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ORCIDSridhar Mahadevan
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- affiliation: Adobe Research, USA
- affiliation: University of Massachusetts Amherst, USA
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2020 – today
- 2024
[i33]Sridhar Mahadevan:
GAIA: Categorical Foundations of Generative AI. CoRR abs/2402.18732 (2024)- [i32]Sridhar Mahadevan:
Universal Imitation Games. CoRR abs/2405.01540 (2024) - 2023
- [j17]Sridhar Mahadevan
:
Universal Causality. Entropy 25(4): 574 (2023) - [c85]Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan:
Smoothed Online Combinatorial Optimization Using Imperfect Predictions. AAAI 2023: 12130-12137 - [c84]Shiv Shankar, Ritwik Sinha, Saayan Mitra, Viswanathan (Vishy) Swaminathan, Sridhar Mahadevan, Moumita Sinha:
Privacy Aware Experiments without Cookies. WSDM 2023: 1144-1147 - [i31]Yeqi Gao, Sridhar Mahadevan, Zhao Song:
An Over-parameterized Exponential Regression. CoRR abs/2303.16504 (2023) - [i30]Yichuan Deng, Sridhar Mahadevan, Zhao Song:
Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension. CoRR abs/2304.04397 (2023) - [i29]Yichuan Deng, Zhihang Li, Sridhar Mahadevan, Zhao Song:
Zero-th Order Algorithm for Softmax Attention Optimization. CoRR abs/2307.08352 (2023) - 2022
- [c83]Md. Mehrab Tanjim, Ritwik Sinha, Krishna Kumar Singh, Sridhar Mahadevan, David Arbour, Moumita Sinha, Garrison W. Cottrell:
Generating and Controlling Diversity in Image Search. WACV 2022: 3908-3916 - [i28]Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan:
Smoothed Online Combinatorial Optimization Using Imperfect Predictions. CoRR abs/2204.10979 (2022) - [i27]Sridhar Mahadevan:
On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property. CoRR abs/2207.02917 (2022) - [i26]Sridhar Mahadevan:
Categoroids: Universal Conditional Independence. CoRR abs/2208.11077 (2022) - [i25]Sridhar Mahadevan:
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory. CoRR abs/2209.06262 (2022) - [i24]Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Viswanathan Swaminathan, Sridhar Mahadevan:
Privacy Aware Experiments without Cookies. CoRR abs/2211.03758 (2022) - [i23]Sridhar Mahadevan:
A Layered Architecture for Universal Causality. CoRR abs/2212.08981 (2022) - 2021
- [i22]Sridhar Mahadevan, Anup Rao, Georgios Theocharous, Jennifer Healey:
Multiscale Manifold Warping. CoRR abs/2109.09222 (2021) - [i21]Sridhar Mahadevan:
Asymptotic Causal Inference. CoRR abs/2109.09653 (2021) - [i20]Sridhar Mahadevan:
Causal Inference in Network Economics. CoRR abs/2109.11344 (2021) - [i19]Sridhar Mahadevan:
Universal Decision Models. CoRR abs/2110.15431 (2021) - [i18]Sridhar Mahadevan:
Causal Homotopy. CoRR abs/2112.01847 (2021) - 2020
- [c82]Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas:
Optimizing for the Future in Non-Stationary MDPs. ICML 2020: 1414-1425 - [i17]Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas:
Optimizing for the Future in Non-Stationary MDPs. CoRR abs/2005.08158 (2020) - [i16]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. CoRR abs/2006.03976 (2020) - [i15]Bo Liu, Sridhar Mahadevan, Ji Liu:
Regularized Off-Policy TD-Learning. CoRR abs/2006.05314 (2020) - [i14]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of GTD Algorithms. CoRR abs/2006.14364 (2020)
2010 – 2019
- 2019
- [c81]Georgios Theocharous, Jennifer Healey, Sridhar Mahadevan, Michele A. Saad:
Personalizing with Human Cognitive Biases. UMAP (Adjunct Publication) 2019: 13-17 - 2018
- [j16]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. J. Artif. Intell. Res. 63: 461-494 (2018) - [c80]Sridhar Mahadevan:
Imagination Machines: A New Challenge for Artificial Intelligence. AAAI 2018: 7988-7993 - [c79]Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh:
A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds. ECML/PKDD (2) 2018: 843-860 - [i13]Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh:
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds. CoRR abs/1804.10834 (2018) - [i12]Ian Gemp, Sridhar Mahadevan:
Global Convergence to the Equilibrium of GANs using Variational Inequalities. CoRR abs/1808.01531 (2018) - 2017
- [c78]Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan:
Generative Multi-Adversarial Networks. ICLR (Poster) 2017 - [i11]Stephen Giguere, Francisco Garcia, Sridhar Mahadevan:
A Manifold Approach to Learning Mutually Orthogonal Subspaces. CoRR abs/1703.02992 (2017) - [i10]Ian Gemp, Sridhar Mahadevan:
Online Monotone Games. CoRR abs/1710.07328 (2017) - 2016
- [c77]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning Algorithms. IJCAI 2016: 4195-4199 - [i9]Ishan P. Durugkar, Clemens Rosenbaum, Stefan Dernbach, Sridhar Mahadevan:
Deep Reinforcement Learning With Macro-Actions. CoRR abs/1606.04615 (2016) - [i8]Ian Gemp, Ishan P. Durugkar, Mario Parente, Melinda Darby Dyar, Sridhar Mahadevan:
Deep Generative Models for Spectroscopic Analysis on Mars. CoRR abs/1608.05983 (2016) - [i7]Ian Gemp, Sridhar Mahadevan:
Online Monotone Optimization. CoRR abs/1608.07888 (2016) - [i6]Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan:
Generative Multi-Adversarial Networks. CoRR abs/1611.01673 (2016) - 2015
- [c76]Thomas Boucher, CJ Carey, Sridhar Mahadevan, Melinda Darby Dyar:
Aligning Mixed Manifolds. AAAI 2015: 2511-2517 - [c75]Ian Gemp, Sridhar Mahadevan, Bo Liu:
Solving Large Sustainable Supply Chain Networks Using Variational Inequalities. AAAI Workshop: Computational Sustainability 2015 - [c74]Lidan Wang, Minwei Feng, Bowen Zhou, Bing Xiang, Sridhar Mahadevan:
Efficient Hyper-parameter Optimization for NLP Applications. EMNLP 2015: 2112-2117 - [c73]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of Proximal Gradient TD Algorithms. UAI 2015: 504-513 - [i5]Sridhar Mahadevan, Sarath Chandar:
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds. CoRR abs/1507.07636 (2015) - 2014
- [j15]Quanqing Xu, Rajesh Vellore Arumugam, Khai Leong Yong, Sridhar Mahadevan:
Efficient and Scalable Metadata Management in EB-Scale File Systems. IEEE Trans. Parallel Distributed Syst. 25(11): 2840-2850 (2014) - [c72]CJ Carey, Sridhar Mahadevan:
Manifold Spanning Graphs. AAAI 2014: 1708-1714 - [c71]Ian Gemp, Sridhar Mahadevan:
Modeling Context in Cognition Using Variational Inequalities. AAAI Fall Symposia 2014 - [i4]Sridhar Mahadevan, Bo Liu, Philip S. Thomas, William Dabney, Stephen Giguere, Nicholas Jacek, Ian Gemp, Ji Liu:
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces. CoRR abs/1405.6757 (2014) - 2013
- [c70]Sridhar Mahadevan, Stephen Giguere, Nicholas Jacek:
Basis Adaptation for Sparse Nonlinear Reinforcement Learning. AAAI 2013: 654-660 - [c69]Chang Wang, Sridhar Mahadevan:
Multiscale Manifold Learning. AAAI 2013: 912-918 - [c68]Chang Wang, Sridhar Mahadevan:
Manifold Alignment Preserving Global Geometry. IJCAI 2013: 1743-1749 - [c67]Quanqing Xu, Rajesh Vellore Arumugam, Khai Leong Yong, Sridhar Mahadevan:
DROP: Facilitating distributed metadata management in EB-scale storage systems. MSST 2013: 1-10 - [c66]Philip S. Thomas, William Dabney, Stephen Giguere, Sridhar Mahadevan:
Projected Natural Actor-Critic. NIPS 2013: 2337-2345 - [i3]Khashayar Rohanimanesh, Sridhar Mahadevan:
Decision-Theoretic Planning with Concurrent Temporally Extended Actions. CoRR abs/1301.2307 (2013) - 2012
- [c65]Hoa Trong Vu, Clifton Carey, Sridhar Mahadevan:
Manifold Warping: Manifold Alignment over Time. AAAI 2012: 1155-1161 - [c64]Bo Liu, Sridhar Mahadevan, Ji Liu:
Regularized Off-Policy TD-Learning. NIPS 2012: 845-853 - [c63]Sridhar Mahadevan, Bo Liu:
Sparse Q-learning with Mirror Descent. UAI 2012: 564-573 - [i2]Sridhar Mahadevan:
Representation Policy Iteration. CoRR abs/1207.1408 (2012) - [i1]Sridhar Mahadevan, Bo Liu:
Sparse Q-learning with Mirror Descent. CoRR abs/1210.4893 (2012) - 2011
- [c62]Chang Wang, Sridhar Mahadevan:
Heterogeneous Domain Adaptation Using Manifold Alignment. IJCAI 2011: 1541-1546 - [c61]Chang Wang, Sridhar Mahadevan:
Jointly Learning Data-Dependent Label and Locality-Preserving Projections. IJCAI 2011: 1547-1552 - [c60]Blake Foster, Sridhar Mahadevan, Rui Wang:
A GPU-Based Approximate SVD Algorithm. PPAM (1) 2011: 569-578 - 2010
- [c59]Georgios Theocharous, Sridhar Mahadevan:
Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization. AAAI 2010: 1147-1152 - [c58]Sridhar Mahadevan:
Representation Discovery in Sequential Decision Making. AAAI 2010: 1718-1721 - [c57]Sarah Osentoski, Sridhar Mahadevan:
Basis function construction for hierarchical reinforcement learning. AAMAS 2010: 747-754 - [c56]Sridhar Mahadevan, Bo Liu:
Basis Construction from Power Series Expansions of Value Functions. NIPS 2010: 1540-1548
2000 – 2009
- 2009
- [j14]Sridhar Mahadevan:
Learning Representation and Control in Markov Decision Processes: New Frontiers. Found. Trends Mach. Learn. 1(4): 403-565 (2009) - [j13]Jeffrey Johns, Marek Petrik, Sridhar Mahadevan:
Hybrid least-squares algorithms for approximate policy evaluation. Mach. Learn. 76(2-3): 243-256 (2009) - [c55]Chang Wang, Sridhar Mahadevan:
A General Framework for Manifold Alignment. AAAI Fall Symposium: Manifold Learning and Its Applications 2009 - [c54]Kimberly Ferguson, Beverly Park Woolf, Sridhar Mahadevan:
Transfer Learning and Representation Discovery in Intelligent Tutoring Systems. AIED 2009: 605-607 - [c53]Chang Wang, Sridhar Mahadevan:
Manifold Alignment without Correspondence. IJCAI 2009: 1273-1278 - [c52]Chang Wang, Sridhar Mahadevan:
Multiscale Analysis of Document Corpora Based on Diffusion Models. IJCAI 2009: 1592-1597 - [c51]Jeffrey Johns, Marek Petrik, Sridhar Mahadevan:
Hybrid Least-Squares Algorithms for Approximate Policy Evaluation. ECML/PKDD (1) 2009: 9 - 2008
- [b1]Sridhar Mahadevan:
Representation Discovery using Harmonic Analysis. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2008, ISBN 978-3-031-00418-6 - [c50]Sridhar Mahadevan:
Fast Spectral Learning using Lanczos Eigenspace Projections. AAAI 2008: 1472-1475 - [c49]Chang Wang, Sridhar Mahadevan:
Manifold alignment using Procrustes analysis. ICML 2008: 1120-1127 - 2007
- [j12]Sridhar Mahadevan, Mauro Maggioni:
Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes. J. Mach. Learn. Res. 8: 2169-2231 (2007) - [j11]Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchical Average Reward Reinforcement Learning. J. Mach. Learn. Res. 8: 2629-2669 (2007) - [c48]Jeffrey Johns, Sridhar Mahadevan, Chang Wang:
Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization. AAAI 2007: 559-564 - [c47]Jeffrey Johns, Sarah Osentoski, Sridhar Mahadevan:
Representation Discovery in Planning using Harmonic Analysis. AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development 2007: 24-31 - [c46]Ivon Arroyo, Kimberly Ferguson, Jeffrey Johns, Toby Dragon, Hasmik Meheranian, Don Fisher, Andrew G. Barto, Sridhar Mahadevan, Beverly Park Woolf:
Repairing Disengagement With Non-Invasive Interventions. AIED 2007: 195-202 - [c45]Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns, Kimberly Ferguson, Chang Wang:
Learning to Plan Using Harmonic Analysis of Diffusion Models. ICAPS 2007: 224-231 - [c44]Jeffrey Johns, Sridhar Mahadevan:
Constructing basis functions from directed graphs for value function approximation. ICML 2007: 385-392 - [c43]Sridhar Mahadevan:
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning. ICML 2007: 585-592 - [c42]Sarah Osentoski, Sridhar Mahadevan:
Learning state-action basis functions for hierarchical MDPs. ICML 2007: 705-712 - 2006
- [j10]Mohammad Ghavamzadeh, Sridhar Mahadevan, Rajbala Makar:
Hierarchical multi-agent reinforcement learning. Auton. Agents Multi Agent Syst. 13(2): 197-229 (2006) - [c41]Sridhar Mahadevan, Mauro Maggioni, Kimberly Ferguson, Sarah Osentoski:
Learning Representation and Control in Continuous Markov Decision Processes. AAAI 2006: 1194-1199 - [c40]Mauro Maggioni, Sridhar Mahadevan:
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes. ICML 2006: 601-608 - [c39]Kimberly Ferguson, Ivon Arroyo, Sridhar Mahadevan, Beverly Park Woolf, Andrew G. Barto:
Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels. Intelligent Tutoring Systems 2006: 453-462 - [c38]Jeffrey Johns, Sridhar Mahadevan, Beverly Park Woolf:
Estimating Student Proficiency Using an Item Response Theory Model. Intelligent Tutoring Systems 2006: 473-480 - 2005
- [c37]Jeffrey Johns, Sridhar Mahadevan:
A Variational Learning Algorithm for the Abstract Hidden Markov Model. AAAI 2005: 9-14 - [c36]Sridhar Mahadevan:
Samuel Meets Amarel: Automating Value Function Approximation Using Global State Space Analysis. AAAI 2005: 1000-1005 - [c35]Sridhar Mahadevan:
Proto-value functions: developmental reinforcement learning. ICML 2005: 553-560 - [c34]Khashayar Rohanimanesh, Sridhar Mahadevan:
Coarticulation: an approach for generating concurrent plans in Markov decision processes. ICML 2005: 720-727 - [c33]Sridhar Mahadevan, Mauro Maggioni:
Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions. NIPS 2005: 843-850 - [c32]Victoria Manfredi, Sridhar Mahadevan, James F. Kurose:
Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network. SECON 2005: 197-206 - [c31]Sridhar Mahadevan:
Representation Policy Iteration. UAI 2005: 372-379 - 2004
- [c30]Suchi Saria, Sridhar Mahadevan:
Probabilistic Plan Recognition in Multiagent Systems. ICAPS 2004: 287-296 - [c29]Mohammad Ghavamzadeh, Sridhar Mahadevan:
Learning to Communicate and Act Using Hierarchical Reinforcement Learning. AAMAS 2004: 1114-1121 - [c28]Sarah Osentoski, Victoria Manfredi, Sridhar Mahadevan:
Learning hierarchical models of activity. IROS 2004: 891-896 - [c27]Khashayar Rohanimanesh, Robert Platt Jr., Sridhar Mahadevan, Roderic A. Grupen:
Coarticulation in Markov Decision Processes. NIPS 2004: 1137-1144 - 2003
- [j9]Andrew G. Barto, Sridhar Mahadevan:
Recent Advances in Hierarchical Reinforcement Learning. Discret. Event Dyn. Syst. 13(1-2): 41-77 (2003) - [j8]Andrew G. Barto, Sridhar Mahadevan:
Recent Advances in Hierarchical Reinforcement Learning. Discret. Event Dyn. Syst. 13(4): 341-379 (2003) - [c26]Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchical Policy Gradient Algorithms. ICML 2003: 226-233 - 2002
- [c25]Mohammad Ghavamzadeh, Sridhar Mahadevan:
A multiagent reinforcement learning algorithm by dynamically merging markov decision processes. AAMAS 2002: 845-846 - [c24]Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchically Optimal Average Reward Reinforcement Learning. ICML 2002: 195-202 - [c23]Georgios Theocharous, Sridhar Mahadevan:
Approximate Planning with Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation. ICRA 2002: 1347-1352 - [c22]Georgios Theocharous, Sridhar Mahadevan:
Learning the hierarchical structure of spatial environments using multiresolution statistical models. IROS 2002: 1038-1043 - [c21]Khashayar Rohanimanesh, Sridhar Mahadevan:
Learning to Take Concurrent Actions. NIPS 2002: 1619-1626 - [c20]Sridhar Mahadevan:
Spatiotemporal Abstraction of Stochastic Sequential Processes. SARA 2002: 33-50 - 2001
- [c19]Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamzadeh:
Hierarchical multi-agent reinforcement learning. Agents 2001: 246-253 - [c18]Silviu Minut, Sridhar Mahadevan:
A reinforcement learning model of selective visual attention. Agents 2001: 457-464 - [c17]Mohammad Ghavamzadeh, Sridhar Mahadevan:
Continuous-Time Hierarchical Reinforcement Learning. ICML 2001: 186-193 - [c16]Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan:
Learning Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation. ICRA 2001: 511-516 - [c15]Khashayar Rohanimanesh, Sridhar Mahadevan:
Decision-Theoretic Planning with Concurrent Temporally Extended Actions. UAI 2001: 472-479 - 2000
- [c14]Silviu Minut, Sridhar Mahadevan, John M. Henderson, Fred C. Dyer:
Face Recognition Using Foveal Vision. Biologically Motivated Computer Vision 2000: 424-433 - [c13]Natalia Hernandez-Gardiol, Sridhar Mahadevan:
Hierarchical Memory-Based Reinforcement Learning. NIPS 2000: 1047-1053
1990 – 1999
- 1999
- [c12]Gang Wang, Sridhar Mahadevan:
Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes. ICML 1999: 464-473 - 1998
- [j7]Sridhar Mahadevan, Georgios Theocharous, Nikfar Khaleeli:
Rapid Concept Learning for Mobile Robots. Auton. Robots 5(3-4): 239-251 (1998) - [j6]Sridhar Mahadevan, Georgios Theocharous, Nikfar Khaleeli:
Rapid Concept Learning for Mobile Robots. Mach. Learn. 31(1-3): 7-27 (1998) - [c11]Sridhar Mahadevan, Georgios Theocharous:
Optimizing Production Manufacturing Using Reinforcement Learning. FLAIRS 1998: 372-377 - 1996
- [j5]Sridhar Mahadevan, Leslie Pack Kaelbling:
The National Science Foundation Workshop on Reinforcement Learning. AI Mag. 17(4): 89-93 (1996) - [j4]Sridhar Mahadevan:
Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results. Mach. Learn. 22(1-3): 159-195 (1996) - [c10]Sridhar Mahadevan:
An Average-Reward Reinforcement Learning Algorithm for Computing Bias-Optimal Policies. AAAI/IAAI, Vol. 1 1996: 875-880 - [c9]Sridhar Mahadevan:
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning. ICML 1996: 328-336 - 1994
- [j3]Sridhar Mahadevan, Prasad Tadepalli:
Quantifying Prior Determination Knowledge Using the PAC Learning Model. Mach. Learn. 17(1): 69-105 (1994) - [c8]Sridhar Mahadevan:
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning. ICML 1994: 164-172 - 1993
- [j2]Sridhar Mahadevan, Tom M. Mitchell, Jack Mostow, Louis I. Steinberg, Prasad Tadepalli:
An Apprentice-Based Approach to Knowledge Acquisition. Artif. Intell. 64(1): 1-52 (1993) - 1992
- [j1]Sridhar Mahadevan, Jonathan Connell:
Automatic Programming of Behavior-Based Robots Using Reinforcement Learning. Artif. Intell. 55(2): 311-365 (1992) - [c7]Sridhar Mahadevan:
Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions. ML 1992: 290-299 - 1991
- [c6]Sridhar Mahadevan, Jonathan Connell:
Automatic Programming of Behavior-Based Robots Using Reinforcement Learning. AAAI 1991: 768-773 - [c5]Sridhar Mahadevan, Jonathan Connell:
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture. ML 1991: 328-332
1980 – 1989
- 1989
- [c4]Sridhar Mahadevan:
Using Determinations in EBL: A Solution to the incomplete Theory Problem. ML 1989: 320-325 - 1988
- [c3]Sridhar Mahadevan, Prasad Tadepalli:
On the Tractability of Learning from Incomplete Theories. ML 1988: 235-241 - 1985
- [c2]Tom M. Mitchell, Sridhar Mahadevan, Louis I. Steinberg:
LEAP: A Learning Apprentice for VLSI Design. IJCAI 1985: 573-580 - [c1]Sridhar Mahadevan:
Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods. IJCAI 1985: 616-623
Coauthor Index
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