Zero-th Order Algorithm for Softmax Attention Optimization - Yichuan Deng; Zhihang Li; Sridhar Mahadevan; Zhao Song (IEEE International Conference on Big Data, BigData 2024, Washington, DC, USA, December 15-18, 2024, 2024)

GAIA: Categorical Foundations of Generative AI - Sridhar Mahadevan (CoRR, 2024)

Universal Imitation Games - Sridhar Mahadevan (CoRR, 2024)

Universal Causality - Sridhar Mahadevan (Entropy, 2023)

Smoothed Online Combinatorial Optimization Using Imperfect Predictions - Kai Wang; Zhao Song; Georgios Theocharous; Sridhar Mahadevan (Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, 2023)

Privacy Aware Experiments without Cookies - Shiv Shankar; Ritwik Sinha; Saayan Mitra; Viswanathan (Vishy) Swaminathan; Sridhar Mahadevan; Moumita Sinha (Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023, 2023)

An Over-parameterized Exponential Regression - Yeqi Gao; Sridhar Mahadevan; Zhao Song (CoRR, 2023)

Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension - Yichuan Deng; Sridhar Mahadevan; Zhao Song (CoRR, 2023)

Zero-th Order Algorithm for Softmax Attention Optimization - Yichuan Deng; Zhihang Li; Sridhar Mahadevan; Zhao Song (CoRR, 2023)

Generating and Controlling Diversity in Image Search - Md. Mehrab Tanjim; Ritwik Sinha; Krishna Kumar Singh; Sridhar Mahadevan; David Arbour; Moumita Sinha; Garrison W. Cottrell (IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Waikoloa, HI, USA, January 3-8, 2022, 2022)

Smoothed Online Combinatorial Optimization Using Imperfect Predictions - Kai Wang; Zhao Song; Georgios Theocharous; Sridhar Mahadevan (CoRR, 2022)

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property - Sridhar Mahadevan (CoRR, 2022)

Categoroids: Universal Conditional Independence - Sridhar Mahadevan (CoRR, 2022)

Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory - Sridhar Mahadevan (CoRR, 2022)

Privacy Aware Experiments without Cookies - Shiv Shankar; Ritwik Sinha; Saayan Mitra; Moumita Sinha; Viswanathan Swaminathan; Sridhar Mahadevan (CoRR, 2022)

A Layered Architecture for Universal Causality - Sridhar Mahadevan (CoRR, 2022)

Multiscale Manifold Warping - Sridhar Mahadevan; Anup Rao; Georgios Theocharous; Jennifer Healey (CoRR, 2021)

Asymptotic Causal Inference - Sridhar Mahadevan (CoRR, 2021)

Causal Inference in Network Economics - Sridhar Mahadevan (CoRR, 2021)

Universal Decision Models - Sridhar Mahadevan (CoRR, 2021)

Causal Homotopy - Sridhar Mahadevan (CoRR, 2021)

Optimizing for the Future in Non-Stationary MDPs - Yash Chandak; Georgios Theocharous; Shiv Shankar; Martha White; Sridhar Mahadevan; Philip S. Thomas (Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, 2020)

Optimizing for the Future in Non-Stationary MDPs - Yash Chandak; Georgios Theocharous; Shiv Shankar; Martha White; Sridhar Mahadevan; Philip S. Thomas (CoRR, 2020)

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity - Bo Liu; Ian Gemp; Mohammad Ghavamzadeh; Ji Liu; Sridhar Mahadevan; Marek Petrik (CoRR, 2020)

Regularized Off-Policy TD-Learning - Bo Liu; Sridhar Mahadevan; Ji Liu (CoRR, 2020)

Finite-Sample Analysis of GTD Algorithms - Bo Liu; Ji Liu; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (CoRR, 2020)

Personalizing with Human Cognitive Biases - Georgios Theocharous; Jennifer Healey; Sridhar Mahadevan; Michele A. Saad (Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, UMAP 2019, Larnaca, Cyprus, June 09-12, 2019, 2019)

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity - Bo Liu; Ian Gemp; Mohammad Ghavamzadeh; Ji Liu; Sridhar Mahadevan; Marek Petrik (J. Artif. Intell. Res., 2018)

Imagination Machines: A New Challenge for Artificial Intelligence - Sridhar Mahadevan (Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, 2018)

A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds - Sridhar Mahadevan; Bamdev Mishra; Shalini Ghosh (Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II, 2018)

A Unified Framework for Domain Adaptation using Metric Learning on Manifolds - Sridhar Mahadevan; Bamdev Mishra; Shalini Ghosh (CoRR, 2018)

Global Convergence to the Equilibrium of GANs using Variational Inequalities - Ian Gemp; Sridhar Mahadevan (CoRR, 2018)

Generative Multi-Adversarial Networks - Ishan P. Durugkar; Ian Gemp; Sridhar Mahadevan (5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017)

A Manifold Approach to Learning Mutually Orthogonal Subspaces - Stephen Giguere; Francisco Garcia; Sridhar Mahadevan (CoRR, 2017)

Online Monotone Games - Ian Gemp; Sridhar Mahadevan (CoRR, 2017)

Proximal Gradient Temporal Difference Learning Algorithms - Bo Liu; Ji Liu; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, 2016)

Deep Reinforcement Learning With Macro-Actions - Ishan P. Durugkar; Clemens Rosenbaum; Stefan Dernbach; Sridhar Mahadevan (CoRR, 2016)

Deep Generative Models for Spectroscopic Analysis on Mars - Ian Gemp; Ishan P. Durugkar; Mario Parente; Melinda Darby Dyar; Sridhar Mahadevan (CoRR, 2016)

Online Monotone Optimization - Ian Gemp; Sridhar Mahadevan (CoRR, 2016)

Generative Multi-Adversarial Networks - Ishan P. Durugkar; Ian Gemp; Sridhar Mahadevan (CoRR, 2016)

Aligning Mixed Manifolds - Thomas Boucher; CJ Carey; Sridhar Mahadevan; Melinda Darby Dyar (Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, 2015)

Solving Large Sustainable Supply Chain Networks Using Variational Inequalities - Ian Gemp; Sridhar Mahadevan; Bo Liu (Computational Sustainability, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015, 2015)

Efficient Hyper-parameter Optimization for NLP Applications - Lidan Wang; Minwei Feng; Bowen Zhou; Bing Xiang; Sridhar Mahadevan (Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, 2015)

Finite-Sample Analysis of Proximal Gradient TD Algorithms - Bo Liu; Ji Liu; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands, 2015)

Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds - Sridhar Mahadevan; Sarath Chandar (CoRR, 2015)

Efficient and Scalable Metadata Management in EB-Scale File Systems - Quanqing Xu; Rajesh Vellore Arumugam; Khai Leong Yong; Sridhar Mahadevan (IEEE Trans. Parallel Distributed Syst., 2014)

Manifold Spanning Graphs - CJ Carey; Sridhar Mahadevan (Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, 2014)

Modeling Context in Cognition Using Variational Inequalities - Ian Gemp; Sridhar Mahadevan (2014 AAAI Fall Symposia, Arlington, Virginia, USA, November 13-15, 2014, 2014)

Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces - Sridhar Mahadevan; Bo Liu; Philip S. Thomas; William Dabney; Stephen Giguere; Nicholas Jacek; Ian Gemp; Ji Liu (CoRR, 2014)

Basis Adaptation for Sparse Nonlinear Reinforcement Learning - Sridhar Mahadevan; Stephen Giguere; Nicholas Jacek (Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA, 2013)

Multiscale Manifold Learning - Chang Wang; Sridhar Mahadevan (Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA, 2013)

Manifold Alignment Preserving Global Geometry - Chang Wang; Sridhar Mahadevan (IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, 2013)

DROP: Facilitating distributed metadata management in EB-scale storage systems - Quanqing Xu; Rajesh Vellore Arumugam; Khai Leong Yong; Sridhar Mahadevan (IEEE 29th Symposium on Mass Storage Systems and Technologies, MSST 2013, May 6-10, 2013, Long Beach, CA, USA, 2013)

Projected Natural Actor-Critic - Philip S. Thomas; William Dabney; Stephen Giguere; Sridhar Mahadevan (Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, 2013)

Decision-Theoretic Planning with Concurrent Temporally Extended Actions - Khashayar Rohanimanesh; Sridhar Mahadevan (CoRR, 2013)

Manifold Warping: Manifold Alignment over Time - Hoa Trong Vu; Clifton Carey; Sridhar Mahadevan (Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada, 2012)

Regularized Off-Policy TD-Learning - Bo Liu; Sridhar Mahadevan; Ji Liu (Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012)

Sparse Q-learning with Mirror Descent - Sridhar Mahadevan; Bo Liu (Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, August 14-18, 2012, 2012)

Representation Policy Iteration - Sridhar Mahadevan (CoRR, 2012)

Sparse Q-learning with Mirror Descent - Sridhar Mahadevan; Bo Liu (CoRR, 2012)

Heterogeneous Domain Adaptation Using Manifold Alignment - Chang Wang; Sridhar Mahadevan (IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, 2011)

Jointly Learning Data-Dependent Label and Locality-Preserving Projections - Chang Wang; Sridhar Mahadevan (IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, 2011)

A GPU-Based Approximate SVD Algorithm - Blake Foster; Sridhar Mahadevan; Rui Wang (Parallel Processing and Applied Mathematics - 9th International Conference, PPAM 2011, Torun, Poland, September 11-14, 2011. Revised Selected Papers, Part I, 2011)

Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization - Georgios Theocharous; Sridhar Mahadevan (Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, 2010)

Representation Discovery in Sequential Decision Making - Sridhar Mahadevan (Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, 2010)

Basis function construction for hierarchical reinforcement learning - Sarah Osentoski; Sridhar Mahadevan (9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3, 2010)

Basis Construction from Power Series Expansions of Value Functions - Sridhar Mahadevan; Bo Liu (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada, 2010)

Learning Representation and Control in Markov Decision Processes: New Frontiers - Sridhar Mahadevan (Found. Trends Mach. Learn., 2009)

Hybrid least-squares algorithms for approximate policy evaluation - Jeffrey Johns; Marek Petrik; Sridhar Mahadevan (Mach. Learn., 2009)

A General Framework for Manifold Alignment - Chang Wang; Sridhar Mahadevan (Manifold Learning and Its Applications, Papers from the 2009 AAAI Fall Symposium, Arlington, Virginia, USA, November 5-7, 2009, 2009)

Transfer Learning and Representation Discovery in Intelligent Tutoring Systems - Kimberly Ferguson; Beverly Park Woolf; Sridhar Mahadevan (Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, July 6-10, 2009, Brighton, UK, 2009)

Manifold Alignment without Correspondence - Chang Wang; Sridhar Mahadevan (IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, 2009)

Multiscale Analysis of Document Corpora Based on Diffusion Models - Chang Wang; Sridhar Mahadevan (IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, 2009)

Hybrid Least-Squares Algorithms for Approximate Policy Evaluation - Jeffrey Johns; Marek Petrik; Sridhar Mahadevan (Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I, 2009)

Representation Discovery using Harmonic Analysis - Sridhar Mahadevan (Morgan & Claypool Publishers, 2008)

Fast Spectral Learning using Lanczos Eigenspace Projections - Sridhar Mahadevan (Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008, 2008)

Manifold alignment using Procrustes analysis - Chang Wang; Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008, 2008)

Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes - Sridhar Mahadevan; Mauro Maggioni (J. Mach. Learn. Res., 2007)

Hierarchical Average Reward Reinforcement Learning - Mohammad Ghavamzadeh; Sridhar Mahadevan (J. Mach. Learn. Res., 2007)

Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization - Jeffrey Johns; Sridhar Mahadevan; Chang Wang (Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada, 2007)

Representation Discovery in Planning using Harmonic Analysis - Jeffrey Johns; Sarah Osentoski; Sridhar Mahadevan (Computational Approaches to Representation Change during Learning and Development, Papers from the 2007 AAAI Fall Symposium, Arlington, Virginia, USA, November 9-11, 2007, 2007)

Repairing Disengagement With Non-Invasive Interventions - Ivon Arroyo; Kimberly Ferguson; Jeffrey Johns; Toby Dragon; Hasmik Meheranian; Don Fisher; Andrew G. Barto; Sridhar Mahadevan; Beverly Park Woolf (Artificial Intelligence in Education, Building Technology Rich Learning Contexts That Work, Proceedings of the 13th International Conference on Artificial Intelligence in Education, AIED 2007, July 9-13, 2007, Los Angeles, California, USA, 2007)

Learning to Plan Using Harmonic Analysis of Diffusion Models - Sridhar Mahadevan; Sarah Osentoski; Jeffrey Johns; Kimberly Ferguson; Chang Wang (Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling, ICAPS 2007, Providence, Rhode Island, USA, September 22-26, 2007, 2007)

Constructing basis functions from directed graphs for value function approximation - Jeffrey Johns; Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, 2007)

Adaptive mesh compression in 3D computer graphics using multiscale manifold learning - Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, 2007)

Learning state-action basis functions for hierarchical MDPs - Sarah Osentoski; Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, 2007)

Hierarchical multi-agent reinforcement learning - Mohammad Ghavamzadeh; Sridhar Mahadevan; Rajbala Makar (Auton. Agents Multi Agent Syst., 2006)

Learning Representation and Control in Continuous Markov Decision Processes - Sridhar Mahadevan; Mauro Maggioni; Kimberly Ferguson; Sarah Osentoski (Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA, 2006)

Fast direct policy evaluation using multiscale analysis of Markov diffusion processes - Mauro Maggioni; Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006, 2006)

Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels - Kimberly Ferguson; Ivon Arroyo; Sridhar Mahadevan; Beverly Park Woolf; Andrew G. Barto (Intelligent Tutoring Systems, 8th International Conference, ITS 2006, Jhongli, Taiwan, June 26-30, 2006, Proceedings, 2006)

Estimating Student Proficiency Using an Item Response Theory Model - Jeffrey Johns; Sridhar Mahadevan; Beverly Park Woolf (Intelligent Tutoring Systems, 8th International Conference, ITS 2006, Jhongli, Taiwan, June 26-30, 2006, Proceedings, 2006)

A Variational Learning Algorithm for the Abstract Hidden Markov Model - Jeffrey Johns; Sridhar Mahadevan (Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, July 9-13, 2005, Pittsburgh, Pennsylvania, USA, 2005)

Samuel Meets Amarel: Automating Value Function Approximation Using Global State Space Analysis - Sridhar Mahadevan (Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, July 9-13, 2005, Pittsburgh, Pennsylvania, USA, 2005)

Proto-value functions: developmental reinforcement learning - Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005, 2005)

Coarticulation: an approach for generating concurrent plans in Markov decision processes - Khashayar Rohanimanesh; Sridhar Mahadevan (Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005, 2005)

Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions - Sridhar Mahadevan; Mauro Maggioni (Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada], 2005)

Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network - Victoria Manfredi; Sridhar Mahadevan; James F. Kurose (Proceedings of the Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, SECON 2005, September 26-29, 2005, Santa Clara, CA, USA, 2005)

Representation Policy Iteration - Sridhar Mahadevan (UAI '05, Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, Edinburgh, Scotland, July 26-29, 2005, 2005)

Probabilistic Plan Recognition in Multiagent Systems - Suchi Saria; Sridhar Mahadevan (Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling (ICAPS 2004), June 3-7 2004, Whistler, British Columbia, Canada, 2004)

Learning to Communicate and Act Using Hierarchical Reinforcement Learning - Mohammad Ghavamzadeh; Sridhar Mahadevan (3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), 19-23 August 2004, New York, NY, USA, 2004)

Learning hierarchical models of activity - Sarah Osentoski; Victoria Manfredi; Sridhar Mahadevan (2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, September 28 - October 2, 2004, 2004)

Coarticulation in Markov Decision Processes - Khashayar Rohanimanesh; Robert Platt Jr.; Sridhar Mahadevan; Roderic A. Grupen (Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada], 2004)

Recent Advances in Hierarchical Reinforcement Learning - Andrew G. Barto; Sridhar Mahadevan (Discret. Event Dyn. Syst., 2003)

Recent Advances in Hierarchical Reinforcement Learning - Andrew G. Barto; Sridhar Mahadevan (Discret. Event Dyn. Syst., 2003)

Hierarchical Policy Gradient Algorithms - Mohammad Ghavamzadeh; Sridhar Mahadevan (Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, 2003)

A multiagent reinforcement learning algorithm by dynamically merging markov decision processes - Mohammad Ghavamzadeh; Sridhar Mahadevan (The First International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2002, July 15-19, 2002, Bologna, Italy, Proceedings, 2002)

Hierarchically Optimal Average Reward Reinforcement Learning - Mohammad Ghavamzadeh; Sridhar Mahadevan (Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002, 2002)

Approximate Planning with Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation - Georgios Theocharous; Sridhar Mahadevan (Proceedings of the 2002 IEEE International Conference on Robotics and Automation, ICRA 2002, May 11-15, 2002, Washington, DC, USA, 2002)

Learning the hierarchical structure of spatial environments using multiresolution statistical models - Georgios Theocharous; Sridhar Mahadevan (IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, September 30 - October 4, 2002, 2002)

Learning to Take Concurrent Actions - Khashayar Rohanimanesh; Sridhar Mahadevan (Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada], 2002)

Spatiotemporal Abstraction of Stochastic Sequential Processes - Sridhar Mahadevan (Abstraction, Reformulation and Approximation, 5th International Symposium, SARA 2002, Kananaskis, Alberta, Canada, August 2-4, 2002, Proceedings, 2002)

Hierarchical multi-agent reinforcement learning - Rajbala Makar; Sridhar Mahadevan; Mohammad Ghavamzadeh (Proceedings of the Fifth International Conference on Autonomous Agents, AGENTS 2001, Montreal, Canada, May 28 - June 1, 2001, 2001)

A reinforcement learning model of selective visual attention - Silviu Minut; Sridhar Mahadevan (Proceedings of the Fifth International Conference on Autonomous Agents, AGENTS 2001, Montreal, Canada, May 28 - June 1, 2001, 2001)

Continuous-Time Hierarchical Reinforcement Learning - Mohammad Ghavamzadeh; Sridhar Mahadevan (Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, 2001)

Learning Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation - Georgios Theocharous; Khashayar Rohanimanesh; Sridhar Mahadevan (Proceedings of the 2001 IEEE International Conference on Robotics and Automation, ICRA 2001, May 21-26, 2001, Seoul, Korea, 2001)

Decision-Theoretic Planning with Concurrent Temporally Extended Actions - Khashayar Rohanimanesh; Sridhar Mahadevan (UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001, 2001)

Face Recognition Using Foveal Vision - Silviu Minut; Sridhar Mahadevan; John M. Henderson; Fred C. Dyer (Biologically Motivated Computer Vision, First IEEE International Workshop, BMVC 2000, Seoul, Korea, May 15-17, 2000, Proceedings, 2000)

Hierarchical Memory-Based Reinforcement Learning - Natalia Hernandez-Gardiol; Sridhar Mahadevan (Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA, 2000)

Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes - Gang Wang; Sridhar Mahadevan (Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, 1999)

Rapid Concept Learning for Mobile Robots - Sridhar Mahadevan; Georgios Theocharous; Nikfar Khaleeli (Auton. Robots, 1998)

Rapid Concept Learning for Mobile Robots - Sridhar Mahadevan; Georgios Theocharous; Nikfar Khaleeli (Mach. Learn., 1998)

Optimizing Production Manufacturing Using Reinforcement Learning - Sridhar Mahadevan; Georgios Theocharous (Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference, May 18-20, 1998, Sanibel Island, Florida, USA, 1998)

The National Science Foundation Workshop on Reinforcement Learning - Sridhar Mahadevan; Leslie Pack Kaelbling (AI Mag., 1996)

Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results - Sridhar Mahadevan (Mach. Learn., 1996)

An Average-Reward Reinforcement Learning Algorithm for Computing Bias-Optimal Policies - Sridhar Mahadevan (Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 96, IAAI 96, Portland, Oregon, USA, August 4-8, 1996, Volume 1, 1996)

Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning - Sridhar Mahadevan (Machine Learning, Proceedings of the Thirteenth International Conference (ICML '96), Bari, Italy, July 3-6, 1996, 1996)

Quantifying Prior Determination Knowledge Using the PAC Learning Model - Sridhar Mahadevan; Prasad Tadepalli (Mach. Learn., 1994)

To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning - Sridhar Mahadevan (Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, 1994)

An Apprentice-Based Approach to Knowledge Acquisition - Sridhar Mahadevan; Tom M. Mitchell; Jack Mostow; Louis I. Steinberg; Prasad Tadepalli (Artif. Intell., 1993)

Automatic Programming of Behavior-Based Robots Using Reinforcement Learning - Sridhar Mahadevan; Jonathan Connell (Artif. Intell., 1992)

Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions - Sridhar Mahadevan (Proceedings of the Ninth International Workshop on Machine Learning (ML 1992), Aberdeen, Scotland, UK, July 1-3, 1992, 1992)

Automatic Programming of Behavior-Based Robots Using Reinforcement Learning - Sridhar Mahadevan; Jonathan Connell (Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14-19, 1991, Volume 2, 1991)

Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture - Sridhar Mahadevan; Jonathan Connell (Proceedings of the Eighth International Workshop (ML91), Northwestern University, Evanston, Illinois, USA, 1991)

Using Determinations in EBL: A Solution to the incomplete Theory Problem - Sridhar Mahadevan (Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), Cornell University, Ithaca, New York, USA, June 26-27, 1989, 1989)

On the Tractability of Learning from Incomplete Theories - Sridhar Mahadevan; Prasad Tadepalli (Machine Learning, Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, June 12-14, 1988, 1988)

LEAP: A Learning Apprentice for VLSI Design - Tom M. Mitchell; Sridhar Mahadevan; Louis I. Steinberg (Proceedings of the 9th International Joint Conference on Artificial Intelligence. Los Angeles, CA, USA, August 1985, 1985)

Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods - Sridhar Mahadevan (Proceedings of the 9th International Joint Conference on Artificial Intelligence. Los Angeles, CA, USA, August 1985, 1985)