Integrating Visual and Range Data for Robotic Object Detection (2008)
Gould, Stephen, Baumstarck, Paul, Quigley, Morgan, Ng, Andrew Y., Koller, Daphne
The problem of object detection and recognition is a notoriously difficult one, and one that has been the focus of much work in the computer vision and robotics communities. Most work has...
Integrating Visual and Range Data for Robotic Object Detection (2008)
Gould, Stephen, Baumstarck, Paul, Quigley, Morgan, Ng, Andrew Y., Koller, Daphne
The problem of object detection and recognition is a notoriously difficult one, and one that has been the focus of much work in the computer vision and robotics communities. Most work has...
Ignorable Information in Multi-Agent Scenarios (2008)
In some multi-agent scenarios, identifying observations that an agent can safely ignore reduces exponentially the size of the agent's strategy space and hence the time required to find a Nash...
Ignorable Information in Multi-Agent Scenarios (2008)
In some multi-agent scenarios, identifying observations that an agent can safely ignore reduces exponentially the size of the agent's strategy space and hence the time required to find a Nash...
Wang, Haidong, Segal, Eran, Ben-Hur, Asa, Li, Qian-Ru, Vidal, Marc, Koller, Daphne
Abstract We propose InSite, a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We compared our...
A Continuation Method for Nash Equilibria in Structured Games (2006)
Blum, Ben, Shelton, Christian R, Koller, Daphne
Structured game representations have recently attracted interest as models for multiagent artificial intelligence scenarios, with rational behavior most commonly characterized by Nash equilibria....
Thrun, Sebastian, Koller, Daphne, Ghahramani, Zoubin, Durrant-Whyte, Hugh, Ng, Andrew Y.
This paper describes a scalable algorithm for the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot....
Thrun, Sebastian, Koller, Daphne, Ghahramani, Zoubin, Durrant-Whyte, Hugh
This paper describes a scalable algorithm for the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot....
Learning Associative Markov Networks (2004)
Ben Taskar, Vassil Chatalbashev, Daphne Koller
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bioinformatics.
Multi-Agent Planning in Complex Uncertain Environments (2004)
Many tasks require a team of agents to act together in a coordinated way in a complex, uncertain environment. Examples include search and rescue, control of a complex system such as a factory, or...
Recovering Articulated Object Models from 3D Range Data (2004)
Dragomir Anguelov, Daphne Koller, Hoi-cheung Pang, Praveen Srinivasan, Sebastian Thrun
We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to di#erent...
FastSLAM: A Factored Solution to the Simultaneous (2004)
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem...
FastSLAM: An Efficient Solution to the Simultaneous (2004)
Sebastian Thrun, Michael Montemerlo, Daphne Koller, Ben Wegbreit, Juan Nieto, Eduardo Nebot
This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association...
Efficient Computation of Equilibria for (2004)
Daphne Koller, Nimrod Megiddo, Bernhard Von Stengel
The Nash equilibria of a two-person, non-zero-sum game are the solutions of a certain linear complementarity problem (LCP). In order to use this for solving a game in extensive form, it is first...
Constructing Small Sample Spaces Satisfying Given (2004)
Daphne Koller, Nimrod Megiddo Y
Abstract. The subject of this paper is finding small sample spaces for joint distributions of n discrete random variables. Such distributions are often only required to obey a certain limited set of...
Fast Algorithms for Finding (2004)
Daphne Koller, Nimrod Megiddo, Bernhard Von Stengel
Interactions among agentscanbeconveniently described by game trees. In order to analyze a game, it is important to derive optimal (or equilibrium) strategies for the differentplayers. The standard...
Finding Mixed Strategies with Small Supports (2004)
Extensiveform Games, Daphne Koller, Nimrod Megiddo
The complexity of algorithms that compute strategies or operate on them typically depends on the representation length of the strategies involved. One measure for the size of a mixed strategy is the...
The ComplexityofTwo-Person Zero-Sum Games (2004)
This paper investigates the complexity of finding max-min strategies for finite two-person zero-sum games in the extensive form. The problem of determining whether a player with imperfect recall can...
Joseph Y. Halpern, Daphne Koller
Non-deductive reasoning systems are often representation dependent: representing the same situation in two di#erent ways may cause such a system to return two di#erent answers.
Max-Margin Markov Networks (2004)
Ben Taskar, Carlos Guestrin, Daphne Koller
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence...
Max-Margin Markov Networks (2004)
Ben Taskar, Carlos Guestrin, Daphne Koller
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence...
Link Prediction in Relational Data (2004)
Ben Taskar, Ming-fai Wong, Pieter Abbeel, Daphne Koller
Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between...
Representation Dependence in Probabilistic Inference (2003)
Halpern, Joseph Y., Koller, Daphne
Non-deductive reasoning systems are often {\em representation dependent}: representing the same situation in two different ways may cause such a system to return two different answers. Some have...
Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model...
From Statistical Knowledge Bases to Degrees of Belief (2003)
Bacchus, Fahiem, Grove, Adam, Halpern, Joseph Y., Koller, Daphne
An intelligent agent will often be uncertain about various properties of its environment, and when acting in that environment it will frequently need to quantify its uncertainty. For example, if the...
Learning Module Networks (2003)
Eran Segal, Bauer Ctr, Daphne Koller, Nir Friedman
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Learning Module Networks (2003)
Eran Segal, Bauer Ctr, Daphne Koller, Nir Friedman
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
In [15] , Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of...
Module Networks: Discovering Regulatory (2003)
Eran Segal, David Botstein, Daphne Koller, Nir Friedman
Introduction The complex functions of a living cell are carried out through the concerted activity of many genes and gene products. This activity is often coordinated by the organization of Computer...
Generalizing Plans to New Environments in Relational MDPs (2003)
Carlos Guestrin, Daphne Koller, Chris Gearhart, Neal Kanodia
A longstanding goal in planning research is the ability to generalize plans developed for some set of environments to a new but similar environment, with minimal or no replanning.
Simultaneous Mapping and Localization With Sparse (2003)
Sebastian Thrun, Daphne Koller, Zoubin Ghahramani, Hugh Durrant-whyte, Andrew Y. Ng
This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot....
Learning Probabilistic Models of Link Structure (2002)
Lise Getoor, Nit Friedman, Daphne Koller, Ben Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Hybrid Bayesian Networks for Reasoning about Complex Systems (2002)
Many real-world systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition,...
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem (2002)
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem...
Sebastian Thrun, Daphne Koller, Zoubin Ghahmarani, Hugh Durrant-whyte
This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot....
Multi-Agent Algorithms for Solving Graphical Games (2002)
Consider the problem of a group of agents trying to find a stable strategy profile for a joint interaction. A standard approach is to describe the situation as a single multi-player game and find an...
Learning Hierarchical Object Maps Of Non-Stationary Environments With Mobile Robots (2002)
Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson Limketkai, Scott Sanner, Sebastian Thrun
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we...
Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net (2002)
Uri Lerner, Brooks Moses, Maricia Scott, Sheila Mcilraith, Daphne Koller
The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human...
Context-Specific Multiagent Coordination and Planning with Factored MDPs (2002)
Carlos Guestrin, Shobha Venkataraman, Daphne Koller
We present an algorithm for coordinated decision making in cooperative multiagent settings, where the agents' value function can be represented as a sum of context-specific value rules. The task of...
First-Order Conditional Logic Revisited* (2002)
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem (2002)
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem...
Application of the decision-theoretic paradigm implicitly assumes knowledge of the utility values assigned to the relevant outcomes by the person a#ected by the decisions. However, in many cases, the...
Multiagent Planning with Factored MDPs (2002)
Carlos Guestrin, Daphne Koller, Ronald Parr
We present a principled and efficient planning algorithm for cooperative multiagent dynamic systems. A striking feature of our method is that the coordination and communication between the agents is...
Probabilistic Abstraction Hierarchies (2002)
Eran Segal, Daphne Koller, Dirk Ormoneit
Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar. In this paper, we provide a general probabilistic...
From Promoter Sequence to Expression: A Probabilistic Framework (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Context-Specific Independence in Bayesian Networks (2002)
Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem (2002)
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem...
Probabilistic Hierarchical Clustering for Biological Data (2002)
Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to "nearby" classes in the tree are similar. Most...
From Promoter Sequence to Expression: (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Context Specific Multiagent Coordination and Planning with Factored MDPs (2002)
Carlos Guestrin, Shobha Venkataraman, Daphne Koller
We present a new, principled and efficient algorithm for decision making and planning cooperative multi-agent dynamic systems. We consider systems where the agents' value function is a sum of local...
Simon Tong, Daphne Koller, Pack Kaelbling
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected...
Active Learning for Structure in Bayesian Networks (2002)
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically...
Solving Factored POMDPs with Linear Value Functions (2001)
Carlos Guestrin, Daphne Koller, Ronald Parr
Partially Observable Markov Decision Processes (POMDPs) provide a coherent mathematical framework for planning under uncertainty when the state of the system cannot be fully observed. However, the...
Multi-Agent Influence Diagrams for Representing and Solving Games (2001)
The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in real-world games. In this paper, we propose a new...
Support Vector Machine Active Learning with Applications to Text Classification (2001)
Simon Tong, Daphne Koller, Pack Kaelbling
Support vector machines have met with signi cant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected...
Carlos Guestrin, Daphne Koller, Ronald Parr
Markov Decision Processes (MDPs) provide a coherent mathematical framework for planning under uncertainty.
Active Learning for Structure in Bayesian Networks (2001)
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically...
Selectivity Estimation using Probabilistic Models (2001)
Lise Getoor, Ben Taskar, Daphne Koller
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It...
Multi-Agent Influence Diagrams for Representing and Solving Games (2001)
The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in real-world games. In this paper, we propose a new...
Multiagent Planning with Factored MDPs (2001)
Carlos Guestrin, Daphne Koller, Ronald Parr
We present a new, principled and efficient planning algorithm for cooperative multi-agent dynamic systems. A striking feature of our method is that the coordination and communication between the...
Support Vector Machine Active Learning with Applications to Text Classification (2001)
. Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected...
Probabilistic Models of Text and Link Structure for Hypertext Classification (2001)
Lise Getoor, Eran Segal, Ben Taskar, Daphne Koller
Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated.
Support Vector Machine Active Learning with Applications to Text Classification (2001)
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected...
Discovering Hidden Variables: A Structure-Based Approach (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Probabilistic Abstraction Hierarchies (2001)
Eran Segal, Daphne Koller, Dirk Ormoneit
Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar. In this paper, we provide a general probabilistic...
Exact Inference in Networks with Discrete Children of Continuous Parents (2001)
Uri Lerner, Eran Segal, Daphne Koller
Many real life domains contain a mixture of discrete
. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent...
Probabilistic Models of Text and Link Structure for Hypertext Classification (2001)
Lise Getoor, Eran Segal, Ben Taskar, Daphne Koller
Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated. For example,...
Learning Probabilistic Models of Relational Structure (2001)
Lise Getoor, Nit Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
Probabilistic Classification and Clustering in Relational Data (2001)
Ben Taskar, Eran Segal, Daphne Koller
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational...
Solving Factored POMDPs with Linear Value Functions (2001)
Carlos Guestrin, Daphne Koller, Ronald Parr
Partially Observable Markov Decision Processes
Learning an Agent's Utility Function by Observing Behavior (2001)
Urszula Chajewska, Daphne Koller, Dirk Ormoneit
This paper considers the task of predicting the future decisions of an agent A based on his past decisions. We assume that A is rational - he uses the principle of maximum expected utility. We also...
Max-norm Projections for Factored MDPs (2001)
Carlos Guestrin, Daphne Koller, Ronald Parr
Markov Decision Processes (MDPs) provide a coherent mathematical framework for planning under uncertainty. However, exact MDP solution algorithms require the manipulation of a value function, which...
Rich Probabilistic Models for Gene Expression (2001)
Eran Segal, Ben Taskar, Audrey Gasch, Nir Friedman, Daphne Koller
Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist...
Max-norm Projections for Factored MDPs (2001)
Carlos Guestrin, Daphne Koller, Ronald Parr
Markov Decision Processes (MDPs) provide a coherent mathematical framework for planning under uncertainty.
Selectivity Estimation using Probabilistic Models (2001)
Lise Getoor, Ben Taskar, Daphne Koller
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It...
Efficient Reinforcement Learning in Factored MDPs (2001)
We present a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can be factored as a dynamic Bayesian network (DBN).
Hierarchically Classifying Documents Using Very Few Words (2001)
The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which...