Pages that link to "Prasad Tadepalli"
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> Prasad TadepalliThe following pages link to Prasad Tadepalli:
View (previous 50) (next 50) (20 | 50 | 100 | 250 | 500)- Publication (← links)
- James A. Hendler (← links)
- Active Learning with Committees for Text Categorization (← links)
- Active Learning with Committees (← links)
- Auto-Exploratory Average Reward Reinforcement Learning (← links)
- Gradient Boosting for Sequence Alignment (← links)
- Learning Goal-Decomposition Rules Using Exercises (← links)
- A Theory of Unsupervised Speedup Learning (← links)
- Optimizing the Predictive Value of Diagnostic Decision Rules (← links)
- Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem (← links)
- A Formalization of Explanation-Based Macro-operator Learning (← links)
- A Decision-Theoretic Model of Assistance (← links)
- An ensemble learning and problem solving architecture for airspace management (← links)
- Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers (← links)
- Automatic discovery and transfer of MAXQ hierarchies (← links)
- Logical Hierarchical Hidden Markov Models for Modeling User Activities (← links)
- Guest editors' introduction: special issue on inductive logic programming (ILP-2007) (← links)
- Structured machine learning: the next ten years (← links)
- Transfer in variable-reward hierarchical reinforcement learning (← links)
- Multi-task reinforcement learning: a hierarchical Bayesian approach (← links)
- Learning for efficient retrieval of structured data with noisy queries (← links)
- A Relational Hierarchical Model for Decision-Theoretic Assistance (← links)
- A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems (← links)
- Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies (← links)
- Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery (← links)
- Dynamic preferences in multi-criteria reinforcement learning (← links)
- Learning first-order probabilistic models with combining rules (← links)
- Model-based Hierarchical Average-reward Reinforcement Learning (← links)
- Learning Decision Rules by Randomized Iterative Local Search (← links)
- On Exact Learning of Unordered Tree Patterns (← links)
- Exact Learning of Unordered Tree Patterns from Queries (← links)
- Learning Horn Definitions: Theory and an Application to Planning (← links)
- Exact Learning of Tree Patterns from Queries and Counterexamples (← links)
- Learning First-Order Acyclic Horn Programs from Entailment (← links)
- Model-Based Average Reward Reinforcement Learning (← links)
- Learning from Examples and Membership Queries with Structured Determinations (← links)
- Learning Goal-Decomposition Rules using Exercises (← links)
- Hierarchical Explanation-Based Reinforcement Learning (← links)
- Learning Horn Definitions with Equivalence and Membership Queries (← links)
- Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (← links)
- Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (← links)
- A Formal Framework for Speedup Learning from Problems and Solutions (← links)
- Quantifying Prior Determination Knowledge Using the PAC Learning Model (← links)
- Learning from Queries and Examples with Tree-structured Bias (← links)
- An Apprentice-Based Approach to Knowledge Acquisition (← links)
- Learning with Incrutable Theories (← links)
- Maximizing the Predictive Value of Production Rules (← links)
- Planning Approximate Plans for Use in the Real World (← links)
- On the Tractability of Learning from Incomplete Theories (← links)
- Two New Frameworks for Learning (← links)
