Geoffrey J. Gordon

Geoffrey J. Gordon's AcademicInfluence.com Rankings

Geoffrey J. Gordon

Computer Science

#2900

World Rank

#3037

Historical Rank

Machine Learning

#746

World Rank

#756

Historical Rank

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Computer Science

Why Is Geoffrey J. Gordon Influential?

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According to Wikipedia, Geoffrey J. Gordon is a professor at the Machine Learning Department at Carnegie Mellon University in Pittsburgh and director of research at the Microsoft Montréal lab. He is known for his research in statistical relational learning and on anytime dynamic variants of the A* search algorithm. His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data , computational learning theory, and game theory.

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Geoffrey J. Gordon'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

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning (2010) (2246)
Relational learning via collective matrix factorization (2008) (1133)
ARA*: Anytime A* with Provable Bounds on Sub-Optimality (2003) (748)
Anytime Dynamic A*: An Anytime, Replanning Algorithm (2005) (632)
Stable Function Approximation in Dynamic Programming (1995) (607)
Automatic Database Management System Tuning Through Large-scale Machine Learning (2017) (403)
Decentralized estimation and control of graph connectivity in mobile sensor networks (2008) (398)
Anytime Point-Based Approximations for Large POMDPs (2006) (388)
Adversarial Multiple Source Domain Adaptation (2018) (350)
Individualized Bayesian Knowledge Tracing Models (2013) (335)
Finding Approximate POMDP solutions Through Belief Compression (2011) (332)
An Empirical Study of Example Forgetting during Deep Neural Network Learning (2018) (312)
Planning in the Presence of Cost Functions Controlled by an Adversary (2003) (290)
On Learning Invariant Representations for Domain Adaptation (2019) (281)
Anytime search in dynamic graphs (2008) (258)
Closing the learning-planning loop with predictive state representations (2009) (253)
Visibility-based Pursuit-evasion with Limited Field of View (2004) (230)
Hilbert Space Embeddings of Hidden Markov Models (2010) (229)
An evaluation of machine-learning methods for predicting pneumonia mortality (1997) (222)
Approximate solutions for partially observable stochastic games with common payoffs (2004) (219)
Real-time fault diagnosis [robot fault diagnosis] (2004) (207)
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (2017) (200)
A Unified View of Matrix Factorization Models (2008) (193)
Decentralized Sensor Fusion with Distributed Particle Filters (2002) (185)
Approximate solutions to markov decision processes (1999) (170)
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees (2005) (168)
DeepArchitect: Automatically Designing and Training Deep Architectures (2017) (165)
Exponential Family PCA for Belief Compression in POMDPs (2002) (152)
On Learning Invariant Representation for Domain Adaptation (2019) (145)
Reinforcement Learning with Function Approximation Converges to a Region (2000) (140)
Query-based Workload Forecasting for Self-Driving Database Management Systems (2018) (138)
No-Regret Reductions for Imitation Learning and Structured Prediction (2010) (130)
Reduced-Rank Hidden Markov Models (2009) (129)
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems (2007) (129)
Better Motion Prediction for People-tracking (2004) (124)
Regret bounds for prediction problems (1999) (119)
Inherent Tradeoffs in Learning Fair Representations (2019) (111)
Learning low dimensional predictive representations (2004) (111)
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift (2020) (108)
Game Theoretic Control for Robot Teams (2005) (99)
Hilbert Space Embeddings of Predictive State Representations (2013) (91)
Constant size descriptors for accurate machine learning models of molecular properties. (2018) (88)
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems (2011) (81)
Automated image analysis of protein localization in budding yeast (2007) (76)
Tractable planning under uncertainty: exploiting structure (2004) (75)
Policy-contingent abstraction for robust robot control (2002) (74)
Distributed Planning in Hierarchical Factored MDPs (2002) (73)
Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions (2011) (72)
No-regret learning in convex games (2008) (71)
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data (2007) (68)
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians. (2018) (66)
RADAR: A Personal Assistant that Learns to Reduce Email Overload (2008) (66)
Decentralized planning under uncertainty for teams of communicating agents (2006) (65)
Model Uncertainty in Classical Conditioning (2003) (65)
Closed-form supervised dimensionality reduction with generalized linear models (2008) (63)
Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces (2016) (63)
Fast State Discovery for HMM Model Selection and Learning (2007) (63)
Conditional Learning of Fair Representations (2019) (62)
Auction Mechanism Design for Multi-Robot Coordination (2003) (62)
A Bayesian Matrix Factorization Model for Relational Data (2010) (62)
Planning under Uncertainty for Reliable Health Care Robotics (2003) (58)
The support vector decomposition machine (2006) (58)
Supervised Learning for Dynamical System Learning (2015) (58)
Locating moving entities in indoor environments with teams of mobile robots (2003) (57)
A Unified Approach for Learning the Parameters of Sum-Product Networks (2016) (57)
Multiple Source Domain Adaptation with Adversarial Learning (2018) (53)
POMDP Planning for Robust Robot Control (2005) (50)
Predictive State Recurrent Neural Networks (2017) (49)
Learning from Experience in Manipulation Planning: Setting the Right Goals (2011) (48)
Predictive State Temporal Difference Learning (2010) (48)
Fast Exact Planning in Markov Decision Processes (2005) (47)
No-regret Algorithms for Online Convex Programs (2006) (47)
Two Manifold Problems with Applications to Nonlinear System Identification (2012) (46)
Adaptive Sampling for Multi-Robot Wide-Area Exploration (2007) (41)
Generalized^2 Linear^2 Models (2002) (41)
A Novel Graphical Model Approach to Segmenting Cell Images (2006) (40)
Dual Policy Iteration (2018) (39)
A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data (2003) (38)
Stable Fitted Reinforcement Learning (1995) (38)
No-Regret Algorithms for Structured Prediction Problems (2005) (38)
Collapsed Variational Inference for Sum-Product Networks (2016) (36)
A Demonstration of the OtterTune Automatic Database Management System Tuning Service (2018) (35)
ARA : formal analysis (2003) (34)
Decision Support for Agent Populations in Uncertain and Congested Environments (2012) (34)
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks (2017) (34)
Generalized2 Linear2 Models (2002) (32)
First-Order Mixed Integer Linear Programming (2009) (28)
Learning Hidden Quantum Markov Models (2017) (28)
Planning for Markov Decision Processes with Sparse Stochasticity (2004) (27)
Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning (2017) (26)
A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games (2009) (26)
The Block Diagonal Infinite Hidden Markov Model (2009) (25)
A Spectral Learning Approach to Range-Only SLAM (2012) (23)
Hybrid Theorem Proving of Aerospace Systems: Applications and Challenges (2014) (23)
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games (2007) (23)
A Spectral Learning Approach to Knowledge Tracing (2013) (23)
Learning Beam Search Policies via Imitation Learning (2018) (21)
Agendas for multi-agent learning (2007) (21)
A Data-Driven Approach for Inferring Student Proficiency from Game Activity Logs (2016) (21)
Efficient Multitask Feature and Relationship Learning (2017) (20)
Towards modular and programmable architecture search (2019) (19)
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games (2006) (19)
Multi-robot coordination and competition using mixed integer and linear programs (2004) (18)
No-regret learning and a mechanism for distributed multiagent planning (2008) (17)
Generalized² Linear² Models (2003) (17)
Information Obfuscation of Graph Neural Networks (2020) (16)
Recurrent Predictive State Policy Networks (2018) (16)
Lagrangian Relaxation for Large-Scale Multi-agent Planning (2012) (15)
Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns (2008) (14)
Spectral Learning for Expressive Interactive Ensemble Music Performance (2015) (13)
Generalizing Dijkstra's Algorithm and Gaussian Elimination for Solving MDPs (2005) (13)
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation (2019) (13)
Fundamental Limits and Tradeoffs in Invariant Representation Learning (2020) (12)
Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems (2017) (12)
Learning Neural Networks with Adaptive Regularization (2019) (12)
Decomposed Mutual Information Estimation for Contrastive Representation Learning (2021) (12)
Online Fitted Reinforcement Learning (1995) (12)
Constant Size Molecular Descriptors For Use With Machine Learning (2017) (12)
Understanding and Mitigating Accuracy Disparity in Regression (2021) (12)
Practical Learning of Predictive State Representations (2017) (11)
Point-based approximations for fast POMDP solving (2006) (11)
Robust planning in domains with stochastic outcomes, adversaries, and partial observability (2006) (11)
A Reduction from Reinforcement Learning to No-Regret Online Learning (2019) (11)
Inherent Tradeoffs in Learning Fair Representation (2019) (11)
Adversarial Privacy Preservation under Attribute Inference Attack (2019) (10)
Learning to Smooth with Bidirectional Predictive State Inference Machines (2016) (8)
A novel approximate inference approach to automated classification of protein subcellular location patterns in multi-cell images (2006) (8)
Expressiveness and Learning of Hidden Quantum Markov Models (2019) (8)
Applying Metric-Trees to Belief-Point POMDPs (2003) (8)
A Generalization of SAT and #SAT for Robust Policy Evaluation (2013) (7)
Deep Generative and Discriminative Domain Adaptation (2019) (7)
Polynomials for directed graphs (1994) (7)
An Instantiation-Based Theorem Prover for First-Order Programming (2011) (7)
Planning under uncertainty in robotics (2006) (7)
Successor Feature Sets: Generalizing Successor Representations Across Policies (2021) (6)
Graph Adversarial Networks: Protecting Information against Adversarial Attacks (2020) (6)
Random Walk Features for Network-aware Topic Models (2013) (6)
Automatic state discovery for unstructured audio scene classification (2010) (5)
An Efficient, Expressive and Local Minima-Free Method for Learning Controlled Dynamical Systems (2017) (5)
Statistical Modeling of Student Performance to Improve Chinese Dictation Skills with an Intelligent Tutor (2014) (5)
Galerkin Methods for Complementarity Problems and Variational Inequalities (2013) (5)
Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources (2011) (5)
Learning Stable Multivariate Baseline Models for Outbreak Detection (2007) (5)
Exploring friend's influence in cultures in Twitter (2013) (4)
Bayesian Methods for Identifying Faults on Robots for Planetary Exploration (2007) (4)
Principled Hybrids of Generative and Discriminative Domain Adaptation (2017) (4)
Decomposition-Based Optimal Market-Based Planning for Multi-Agent Systems with Shared Resources (2011) (3)
A New View of Predictive State Methods for Dynamical System Learning (2015) (3)
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (1996) (3)
Random Walks with Random Projections (2009) (3)
Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time (2006) (3)
A Unification of Extensive-Form Games and Markov Decision Processes (2007) (3)
Predicting Structure in Handwritten Algebra Data From Low Level Features (2015) (2)
Adversarial Task-Specific Privacy Preservation under Attribute Attack (2019) (2)
Supervised Learning for Controlled Dynamical System Learning (2017) (2)
Linear Time Computation of Moments in Sum-Product Networks (2017) (2)
Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks (2018) (2)
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint (2017) (2)
Fast Solutions to Projective Monotone Linear Complementarity Problems (2012) (2)
Learning latent variable and predictive models of dynamical systems (2009) (2)
The Block Diagonal Inﬁnite Hidden Markov Model (2009) (2)
Learning Filaments (2000) (1)
No-regret algorithms for structured prediction problems — DRAFT (2005) (1)
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings (2017) (1)
Fast and Improved SLEX Analysis of High-Dimensional Time Series (2014) (1)
Mixed-Initiative Control of Autonomous Unmanned Units Under Uncertainty (2006) (1)
Efficient Computation of Moments in Sum-Product Networks (2017) (1)
Exploratory Analysis of Speedup Learning Data Using Epectation Maximization (1996) (1)
Two-Manifold Problems (2011) (1)
Online Spectral Identification of Dynamical Systems (2011) (1)
Depth without distress (2017) (1)
An Accelerated Gradient Method for Distributed Multi-Agent Planning with Factored MDPs (2011) (0)
Embedding parameters in ab initio theory to develop well-controlled approximations based on molecular similarity (2013) (0)
Sholom M. Weiss and Casimir A. Kulikowski, Computer Systems That Learn (1993) (0)
A Bayesian Matrix Model for Relational Data (2009) (0)
Strategy Transfer Learning via Parametric Deviation Sets (2011) (0)
DURING DEEP NEURAL NETWORK LEARNING (2019) (0)
A Game-Theoretic Approach to Modeling Cross-Cultural Negotiation (2013) (0)
Learning Non-Gaussian Stochastic Systems for Dynamic Textures (2007) (0)
A projection algorithm for strictly monotone linear complementarity problems (2013) (0)
Effective Multi-Model Tracking Using Team Actuation Models (2005) (0)
Fast Exact Planning in Markov Decision Processes DRAFT — please check for updates before redistributing (0)
Learning Relation Networks for Relational Retrieval (2011) (0)
Transdisciplinary Collaboration in Developing Robotic Assistive Technology for Older Adults (2011) (0)
Policy-contingent state abstraction for hierarchical MDPs (2002) (0)
Embedding parameters in ab initio theory to develop approximations based on molecular similarity (2015) (0)
An Empirical Investigation of Beam-Aware Training in Supertagging (2020) (0)
Fast Algorithms for Proximity Search on Large Graphs Purnamrita (2008) (0)
De-Aliasing States In Dialogue Modelling With Inverse Reinforcement Learning (2020) (0)
Generalization Bound for Multiple Source Domain Adaptation (2018) (0)
On Automatic Database Management System Tuning Using Machine Learning (2021) (0)
Approximate Empirical Bayes for Deep Neural Networks (2018) (0)

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What Schools Are Affiliated With Geoffrey J. Gordon?

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