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]

  • General Value Function Networks. Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, and Martha White. Under Submission, 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

  • Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study. Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, and Adam White. Accepted to the 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]