Roni Khardon

Learning Horn Expressions with LogAn-H Appears in the Proceedings of ICML 2000 (2007)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

In Proceedings of NIPS'01 Efficiency versus Convergence of Boolean (2007)

The paper introduces LogAn-H--- a system for learning first-order function-free Horn expressions from interpretations. The system is based on an interactive algorithm (that asks questions) that was...

Polynomial Certificates for Propositional Classes (2007)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features.

Marta Arias, Roni Khardon, Rocco A. Servedio

This paper studies the query complexity of learning classes of expressions in propositional logic from equivalence and membership queries. We give new constructions of polynomial size certificates of...

Maximum Margin Algorithms with Boolean Kernels (2007)

On mining closed sets in multi-relational data (2007)

Recent work has introduced Boolean kernels with which one can learn over a feature space containing all conjunctions of length up to k (for any 1 k n) over the original n Boolean features in the...

Garriga, Gemma, Khardon, Roni, De Raedt, Luc

We investigate the problem of mining closed sets in multi-relational databases. Previous work introduced different semantics and associated algorithms for mining closed sets in multi-relational...

First order decision diagrams for relational MDPs (2007)

Chenggang Wang, Saket Joshi, Roni Khardon

Dynamic programming algorithms provide a basic tool identifying optimal solutions in Markov Decision Processes (MDP). The paper develops a representation for decision diagrams suitable for describing...

First order decision diagrams for relational MDPs (2007)

Chenggang Wang, Saket Joshi, Roni Khardon

Dynamic programming algorithms provide a basic tool identifying optimal solutions in Markov Decision Processes (MDP). The paper develops a representation for decision diagrams suitable for describing...

First order decision diagrams for relational MDPs (2007)

Saket Joshi, Roni Khardon, Chenggang Wang

Dynamic programming algorithms provide a basic tool identifying optimal solutions in Markov Decision Processes (MDP). The paper develops a representation for decision diagrams suitable for describing...

On mining closed sets in multi-relational data (2007)

Khardon, Roni

http://www.ijcai.org/papers/Papers/IJCAI07-129.pdf

Noise tolerant variants of the perceptron algorithm (2005)

Roni Khardon, Gabriel Wachman, J. Collins

A large number of variants of the Perceptron algorithm have been proposed and partially evaluated in recent work. One type of algorithm aims for noise tolerance by replacing the last hypothesis of...

Abstract (2005)

Marta Arias, Jérôme Maloberti, Roni Khardon

The paper introduces LogAn-H — a system for learning first-order function-free Horn expressions from interpretations. The system is based on an algorithm that learns by asking questions and which...

Noise tolerant variants of the perceptron algorithm (2005)

Roni Khardon, Gabriel M. Wachman

Abstract. A large number of variants of the Perceptron algorithm have been proposed and partially evaluated in recent work. One type of algorithm aims for noise tolerance by replacing the last...

The subsumption lattice and query learning (2004)

Marta Arias, Roni Khardon, Marta Arias, Roni Khardon

The paper identifies several new properties of the lattice induced by the subsumption relation over first-order clauses and derives implications of these for learnability. In particular, it is shown...

Abstract (2004)

Marta Arias, Roni Khardon, Aaron Feigelson, Rocco A. Servedio, Marta Arias, Roni Khardon, ...

This paper studies the complexity of learning classes of expressions in propositional logic from equivalence queries and membership queries. In particular, we focus on bounding the number of queries...

Bottom-up ILP using large refinement steps (2004)

Maximum margin algorithms with Boolean kernels (2003)

Abstract. The LogAn-H system is a bottom up ILP system for learning multi-clause and multi-predicate function free Horn expressions in the framework of learning from interpretations. The paper...

Complexity parameters of first order classes (2003)

Abstract. Recent work has introduced Boolean kernels with which one can learn over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ≤ n) over the original n Boolean...

Discovering all most specific sentences (2003)

Abstract. We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of...

Dimitrios Gunopulos, Roni Khardon, Ram Sewak Sharma

Data mining can be viewed, in many instances, as the task of computing a representation of a theory of a model or a database, in particular by finding a set of maximally specific sentences satisfying...

Discovering All Most Specific Sentences (2003)

Dimitrios Gunopulos, Roni Khardon, Heikki Mannila, Sanjeev Saluja, Hannu Toivonen, Ram Sewak Sharma

Data mining can be viewed, in many cases, as the task of computing a representation of a theory...

Complexity parameters of first order classes (2003)

Maximum margin algorithms with Boolean kernels (2003)

Abstract. We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of...

Maximum margin algorithms with Boolean kernels (2003)

Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ≤ n) over the...

Complexity parameters of first order classes (2003)

Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ≤ n) over the...

Marta Arias, Marta Arias, Roni Khardon, Roni Khardon

Abstract. We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of...

Polynomial certificates for propositional classes (2003)

Marta Arias, Roni Khardon, Rocco A. Servedio

Abstract. This paper studies the query complexity of learning classes of expressions in propositional logic from equivalence and membership queries. We give new constructions of polynomial size...

Learning Closed Horn Expressions (2002)

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

This paper considers the problem of learning an unknown rst order expression T (often called target expression) from examples of clauses that T entails or does not entail. This type of learning...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Roni Khardon, Dan Roth, Rocco A. Servedio

The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational...

Roni Khardon, Dan Roth, Rocco A. Servedio

The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing...

Efficiency versus convergence of boolean kernels for on-line learning algorithms (2001)

Roni Khardon, Dan Roth, Rocco A. Servedio

The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing...

A New Algorithm for Learning Range Restricted Horn Expressions (2000)

A New Algorithm for Learning Range Restricted Horn Expressions (2000)

Abstract. A learning algorithm for the class of range restricted Horn expressions is presented and proved correct. The algorithm works within the framework of learning from entailment, where the goal...

Marta Arias, Marta Arias, Marta Arias, Roni Khardon, Roni Khardon, Roni Khardon

A learning algorithm for the class of range restricted Horn expressions is presented and proved correct. The algorithm works within the framework of learning from entailment, where the goal is to...

A New Algorithm for Learning Range Restricted Horn Expressions (2000)

Learning Inequated Range Restricted Horn Expressions (2000)

A learning algorithm for the class of range restricted Horn expressions is presented and proved correct. The algorithm works within the framework of learning from entailment, where the goal is to...

Learning Horn Expressions with LogAn-H (2000)

. A learning algorithm for the class of inequated range restricted Horn expressions is presented and proved correct. The main property of this class is that all the terms in the conclusion of a...

Learning Inequated Range Restricted Horn Expressions (2000)

The paper introduces LogAn-H --- a system for learning first-order function-free Horn expressions from interpretations. The system is based on an interactive algorithm (that asks questions) that was...

Marta Arias, Marta Arias, Marta Arias, Roni Khardon, Roni Khardon, Roni Khardon

A learning algorithm for the class of inequated range restricted Horn expressions is presented and proved correct. The main property of this class is that all the terms in the conclusion of a clause...

A New Algorithm for Learning Range Restricted Horn Expressions (2000)

Learning action strategies for planning domains (1999)

Abstract. A learning algorithm for the class of range restricted Horn expressions is presented and proved correct. The algorithm works within the framework of learning from entailment, where the goal...

Learning Action Strategies for Planning Domains (1999)

This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a...

Relational Learning for NLP using Linear Threshold Elements (1999)

This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a...

Roni Khardon, Dan Roth, Leslie G. Valiant

We describe a coherent view of learning and reasoning with relational representations in the context of natural language processing. In particular, we discuss the Neuroidal Architecture, Inductive...

Learning Range Restricted Horn Expressions (1999)

Roni Khardon, Edinburgh Eh Jz

. We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of...

Relational Learning for NLP using Linear Threshold Elements (1999)

Roni Khardon, Dan Roth, Leslie G. Valiant

We describe a coherent view of learning and reasoning with relational representations in the context of natural language processing. In particular, we discuss the Neuroidal Architecture, Inductive...

Learning Function-Free Horn Expressions (1998)

Learning Function-Free Horn Expressions (1998)

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the...

Learning to Reason with a Restricted View (1998)

. The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including...

Roni Khardon, K Dan Roth

The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge....

Learning to Take Actions (1998)

Learning Function-Free Horn Expressions (1998)

We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. We then identify a...

Learning Range Restricted Horn Expressions (1998)

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the...

Reasoning with Examples: Propositional Formulae and Database Dependencies (1998)

We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of expressions where every term in the consequent of a clause appears also in...

Roni Khardon, Heikki Mannila, Dan Roth

For humans, looking at how concrete examples behave is an intuitive way of deriving conclusions. The drawback with this method is that it does not necessarily give the correct results. However, under...

On Learning Read-k-Satisfy-j DNF (1998)

Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, Dan Roth, ...

.<F3.824e+05> We study the learnability of<F3.347e+05><F3.824e+05><F3.347e+05> read-k-satisfy-j<F3.824e+05> (RkSj) DNF formulas. These are boolean formulas in...

The King’s Buildings Edinburgh EH9 3JZ Scotland (1998)

Data mining, hypergraph transversals, and machine learning (1997)

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the...

Dimitrios Gunopulos, Roni Khardon, Heikki Mannila, Hannu Toivonen

Several data mining problems can be formulated as problems of finding maximally specific sentences that are interesting in a database. We first show that this problem has a close relationship with...

Data mining, Machine Learning (1997)

Dimitrios Gunopulos, Roni Khardon, Heikki Mannila, Hannu Toivonen

Several data mining problems can be formulated as problems of finding maximally specific sentences that are interesting in a database. We first show that this problem has a close relationship with...

Defaults and Relevance in Model Based Reasoning (1997)

L2Act: User Manual (1997)

Reasoning with model-based representations is an intuitive paradigm, which has been shown to be theoretically sound and to possess some computational advantages over reasoning with formula-based...

Data mining, Hypergraph Transversals, and Machine Learning (1997)

Introduction This note describes the system L2Act, the options it includes, and how to use it. We assume knowledge of the general ideas behind the system [1], as well as some details on the...

Dimitrios Gunopulos, Roni Khardon, Heikki Mannila, Hannu Toivonen

Several data mining problems can be formulated as problems of finding maximally specific sentences that are interesting in a database. We first show that this problem has a close relationship with...

Defaults and Relevance in Model Based Reasoning (1997)

Learning to be competent /--a thesis presented by Roni Khardon. (1996)

Reasoning with model-based representations is an intuitive paradigm, which has been shown to be theoretically sound and to possess some computational advantages over reasoning with formula-based...

Khardon, Roni.

Thesis (Ph. D.)--Harvard University, 1996.

Learning to be Competent (1996)

Learning to Reason (1996)

The thesis presents a new approach for the study of competent cognitive behavior. The approach, learning to be competent, suggests that learning phenomena and the competencies attributed to...

Roni Khardon, Dan Roth, Aiken Computation Laboratory

We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests that...

Reasoning with Models (1996)

Roni Khardon, Dan Roth, Aiken Computation Laboratory

We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of models (satisfying assignments) rather than a logical formula, and the set of queries is...

Learning to Take Actions (1996)

Reasoning with Models (1996)

We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. We then identify a...

On Learning Read-k-Satisfy-j DNF (1996)

We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of models (satisfying assignments) rather than a logical formula, and the set of queries is...

Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, Dan Roth

We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by...

Reasoning with Models (1996)

Roni Khardon, Dan Roth, Aiken Computation Laboratory, Cambridge Ma

We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of models (satisfying assignments) rather then a logical formula, and the set of queries is...

Translating between Horn representations and their characteristic models (1995)

Learning to Reason with a Restricted View (1995)

Characteristic models are an alternative, model based, representation for Horn expressions. It has been shown that these two representations are incomparable and each has its advantages over the...

Roni Khardon, Roni Khardon, Dan Roth, Dan Roth

The current emphasis of the research in learning theory is on the study of inductive learning (from examples) of concepts (binary classifications of examples). The work in AI identifies other tasks,...

Reasoning with Examples: Propositional Formulae and Database Dependencies (1995)

Roni Khardon, Roni Khardon, Heikki Mannila, Heikki Mannila, Dan Roth, Dan Roth

For humans, looking at how concrete examples behave is an intuitive way of deriving conclusions. The drawback with this method is that it does not necessarily give the correct results. However, under...

Translating between Horn Representations and their Characteristic Models (1995)

Defaults and Relevance in Model Based Reasoning (1995)

Characteristic models are an alternative, model based, representation for Horn expressions. It has been shown that these two representations are incomparable and each has its advantages over the...

Learning to Reason with a Restricted View (1995)

Reasoning with model-based representations is an intuitive paradigm, which has been shown to be theoretically sound and to possess some computational advantages over reasoning with formula-based...

Roni Khardon, Edinburgh Eh Jz, Dan Roth

The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge....

Learning to reason (1994)

On learning read-k-satisfy-j dnf (1994)

Abstract. We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests...

Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, Dan Roth

We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by...

On Learning (1994)

Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, ...

We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by...

Learning to Reason (1994)

Roni Khardon, Dan Roth, Aiken Computation Laboratory

We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here combines the interfaces to the world used by known learning models with the...

Learning to Reason (1994)

On Using the Fourier Transform to Learn Disjoint DNF (1993)

ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission...

Reasoning with Examples: Propositional Formulae and Database Dependencies

this paper. I would also like to thank Les Valiant for insightful comments. References

Roni Khardon, Heikki Mannila, Dan Roth

For humans, looking at how concrete examples behave is an intuitive way of deriving conclusions. The drawback with this method is that it does not necessarily give the correct results. However, under...

Exploiting Relevance through Model-Based Reasoning