Preprints and Working Papers
- Investigating Objectives for Off-policy Value Estimation in Reinforcement Learning.
Andrew Patterson, Sina Ghiassian, Adam White and Martha White.
Working Paper, 2020. [pdf]
- Actor-Expert: A Framework for using Q-learning in Continuous Action Spaces.
Sungsu Lim, Ajin Joseph, Lei Le, Yangchen Pan, and Martha White.
In Revision, 2019. [pdf]
- Online Off-policy Prediction.
Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton,
and Adam White.
arXiv, 2018. [pdf]
Publications
- General Value Function Networks.
Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew
Patterson, Adam White, and Martha White.
Journal of AI Research (JAIR), 2021. [pdf]
- An implicit function learning approach for parametric modal regression.
Yancghen Pan, Ehsan Imani and Martha White.
Advances in Neural Information Processing Systems (NeurIPS), 2020. [pdf]
- Towards Safe Policy Improvement for Non-Stationary MDPs.
Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Phillip S. Thomas.
Advances in Neural Information Processing Systems (NeurIPS), 2020. [pdf]
- Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study.
Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, and Adam White.
Journal of AI Research (JAIR), 2020. [pdf]
- Gradient Temporal-Difference Learning with Regularized Corrections.
Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White and Martha White.
International Conference on Machine Learning (ICML), 2020. [pdf]
- Selective Dyna-style Planning Under Limited Model Capacity.
Zaheer Abbas, Sam Sokota, Erin Talvitie and Martha White.
International Conference on Machine Learning (ICML), 2020. [pdf]
- Optimizing for the Future in Non-Stationary MDPs.
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas
International Conference on Machine Learning (ICML), 2020. [pdf]
- Training Recurrent Neural Networks Online by Learning Explicit State Variables.
Somjit Nath, Vincent Liu, Alan Chan, Adam White and Martha White.
International Conference on Learning Representations (ICLR), 2020. [pdf]
- Maxmin Q-learning: Controlling the Estimation Bias of Q-learning.
Qingfeng Lan, Yangchen Pan, Alona Fyshe and Martha White.
International Conference on Learning Representations (ICLR), 2020. [pdf]
- Maximizing Information Gain in Partially Observable Environments
via Prediction Rewards.
Yash Satsangi, Sungsu Lim,
Shimon Whiteson, Frans Oliehoek and Martha White.
International
Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 2020. [pdf]
- Meta-Learning Representations for Continual Learning.
Khurram Javed and Martha White.
Advances in Neural Information Processing Systems (NeurIPS), 2019. [pdf]
- Importance Resampling for Off-policy Prediction.
Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian and Martha White.
Advances in Neural Information Processing Systems (NeurIPS), 2019. [pdf]
- Learning Macroscopic Brain Connectomes via Group-Sparse Factorization.
Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar Caiafa, Russell Greiner and Martha White.
Advances in Neural Information Processing Systems (NeurIPS), 2019.
- Planning with Expectation Models.
Yi Wan, Muhammad Zaheer, Adam White, Martha White and Richard S. Sutton.
International Joint Conference on Artificial Intelligence (IJCAI), 2019. [pdf]
- Hill Climbing on Value Estimates for Search-control in Dyna.
Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand and Martha White.
International Joint Conference on Artificial Intelligence (IJCAI), 2019. [pdf]
- Two-Timescale Networks for Nonlinear Value Function Approximation .
Wesley Chung, Somjit Nath, Ajin Joseph and Martha White.
International Conference on Learning Representations (ICLR), 2019.
[pdf]
- The Utility of Sparse Representations for Control in Reinforcement Learning.
Vincent Liu, Raksha Kumaraswamy, Lei Le and Martha White.
AAAI Conference on Artificial Intelligence, 2019.
[pdf]
- Meta-descent for online, continual prediction.
Andrew Jacobsen, Matthew Schlegel, Cam Linke, Thomas Degris, Adam White and Martha White.
AAAI Conference on Artificial Intelligence, 2019. [pdf]
- An Off-policy Policy Gradient Theorem Using Emphatic Weightings.
Ehsan Imani, Eric Graves and Martha White.
Advances in Neural Information Processing Systems (NIPS),
2018. [pdf]
- Supervised autoencoders: Improving generalization performance with unsupervised regularizers.
Lei Le, Andrew Patterson and Martha White.
Advances in Neural Information Processing Systems (NIPS),
2018. [pdf]
- Context-dependent upper-confidence bounds for directed exploration.
Raksha Kumaraswamy, Matthew Schlegel, Adam White and Martha White.
Advances in Neural Information Processing Systems (NIPS),
2018. [pdf]
- Improving Regression Performance with Distributional Losses.
Ehsan Imani and Martha White.
International Conference on Machine Learning (ICML),
2018. [pdf]
- Reinforcement Learning with
Function-Valued Action Spaces for Partial Differential Equation Control.
Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi,
Piyush Grover, Daniel Nikovski.
International Conference on Machine Learning (ICML),
2018. [pdf]
- Organizing experience: a
deeper look at replay mechanisms for sample-based planning in
continuous state domains. Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
International Joint Conference on Artificial Intelligence (IJCAI),
2018. [pdf]
- High-confidence error estimates for learned value functions.
Touqir Sajed, Wesley Chung and Martha White.
Uncertainty in Artificial Intelligence (UAI),
2018. [pdf]
- Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.
Craig Sherstan, Dylan Ashley, Brendan Bennet, Kenny Young, Adam White, Martha White, Richard Sutton.
Uncertainty in Artificial Intelligence (UAI),
2018. [pdf]
- Multi-view Matrix Factorization for Linear Dynamical System Estimation.
Mahdi Karami, Martha White, Dale Schuurmans and Csaba Szepesvari.
Advances in Neural Information Processing Systems (NIPS), 2017.
[pdf]
- Unifying task specification in reinforcement learning.
Martha White.
International Conference on Machine Learning (ICML), 2017.
[pdf]
- Adapting kernel representations online using submodular maximization.
Matthew Schlegel, Yangchen Pan and Martha White.
International Conference on Machine Learning (ICML),
2017. [pdf]
- Effective sketching methods for value function approximation.
Yangchen Pan, Erfan Sadeqi Azer and Martha White.
Uncertainty in Artificial Intelligence (UAI), 2017. [pdf]
- Learning sparse representations in reinforcement learning with sparse coding.
Lei Le, Raksha Kumaraswamy and Martha White.
International Joint Conference on Artificial Intelligence (IJCAI), 2017. [pdf]
- Accelerated Gradient Temporal Difference Learning.
Yangchen Pan, Adam White and Martha White.
AAAI Conference on Artificial Intelligence, 2017.
[pdf]
- Recovering true classifier performance in positive-unlabeled learning.
Shantanu Jain, Martha White and Predrag Radivojac.
[pdf]
AAAI Conference on Artificial Intelligence, 2017.
- Estimating the class prior and posterior from noisy positives and unlabeled data .
Shantanu Jain, Martha White and Predrag Radivojac.
Advances in Neural Information Processing Systems (NIPS), 2016.
[pdf]
- Identifying global optimality for dictionary learning.
Lei Le and Martha White.
In submission to JMLR, 2016.
[pdf]
- Nonparametric semi-supervised learning of class proportions.
Shantanu Jain, Martha White, Michael W. Trosset and Predrag Radivojac.
In submission to JMLR, 2016.
[pdf]
- Incremental Truncated LSTD.
Clement Gehring, Yangchen Pan and Martha White.
International Joint Conference on Artificial Intelligence (IJCAI), 2016.
[pdf]
- Investigating practical, linear temporal difference learning.
Adam White and Martha White.
Autonomous Agents and Multi-agent Systems (AAMAS), 2016.
[pdf]
- A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning .
Adam White and Martha White.
Autonomous Agents and Multi-agent Systems (AAMAS), 2016.
[pdf]
- Scalable Metric Learning for Co-embedding.
Farzaneh Mirzazadeh, Martha White, Andras Gyorgy and Dale Schuurmans.
ECML PKDD, 2015.
[pdf]
- An Emphatic Approach to the Problem
of Off-policy Temporal-Difference Learning.
Richard S. Sutton, A Rupam Mahmood and Martha White.
Journal of Machine Learning Research (JMLR), 2016.
[pdf]
- Optimal Estimation of
Multivariate ARMA Models.
Martha White, Junfeng Wen, Michael Bowling and Dale Schuurmans.
AAAI Conference on Artificial Intelligence, 2015.
[pdf]
- Partition Tree Weighting.
Joel Veness, Martha White, Michael Bowling and Andras Gyorgy.
Data Compression Conference (DCC), 2013.
[pdf]
- Convex Multi-view Subspace Learning.
Martha White, Yaoliang Yu, Xinhua Zhang, and Dale Schuurmans.
Advances in Neural Information Processing Systems (NIPS), 2012.
[pdf]
- Off-Policy Actor-Critic.
Thomas Degris, Martha White, Richard S. Sutton.
In International Conference on Machine Learning (ICML), 2012.
[pdf]
- Generalized Optimal Reverse Prediction.
Martha White and Dale Schuurmans.
In International Conference on
Artificial Intelligence and Statistics (AISTATS), 2012.
[pdf]
- Convex Sparse Coding, Subspace
Learning, and Semi-Supervised Extensions.
Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang and Dale
Schuurmans.
In AAAI Conference on Artificial Intelligence (AAAI), 2011.
[pdf]
- Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains.
Martha White and Adam White.
In Advances in Neural Information Processing Systems (NIPS), 2010.
[pdf]
- Relaxed Clipping: A Global Training Method for Robust Regression and Classification.
Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans.
In Advances in Neural Information Processing Systems (NIPS),
2010.
[pdf]
- Learning a Value Analysis Tool For Agent Evaluation.
Martha White and Michael Bowling.
In International Joint Conference on Artificial Intelligence (IJCAI),
2009.
[pdf]
- Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning.
Linli Xu, Martha White, and Dale Schuurmans.
In International Conference on Machine Learning (ICML),
2009.
[pdf]
Theses
- Martha White. Regularized
factor models. PhD thesis,
Details
BibTeX
Download:
[pdf]
- Martha White. A General Framework for Reducing Variance in Agent Evaluation. Master's thesis,
Details
BibTeX
Download:
[pdf]