Sridhar Mahadevan
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Sridhar Mahadevancomputer-science Degrees
Computer Science
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Machine Learning
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Artificial Intelligence
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Database
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Computer Science
Sridhar Mahadevan's Degrees
- PhD Computer Science Stanford University
- Masters Computer Science Stanford University
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(Suggest an Edit or Addition)Sridhar Mahadevan's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Recent Advances in Hierarchical Reinforcement Learning (2003) (1038)
- Automatic Programming of Behavior-Based Robots Using Reinforcement Learning (1991) (739)
- Recent Advances in Hierarchical Reinforcement Learning (2003) (589)
- Heterogeneous Domain Adaptation Using Manifold Alignment (2011) (390)
- Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes (2007) (347)
- LEAP: A Learning Apprentice for VLSI Design (1985) (344)
- Average reward reinforcement learning: Foundations, algorithms, and empirical results (2004) (319)
- Manifold alignment using Procrustes analysis (2008) (271)
- Generative Multi-Adversarial Networks (2016) (267)
- Hierarchical multi-agent reinforcement learning (2001) (234)
- Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning (1999) (222)
- Manifold Alignment without Correspondence (2009) (194)
- Robot Learning (1993) (171)
- Finite-Sample Analysis of Proximal Gradient TD Algorithms (2015) (151)
- Repairing Disengagement With Non-Invasive Interventions (2007) (145)
- Proto-value functions: developmental reinforcement learning (2005) (131)
- Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions (2005) (127)
- Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results (2005) (109)
- A General Framework for Manifold Alignment (2009) (100)
- A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy (2015) (92)
- Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (1991) (82)
- Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning (2007) (81)
- A reinforcement learning model of selective visual attention (2001) (75)
- Optimizing Production Manufacturing Using Reinforcement Learning (1998) (66)
- Rapid Task Learning for Real Robots (1993) (66)
- Approximate planning with hierarchical partially observable Markov decision process models for robot navigation (2002) (65)
- Learning Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation (2001) (65)
- Hierarchical Memory-Based Reinforcement Learning (2000) (64)
- Learning to communicate and act using hierarchical reinforcement learning (2004) (64)
- Learning Representation and Control in Markov Decision Processes: New Frontiers (2009) (63)
- Regularized Off-Policy TD-Learning (2012) (63)
- Proto-transfer Learning in Markov Decision Processes Using Spectral Methods (2006) (61)
- 14 - Gaze Control for Face Learning and Recognition by Humans and Machines (2001) (61)
- Hierarchical Policy Gradient Algorithms (2003) (60)
- Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces (2014) (60)
- Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (1992) (57)
- Machine learning tools formineral recognition and classification from Raman spectroscopy (2014) (57)
- Learning to Take Concurrent Actions (2002) (55)
- Decision-Theoretic Planning with Concurrent Temporally Extended Actions (2001) (52)
- To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning (1994) (52)
- Probabilistic Plan Recognition in Multiagent Systems (2004) (50)
- Samuel Meets Amarel: Automating Value Function Approximation Using Global State Space Analysis (2005) (49)
- Fast direct policy evaluation using multiscale analysis of Markov diffusion processes (2006) (49)
- Representation Policy Iteration (2005) (48)
- Constructing basis functions from directed graphs for value function approximation (2007) (45)
- Hierarchical learning and planning in partially observable markov decision processes (2002) (45)
- Estimating Student Proficiency Using an Item Response Theory Model (2006) (44)
- Sparse Q-learning with Mirror Descent (2012) (43)
- Optimizing for the Future in Non-Stationary MDPs (2020) (43)
- Global Convergence to the Equilibrium of GANs using Variational Inequalities (2018) (42)
- A learning apprentice system for VLSI design (1986) (41)
- Continuous-Time Hierarchical Reinforcement Learning (2001) (39)
- Learning hierarchical models of activity (2004) (39)
- Manifold Alignment Preserving Global Geometry (2013) (38)
- Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes (1999) (37)
- An Expert System for Assigning Patients into Clinical Trials Based on Bayesian Networks (1998) (37)
- Manifold Warping: Manifold Alignment over Time (2012) (37)
- Efficient Hyper-parameter Optimization for NLP Applications (2015) (36)
- Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network (2005) (36)
- Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels (2006) (36)
- Learning Representation and Control in Continuous Markov Decision Processes (2006) (34)
- Evaluating the Feasibility of Learning Student Models from Data (2005) (33)
- Verification-based learning: a generalisation strategy for inferring problem-reduction methods (1985) (33)
- Comparison of univariate and multivariate models for prediction of major and minor elements from laser-induced breakdown spectra with and without masking (2016) (32)
- Imagination Machines: A New Challenge for Artificial Intelligence (2018) (30)
- Hierarchical Average Reward Reinforcement Learning (2007) (30)
- Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (1996) (30)
- A GPU-Based Approximate SVD Algorithm (2011) (29)
- Face Recognition Using Foveal Vision (2000) (28)
- Machine Learning for Robots A Comparison of Di erent Paradigms (2002) (28)
- Representation Discovery using Harmonic Analysis (2008) (27)
- Learning hierarchical observable Markov decision process models for robot navigation (2001) (25)
- Learning state-action basis functions for hierarchical MDPs (2007) (25)
- Basis Construction from Power Series Expansions of Value Functions (2010) (24)
- The NSF Workshop on Reinforcement Learning: Summary and Observations (1996) (24)
- An Average-Reward Reinforcement Learning Algorithm for Computing Bias-Optimal Policies (1996) (23)
- A Unified Framework for Domain Adaptation using Metric Learning on Manifolds (2018) (23)
- An Apprentice-Based Approach to Knowledge Acquisition (1993) (21)
- Coarticulation: an approach for generating concurrent plans in Markov decision processes (2005) (21)
- Multiscale analysis of document corpora based on diffusion models (2009) (21)
- Rapid Concept Learning for Mobile Robots (1998) (20)
- Projected Natural Actor-Critic (2013) (20)
- Deep Reinforcement Learning With Macro-Actions (2016) (19)
- Aligning Mixed Manifolds (2015) (19)
- Using Determinations in EBL: A Solution to the incomplete Theory Problem (1989) (18)
- Proximal Gradient Temporal Difference Learning Algorithms (2016) (18)
- Reconfigurable adaptable micro-robot (1999) (18)
- Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (2007) (18)
- Introduction to Robot Learning (1993) (18)
- Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization (2007) (17)
- Hybrid least-squares algorithms for approximate policy evaluation (2009) (17)
- A multiagent reinforcement learning algorithm by dynamically merging markov decision processes (2002) (16)
- A geometric framework for transfer learning using manifold alignment (2010) (15)
- Robust Mobile Robot Navigation using Partially-Observable Semi-Markov Decision Processes (1999) (15)
- Fast Spectral Learning using Lanczos Eigenspace Projections (2008) (15)
- Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions (2015) (15)
- Basis function construction for hierarchical reinforcement learning (2010) (15)
- Hierarchically Optimal Average Reward Reinforcement Learning (2002) (15)
- Rapid Concept Learning for Mobile Robots (2004) (14)
- Proximal methods for calibration transfer (2017) (14)
- Sparse Approximate Policy Evaluation using Graph-based Basis Functions (2009) (12)
- A Fully Customized Baseline Removal Framework for Spectroscopic Applications (2017) (11)
- Hierarchical Reinforcement Learning Using Graphical Models (11)
- Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods (1985) (11)
- Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity (2018) (11)
- Basis Adaptation for Sparse Nonlinear Reinforcement Learning (2013) (11)
- The National Science Foundation Workshop on Reinforcement Learning (1996) (11)
- Learning the hierarchical structure of spatial environments using multiresolution statistical models (2002) (10)
- Hierarchical reinforcement learning in continuous state and multi-agent environments (2005) (10)
- Learning to Communicate and Act in Cooperative Multiagent Systems using Hierarchical Reinforcement Learning (2004) (10)
- Spatial and Temporal Abstractions in POMDPs Applied to Robot Navigation (2005) (9)
- Representation Discovery in Sequential Decision Making (2010) (9)
- Basis construction and utilization for markov decision processes using graphs (2010) (8)
- On the Tractability of Learning from Incomplete Theories (1988) (8)
- Extraction of Key Words from News Stories (2004) (8)
- Coarticulation in Markov Decision Processes (2004) (8)
- Personalizing with Human Cognitive Biases (2019) (8)
- Multiscale Manifold Learning (2013) (8)
- Multiscale Dimensionality Reduction Based on Diffusion Wavelets (2009) (7)
- An apprentice-based approach to learning problem-solving knowledge (1990) (7)
- Concurrent decision making in markov decision processes (2006) (7)
- Eye Movements in Human Face Learning and Recognition (2000) (7)
- Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization (2010) (7)
- Quantifying Prior Determination Knowledge Using the PAC Learning Model (1994) (6)
- Compressive Reinforcement Learning with Oblique Random Projections (2011) (6)
- Quantifying prior determination knowledge using the PAC learning model (2004) (6)
- Online Monotone Games (2017) (6)
- Sparse Manifold Alignment (2012) (5)
- Spatiotemporal Abstraction of Stochastic Sequential Processes (2002) (5)
- Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds (2015) (5)
- Action-based representation discovery in markov decision processes (2009) (5)
- Manifold Spanning Graphs (2014) (5)
- Online Monotone Optimization (2016) (4)
- Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension (2023) (4)
- Causal Inference in Network Economics (2021) (4)
- A Greedy Divide-and-Conquer Approach to Optimizing Large Manufacturing Systems using Reinforcement Learning (1998) (4)
- Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory (2022) (4)
- Hierarchical Map Learning For Robot Navigation (2000) (4)
- Kalman filters for prediction and tracking in an adaptive sensor network (2005) (4)
- An Over-parameterized Exponential Regression (2023) (4)
- On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property (2022) (3)
- Automatic Whole-Spectrum Matching (2015) (3)
- Causal Homotopy (2021) (3)
- Inverting Variational Autoencoders for Improved Generative Accuracy (2016) (3)
- Finding Equilibria in Large Games using Variational Inequalities (2015) (3)
- BASELINE REMOVAL IN RAMAN SPECTROSCOPY : OPTIMIZATION TECHNIQUES (2015) (2)
- Extending Hierarchical Reinforcement Learning to Continuous-Time, Average-Reward, and Multi-Agent Models (2003) (2)
- Categoroids: Universal Conditional Independence (2022) (2)
- Representation Discovery in Planning using Harmonic Analysis (2007) (2)
- Learning to Plan Using Harmonic Analysis of Diffusion Models (2007) (2)
- Value Function Approximation with Diffusion Wavelets and (2005) (2)
- Modeling Context in Cognition Using Variational Inequalities (2014) (2)
- An Optimization Perspective on Baseline Removal for Spectroscopy (2015) (2)
- A Manifold Approach to Learning Mutually Orthogonal Subspaces (2017) (2)
- CALIBRATION TRANSFER OF LIBS SPECTRA TO CORRECT FOR MARS-EARTH LAB (2015) (2)
- PRELIMINARY CALIBRATION FOR MEASURING FERRIC IRON IN SILICATE GLASSES : A MÖSSBAUER AND X-RAY ABSORPTION SPECTROSCOPY STUDY (2015) (2)
- A Variational Learning Algorithm for the Abstract Hidden Markov Model (2005) (2)
- Solving Large Sustainable Supply Chain Networks Using Variational Inequalities (2015) (2)
- Baseline Removal in LIBS and FTIR Spectroscopy: Optimization Techniques (2015) (1)
- Learning to Cooperate using Hierarchical Reinforcement Learning (2006) (1)
- Universal Decision Models (2021) (1)
- Manifold Learning for Regression of Mars Spectra (2015) (1)
- Multiscale Manifold Alignment (2010) (1)
- Transfer Learning and Representation Discovery in Intelligent Tutoring Systems (2009) (1)
- Cr, Ni, Mn, Co, Zn, and S Standards for Use in Laser-Induced Breakdown Spectroscopy on Mars (2015) (1)
- Redox State of Iron in Lunar Glasses using X-ray Absorption Spectroscopy and Multivariate Analysis (2014) (1)
- Errata Preface Recent Advances in Hierarchical Reinforcement Learning (2003) (1)
- Jointly Learning Data-Dependent Label and Locality-Preserving Projections (2011) (0)
- Deep Generative Models for Spectroscopic Analysis on Mars (2016) (0)
- 11 Hierarchical Approaches to Concurrency , Multiagency , and Partial Observability (2004) (0)
- Nav RNav G Nav Y Root Cooperative Subtask Communicate Not − (0)
- Remediating disengagement with non-invasive interventions (0)
- Asymptotic Causal Inference (2021) (0)
- Report of the 1996 Workshop on Reinforcement Learning Sponsored by the National Science Foundation Preface and Acknowledgements (2007) (0)
- Learning Locality-Preserving Discriminative Features (2010) (0)
- Fully-Customized Baseline Removal Applied to LIBS Spectroscopy Under Mars Conditions (2016) (0)
- Hierarchical Learning and Planning Using Multi-Scale Models (2000) (0)
- Finite-Sample Analysis of GTD Algorithms (2020) (0)
- n Average- ent Learning Algorit (1996) (0)
- Successes and Challenges of Laser-Induced Breakdown Spectroscopy (LIBS) Applied to Chemical Analyses of Geological Samples. (2014) (0)
- Hierarchical Average Reward Reinforcement Learning Hierarchical Average Reward Reinforcement Learning (2007) (0)
- STUDENT PAPER : A Multiagent Reinforcement Learning Algorithm by Dynamically Merging Markov Decision Processes (2002) (0)
- Award FA 9550-10-1-0383 : Learning Representation and Control in Markov Decision Processes : Final Report (2009) (0)
- Universal Causality (2023) (0)
- U NIFYING C AUSAL I NFERENCE AND R EINFORCEMENT L EARNING USING H IGHER -O RDER C ATEGORY T HEORY ∗ (2022) (0)
- DEEP LEARNING MODELS FOR SPECTROSCOPIC DATA : SEMI-SUPERVIED GENERATIVE MODELS APPLIED TO LASER-INDUCED BREAKDOWN SPECTROSCOPIC DATA (2017) (0)
- A Layered Architecture for Universal Causality (2022) (0)
- Face Recognition Using Foveal Vision : Preliminary Report (2007) (0)
- N Average- Ent Learning Algorit Mot Ivat Ion (1999) (0)
- Smoothed Online Combinatorial Optimization Using Imperfect Predictions (2022) (0)
- Multiscale Manifold Warping (2021) (0)
- Reconfigurable and Adaptable Micro-Robots (2004) (0)
- Inverting VAEs for Improved Generative Accuracy (2017) (0)
- Learning to Act using Concurrent Temporally Extended Actions (2007) (0)
- A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions (1999) (0)
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What Schools Are Affiliated With Sridhar Mahadevan?
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