) 🐝 Cross-Pollinator (10) πŸƒ Academic Marathon (17) 🌍 Conference Polyglot (16) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (10) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (40) πŸ† Keyword Champion (6) πŸ”¬ Deep Specialist (19) 🀝 Dynamic Duo (10) πŸ† Grand Slam πŸ‘‘ Triple Crown 🌱 Topic Pioneer ❓ The Questioner (3) ⚑ Prolific Year (15) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (161) πŸš€ Conference Pioneer πŸ’Ž Century Club (113) πŸ”₯ Unstoppable (18)

Conferences

NIPS (40) ICML (23) AAAI (11) AISTATS (11) ICLR (9) IJCAI (6) JMLR (3) EMNLP (2) UAI (2) ACL (1) ACML (1) CORL (1) CVPR (1) INTERSPEECH (1) MIDL (1) NAACL (1)

Top co-authors

Joelle Pineau (10) David Meger (8) Yoshua Bengio (7) Khimya Khetarpal (7) Andre Barreto (6) Martin Klissarov (5) Scott Fujimoto (5) Prakash Panangaden (5) Gheorghe Comanici (5) David Abel (5)

Research topics

Robotics (1) Privacy (1)

Keywords

reinforcement learning (36) value function (16) markov decision process (14) temporal difference learning (9) representation learning (8) function approximation (8) transfer learning (6) temporal abstraction (6) continuous control (6) value function approximation (6) deep reinforcement learning (5) hierarchical reinforcement learning (5) policy evaluation (5) partial observability (5) bellman error (5) neural network (5) policy gradient (4) sequential decision making (4) continual learning (4) policy optimization (4)

Papers

Bootstrapping Personalized Insulin Therapy via Model-Based Reinforcement Learning: An In Silico Study AAAI 2026
Training Language Models to Self-Correct via Reinforcement Learning ICLR 2025
Selective Unlearning via Representation Erasure Using Domain Adversarial Training ICLR 2025
MaestroMotif: Skill Design from Artificial Intelligence Feedback ICLR 2025
Rejecting Hallucinated State Targets during Planning ICML 2025
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning ICLR 2025
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo ICLR 2024
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search IJCAI 2024
Code as Reward: Empowering Reinforcement Learning with VLMs ICML 2024
Mixtures of Experts Unlock Parameter Scaling for Deep RL ICML 2024
Nash Learning from Human Feedback ICML 2024
On the Privacy of Selection Mechanisms with Gaussian Noise AISTATS 2024
Discrete Probabilistic Inference as Control in Multi-path Environments UAI 2024
On learning history-based policies for controlling Markov decision processes AISTATS 2024
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization NAACL 2024
Policy Gradient Methods in the Presence of Symmetries and State Abstractions JMLR 2024
Efficient Reinforcement Learning by Discovering Neural Pathways NIPS 2024
Learning Successor Features the Simple Way NIPS 2024
Adaptive Exploration for Data-Efficient General Value Function Evaluations NIPS 2024
Offline Multitask Representation Learning for Reinforcement Learning NIPS 2024
QGFN: Controllable Greediness with Action Values NIPS 2024
Parseval Regularization for Continual Reinforcement Learning NIPS 2024
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies AISTATS 2024
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning ICLR 2024
ReactZyme: A Benchmark for Enzyme-Reaction Prediction NIPS 2024
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning AAAI 2023
On the Challenges of Using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects AAAI 2023
For SALE: State-Action Representation Learning for Deep Reinforcement Learning NIPS 2023
Prediction and Control in Continual Reinforcement Learning NIPS 2023
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability NIPS 2023
A Definition of Continual Reinforcement Learning NIPS 2023
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation AISTATS 2023
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets ICML 2023
Temporal Abstraction in Reinforcement Learning with the Successor Representation JMLR 2023
Continuous MDP Homomorphisms and Homomorphic Policy Gradient NIPS 2022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation ICLR 2022
Policy Gradients Incorporating the Future ICLR 2022
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates ICLR 2022
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification ICML 2022
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error ICML 2022
Towards painless policy optimization for constrained MDPs UAI 2022
Revisiting Heterophily For Graph Neural Networks NIPS 2022
Proving Theorems using Incremental Learning and Hindsight Experience Replay ICML 2022
On the Expressivity of Markov Reward (Extended Abstract) IJCAI 2022
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation ICML 2021
Randomized Exploration in Reinforcement Learning with General Value Function Approximation ICML 2021
Gradient Starvation: A Learning Proclivity in Neural Networks NIPS 2021
Variance Penalized On-Policy and Off-Policy Actor-Critic AAAI 2021
Self-Supervised Attention-Aware Reinforcement Learning AAAI 2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation NIPS 2021
On the Expressivity of Markov Reward NIPS 2021
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards ICML 2021
Flexible Option Learning NIPS 2021
Temporally Abstract Partial Models NIPS 2021
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning NIPS 2021
Preferential Temporal Difference Learning ICML 2021
What can I do here? A Theory of Affordances in Reinforcement Learning ICML 2020
Forethought and Hindsight in Credit Assignment NIPS 2020
On Efficiency in Hierarchical Reinforcement Learning NIPS 2020
Value-driven Hindsight Modelling NIPS 2020
Reward Propagation Using Graph Convolutional Networks NIPS 2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay NIPS 2020
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction AAAI 2020
Options of Interest: Temporal Abstraction with Interest Functions AAAI 2020
Gifting in Multi-Agent Reinforcement Learning (Student Abstract) AAAI 2020
Value Preserving State-Action Abstractions AISTATS 2020
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning AISTATS 2020
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms AISTATS 2020
Interference and Generalization in Temporal Difference Learning ICML 2020
Invariant Causal Prediction for Block MDPs ICML 2020
SVRG for Policy Evaluation with Fewer Gradient Evaluations IJCAI 2020
Leveraging Observations in Bandits: Between Risks and Benefits AAAI 2019
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments CORL 2019
Hindsight Credit Assignment NIPS 2019
The Option Keyboard: Combining Skills in Reinforcement Learning NIPS 2019
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks NIPS 2019
The Termination Critic AISTATS 2019
Learning Options with Interest Functions AAAI 2019
Combined Reinforcement Learning via Abstract Representations AAAI 2019
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data MIDL 2019
Neural Transfer Learning for Cry-Based Diagnosis of Perinatal Asphyxia INTERSPEECH 2019
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning AISTATS 2019
Off-Policy Deep Reinforcement Learning without Exploration ICML 2019
Per-Decision Option Discounting ICML 2019
Nonlinear Weighted Finite Automata AISTATS 2018
Learning Safe Policies with Expert Guidance NIPS 2018
Convergent Tree Backup and Retrace with Function Approximation ICML 2018
Temporal Regularization for Markov Decision Process NIPS 2018
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions EMNLP 2017
Approximate Value Iteration with Temporally Extended Actions (Extended Abstract) IJCAI 2017
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data ACL 2016
Differentially Private Policy Evaluation ICML 2016
Learning Multi-Step Predictive State Representations IJCAI 2016
Practical Kernel-Based Reinforcement Learning JMLR 2016
Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms EMNLP 2016
An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data IJCAI 2015
Variational Generative Stochastic Networks with Collaborative Shaping ICML 2015
Basis refinement strategies for linear value function approximation in MDPs NIPS 2015
Data Generation as Sequential Decision Making NIPS 2015
Optimizing Energy Production Using Policy Search and Predictive State Representations NIPS 2014
Sample-based approximate regularization ICML 2014
A new Q(lambda) with interim forward view and Monte Carlo equivalence ICML 2014
Learning with Pseudo-Ensembles NIPS 2014
Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI CVPR 2014
Learning from Limited Demonstrations NIPS 2013
Average Reward Optimization Objective In Partially Observable Domains ICML 2013
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces NIPS 2013
Value Pursuit Iteration NIPS 2012
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems AISTATS 2012
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization NIPS 2012
Reinforcement Learning using Kernel-Based Stochastic Factorization NIPS 2011
A Study of Approximate Inference in Probabilistic Relational Models ACML 2010
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation NIPS 2009
Bounding Performance Loss in Approximate MDP Homomorphisms NIPS 2008