Dale Schuurmans

发表

Dual Representations for Dynamic Programming

Michael Bowling, Tao Wang, D. Lizotte, 2008 .

Learning Exercise Policies for American Options

Dale Schuurmans, Csaba Szepesvári, Yuxi Li, 2009, AISTATS.

Multi-view Matrix Factorization for Linear Dynamical System Estimation

Martha White, Dale Schuurmans, Csaba Szepesvári, 2017, NIPS.

A General Projection Property for Distribution Families

Yaoliang Yu, Dale Schuurmans, Csaba Szepesvári, 2009, NIPS.

Efficient Stopping Rules

Csaba Szepesvari, Dale Schuurmans, Volodymyr Mnih, 2008 .

A New Metric-Based Approach to Model Selection

Dale Schuurmans, 1997, AAAI/IAAI.

General Convergence Results for Linear Discriminant Updates

Dale Schuurmans, Adam J. Grove, Nick Littlestone, 1997, COLT '97.

Probabilities for SV Machines

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

Regularization Networks and Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Reward Augmented Maximum Likelihood for Neural Structured Prediction

Dale Schuurmans, Samy Bengio, Navdeep Jaitly, 2016, NIPS.

An experimental methodology for response surface optimization methods

D. Lizotte, Dale Schuurmans, R. Greiner, 2012, J. Glob. Optim..

Bounds on Error Expectation for SVM

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Natural Regularization from Generative Models

Smola, P. Bartlett, Dale Schuurmans, 2000 .

Dynamic Alignment Kernels

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Boosting in the Limit: Maximizing the Margin of Learned Ensembles

Dale Schuurmans, Adam J. Grove, Dale Schuurmans, 1998, AAAI/IAAI.

Entropy Numbers for Convex Combinations and MLPs

Smola, R. C. Williamson, P. Bartlett, 2000 .

MapReduce for Parallel Reinforcement Learning

Dale Schuurmans, Yuxi Li, Dale Schuurmans, 2011, EWRL.

Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination

Dale Schuurmans, Yoshua Bengio, Yoshua Bengio, 2002, Machine Learning.

Linear Discriminant and Support Vector Classifiers

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

Bayesian sparse sampling for on-line reward optimization

Tao Wang, Dale Schuurmans, Michael H. Bowling, 2005, ICML.

Bridging the Gap Between Value and Policy Based Reinforcement Learning

Dale Schuurmans, Mohammad Norouzi, Kelvin Xu, 2017, NIPS.

Direct value-approximation for factored MDPs

Dale Schuurmans, Relu Patrascu, Dale Schuurmans, 2001, NIPS.

Automatic Gait Optimization with Gaussian Process Regression

Tao Wang, Dale Schuurmans, Michael H. Bowling, 2007, IJCAI.

Discriminative Batch Mode Active Learning

Dale Schuurmans, Yuhong Guo, Dale Schuurmans, 2007, NIPS.

Gaussian Processes and SVM: Mean Field and Leave-One-Out

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

Generalized Conditional Gradient for Sparse Estimation pdf

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2014, J. Mach. Learn. Res..

Learning Default Concepts

Dale Schuurmans, R. Greiner, 1994 .

Scalable and Sound Low-Rank Tensor Learning

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2016, AISTATS.

Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs

Dale Schuurmans, Carlos Guestrin, Relu Patrascu, 2002, ICML.

Maximum Margin Clustering

Dale Schuurmans, Linli Xu, James Neufeld, 2004, NIPS.

Relaxed Clipping: A Global Training Method for Robust Regression and Classification

Martha White, Yaoliang Yu, Dale Schuurmans, 2010, NIPS.

Learning with a Strong Adversary pdf

Dale Schuurmans, Csaba Szepesvári, Bing Xu, 2015, ArXiv.

Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering

Yaoliang Yu, Dale Schuurmans, Dale Schuurmans, 2011, UAI.

A Polynomial-time Form of Robust Regression

Yaoliang Yu, Dale Schuurmans, Özlem Aslan, 2012, NIPS.

Characterizing the Representer Theorem

Yaoliang Yu, Dale Schuurmans, Csaba Szepesvári, 2013, ICML.

Polar Operators for Structured Sparse Estimation

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2013, NIPS.

Robust Support Vector Machine Training via Convex Outlier Ablation

Koby Crammer, Dale Schuurmans, Linli Xu, 2006, AAAI.

Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions

Martha White, Yaoliang Yu, Xinhua Zhang, 2011, AAAI.

Accelerated Training for Matrix-norm Regularization: A Boosting Approach

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2012, NIPS.

Metric-Based Methods for Adaptive Model Selection and Regularization

Dale Schuurmans, Finnegan Southey, Dale Schuurmans, 2002, Machine Learning.

GACV for Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Monte Carlo inference via greedy importance sampling pdf

Dale Schuurmans, Finnegan Southey, Dale Schuurmans, 2000, UAI.

Beyond the Margin

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Fast normalized cut with linear constraints

Dale Schuurmans, Linli Xu, Wenye Li, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Automatic basis selection techniques for RBF networks

Dale Schuurmans, Ali Ghodsi, Dale Schuurmans, 2003, Neural Networks.

The latent maximum entropy principle

Dale Schuurmans, R. Rosenfeld, Shaojun Wang, 2002, Proceedings IEEE International Symposium on Information Theory,.

Adaptive Margin Support Vector Machines

Alexander J. Smola, Dale Schuurmans, Bernhard Schölkopf, 2000 .

Face alignment using statistical models and wavelet features

Harry Shum, Dale Schuurmans, Stan Z. Li, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

Characterizing the benefits of model-based vs . direct-control learning in exploration

Dale Schuurmans, 2007 .

Direct value-approxiIllation for factored MDPs

Dale Schuurmans, ReIn Patrascll, 2001 .

Advances in Large Margin Classiiers Library of Congress Cataloging-in-publication Data Advances in Large Margin Classiiers / Edited Includes Bibliographical References and Index. Isbn 0-xxx-xxxxx-x (alk. Paper) 1. Machine Learning. 2. Algorithms. 3. Kernel Functions

P. Bartlett, Dale Schuurmans, Alex Smola, 1999 .

Greedy linear value-approximation for factored Markov decision processes

Craig Boutilier, Dale Schuurmans, Pascal Poupart, 2002, AAAI/IAAI.

Exponential Family Estimation via Dynamics Embedding

Dale Schuurmans, Le Song, A. Gretton, 2018 .

Efficient global optimization for exponential family PCA and low-rank matrix factorization

Dale Schuurmans, Yuhong Guo, 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

Learning to Classify Incomplete ExamplesDale SchuurmansDepartment of Computer ScienceUniversity of TorontoToronto , ON M 5 S 1 A 4

Dale Schuurmans, R. Greiner, 1993 .

A Disease Classifier for Metabolic Profiles Based on Metabolic Pathway Knowledge Examining Committee

Dale Schuurmans, V. Baracos, 2010 .

Fil tering Junk E-Mail : A Performance Comparison b etween Genetic Programming

Dale Schuurmans, Hooman Katirai, 1999 .

Shape Time Discriminative Classification of Video Objects

Dale Schuurmans, Shaojun Wang, C. Lei, 2022 .

Exploration in Policy Mirror Descent

Dale Schuurmans, Martin Müller, Ruitong Huang, 2018 .

Formalizing Anthropomorphism Through Games: A Study in Deep Neural Networks

Dale Schuurmans, Martin A. Zinkevich, Dale Schuurmans, 2017, AAAI Workshops.

Fast (distribution Speciic) Learning

Dale Schuurmans, R. Greiner, 1997 .

Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007

Dale Schuurmans, Yoshua Bengio, Léon Bottou, 2008, NIPS.

The Unique Heart Sound Signature of Children with Pulmonary Artery Hypertension

Dale Schuurmans, Roger Zemp, Mohamed Elgendi, 2015, Pulmonary circulation.

The Voice of the Heart: Vowel-Like Sound in Pulmonary Artery Hypertension

Dale Schuurmans, M. Elgendi, R. Zemp, 2018, Diseases.

Time-Domain Analysis of Heart Sound Intensity in Children with and without Pulmonary Artery Hypertension: A Pilot Study using a Digital Stethoscope

Dale Schuurmans, M. Elgendi, R. Zemp, 2014, Pulmonary circulation.

Large Margin Bank Boundaries for Ordinal Regression

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

L EARNING WITH A S TRONG A DVERSARY

Csaba Szepesvari, Dale Schuurmans, Ruitong Huang, 2015 .

An Adaptive Regularization Criterion for Supervised Learning

Dale Schuurmans, Finnegan Southey, Dale Schuurmans, 2000, ICML.

Learning to classify incomplete examples

Dale Schuurmans, Russell Greiner, Dale Schuurmans, 1997, COLT 1997.

EARNING WITH A S TRONG A DVERSARY

Csaba Szepesvari, Dale Schuurmans, Ruitong Huang, 2016 .

Latent Maximum Entropy Approach for Semantic N-gram Language Modeling

Dale Schuurmans, Fuchun Peng, Shaojun Wang, 2003, AISTATS.

Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields

Dale Schuurmans, Russell Greiner, Li Cheng, 2004, 2004 International Symposium on Chinese Spoken Language Processing.

Non-parametric Regression between Riemannian Manifolds

Dale Schuurmans, Yoshua Bengio, Léon Bottou, 2009, NIPS 2009.

Chain of Thought Prompting Elicits Reasoning in Large Language Models

Dale Schuurmans, Xuezhi Wang, Ed Chi, 2022, ArXiv.

Chain of Thought Prompting Elicits Reasoning in Large Language Models

Dale Schuurmans, Xuezhi Wang, Ed Chi, 2022, ArXiv.

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces

Dale Schuurmans, H. Dai, Haoran Sun, 2022, NeurIPS.

Memory Augmented Large Language Models are Computationally Universal

Dale Schuurmans, 2023, ArXiv.

Stochastic Neural Networks with Monotonic Activation Functions

Dale Schuurmans, Barnabás Póczos, Jeff G. Schneider, 2016, AISTATS.

Advantage Amplification in Slowly Evolving Latent-State Environments

Craig Boutilier, Dale Schuurmans, Ofer Meshi, 2019, IJCAI.

Robust Ensemble Learning

Gunnar Rätsch, Bernhard Schölkopf, Dale Schuurmans, 2000 .

Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting

Dale Schuurmans, Yi Shi, Guohui Lin, 2009, COCOA.

Convex Deep Learning via Normalized Kernels

Xinhua Zhang, Dale Schuurmans, Özlem Aslan, 2014, NIPS.

Improved Natural Language Learning via Variance-Regularization Support Vector Machines

Dale Schuurmans, Dekang Lin, Shane Bergsma, 2010, CoNLL.

implicit Online Learning with Kernels

Dale Schuurmans, S. V. N. Vishwanathan, Terry Caelli, 2006, NIPS.

Convex Two-Layer Modeling

Xinhua Zhang, Dale Schuurmans, Hao Cheng, 2013, NIPS.

KERNEL-BASED GENE REGULATORY NETWORK INFERENCE

Dale Schuurmans, Yuhong Guo, Guohui Lin, 2010 .

Learning Gene Regulatory Networks via Globally Regularized Risk Minimization

Dale Schuurmans, Yuhong Guo, Dale Schuurmans, 2007, RECOMB-CG.

Data perturbation for escaping local maxima in learning

Dale Schuurmans, Nir Friedman, Gal Elidan, 2002, AAAI/IAAI.

Exponential Family Estimation via Adversarial Dynamics Embedding

Le Song, Zhen Liu, Dale Schuurmans, 2019, NeurIPS.

Generalized Optimal Reverse Prediction

Martha White, Dale Schuurmans, Dale Schuurmans, 2012, AISTATS.

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning

Martha White, Dale Schuurmans, Linli Xu, 2009, ICML '09.

Semi-Supervised Convex Training for Dependency Parsing

Dale Schuurmans, Qin Iris Wang, Dekang Lin, 2008, ACL.

Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields

Dale Schuurmans, Chi-Hoon Lee, Russell Greiner, 2006, NIPS.

Unsupervised and Semi-Supervised Multi-Class Support Vector Machines

Dale Schuurmans, Linli Xu, Dale Schuurmans, 2005, AAAI.

Protein phosphorylation site prediction via feature discovery support vector machine

Bo Yuan, Dale Schuurmans, Yi Shi, 2012 .

Optimal Estimation of Multivariate ARMA Models

Martha White, Dale Schuurmans, Michael H. Bowling, 2015, AAAI.

Spectral analysis of the heart sounds in children with and without pulmonary artery hypertension.

Dale Schuurmans, Roger Zemp, Mohamed Elgendi, 2014, International journal of cardiology.

An experimental methodology for response surface optimization methods

D. Lizotte, Dale Schuurmans, R. Greiner, 2011, Journal of Global Optimization.

Real-Time Discriminative Background Subtraction

Dale Schuurmans, Minglun Gong, Terry Caelli, 2011, IEEE Transactions on Image Processing.

Non-delusional Q-learning and value-iteration

Craig Boutilier, Dale Schuurmans, Tyler Lu, 2018, NeurIPS.

Chain of Thought Prompting Elicits Reasoning in Large Language Models

Dale Schuurmans, Xuezhi Wang, Denny Zhou, 2022, NeurIPS.

Dual Representations for Dynamic Programming and Reinforcement Learning

Tao Wang, D. Schuurmans, M. Bowling, 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

Local search characteristics of incomplete SAT procedures

Dale Schuurmans, Finnegan Southey, Dale Schuurmans, 2000, Artif. Intell..

Frequency analysis of photoplethysmogram and its derivatives

Nigel H. Lovell, Dale Schuurmans, Derek Abbott, 2015, Comput. Methods Programs Biomed..

Constraint-based optimization and utility elicitation using the minimax decision criterion

Craig Boutilier, Dale Schuurmans, Pascal Poupart, 2006, Artif. Intell..

Monte Carlo Matrix Inversion Policy Evaluation pdf

Dale Schuurmans, Fletcher Lu, Dale Schuurmans, 2002, UAI.

Characterizing Rational Versus Exponential learning Curves

Dale Schuurmans, Dale Schuurmans, 1995, J. Comput. Syst. Sci..

Learning to Generalize from Sparse and Underspecified Rewards pdf

Dale Schuurmans, Chen Liang, Mohammad Norouzi, 2019, ICML.

Learning With Adversary

Dale Schuurmans, Csaba Szepesvári, Bing Xu, 2015 .

Discriminative Maximum Margin Image Object Categorization with Exact Inference

Dale Schuurmans, Luping Zhou, Qinfeng Shi, 2009, 2009 Fifth International Conference on Image and Graphics.

Learning Continuous Latent Variable Models with Bregman Divergences

Dale Schuurmans, Shaojun Wang, Dale Schuurmans, 2003, ALT.

The latent maximum entropy principle

Dale Schuurmans, Yunxin Zhao, Shaojun Wang, 2002, Proceedings IEEE International Symposium on Information Theory,.

Variance Reduction via Antithetic Markov Chains

Dale Schuurmans, Michael H. Bowling, James Neufeld, 2015, AISTATS.

Representational Difficulties with Classifier Systems

Dale Schuurmans, Jonathan Schaeffer, Dale Schuurmans, 1989, ICGA.

Modular Community Detection in Networks

Dale Schuurmans, Wenye Li, Dale Schuurmans, 2011, IJCAI.

Understanding the impact of entropy on policy optimization

Nicolas Le Roux, Dale Schuurmans, Mohammad Norouzi, 2018, ICML.

Semi-supervised Multi-label Classification - A Simultaneous Large-Margin, Subspace Learning Approach

Dale Schuurmans, Yuhong Guo, Dale Schuurmans, 2012, ECML/PKDD.

Adaptive Large Margin Training for Multilabel Classification

Dale Schuurmans, Yuhong Guo, Dale Schuurmans, 2011, AAAI.

Linear Coherent Bi-Clustering via Beam Searching and Sample Set Clustering

Dale Schuurmans, Yi Shi, Guohui Lin, 2012, Discret. Math. Algorithms Appl..

Tangent-corrected embedding

Dale Schuurmans, Ali Ghodsi, Finnegan Southey, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Improving Policy Gradient by Exploring Under-appreciated Rewards pdf

Dale Schuurmans, Mohammad Norouzi, Ofir Nachum, 2016, ICLR.

Piecewise linear value function approximation for factored MDPs

Craig Boutilier, Dale Schuurmans, Pascal Poupart, 2002, AAAI/IAAI.

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control pdf

Dale Schuurmans, Mohammad Norouzi, Kelvin Xu, 2017, ICLR.

Learning Coordination Classifiers

Dale Schuurmans, Russell Greiner, Yuhong Guo, 2005, IJCAI.

Dale Schuurmans, Russell Greiner, William W. Cohen, 2019, 99 Variations on a Proof.

Greedy importance sampling: A new Monte Carlo inference method

Dale Schuurmans, Dale Schuurmans, 1999 .

Constraint-Based Optimization with the Minimax Decision Criterion

Craig Boutilier, Dale Schuurmans, Pascal Poupart, 2003, CP.

Distributed Flow Algorithms for Scalable Similarity Visualization

Alexander J. Smola, Dale Schuurmans, Novi Quadrianto, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Convex Multi-view Subspace Learning

Martha White, Yaoliang Yu, Xinhua Zhang, 2012, NIPS.

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals

Nigel H. Lovell, Derek Abbott, Dale Schuurmans, 2015, International journal of environmental research and public health.

Regret-based Utility Elicitation in Constraint-based Decision Problems

Craig Boutilier, Dale Schuurmans, Pascal Poupart, 2005, IJCAI.

Discriminative unsupervised learning of structured predictors

Dale Schuurmans, Linli Xu, Finnegan Southey, 2006, ICML.

Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields

Dale Schuurmans, Russell Greiner, Shaojun Wang, 2004, 2004 International Symposium on Chinese Spoken Language Processing.

Dynamic Web log session identification with statistical language models

Dale Schuurmans, Xiangji Huang, Fuchun Peng, 2004, J. Assoc. Inf. Sci. Technol..

Compact, Convex Upper Bound Iteration for Approximate POMDP Planning

Tao Wang, Dale Schuurmans, Pascal Poupart, 2006, AAAI.

Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling

Dale Schuurmans, Chi-Hoon Lee, Russell Greiner, 2006, ACL.

Text Classification in Asian Languages without Word Segmentation

Dale Schuurmans, Xiangji Huang, Fuchun Peng, 2003, IRAL.

Language and Task Independent Text Categorization with Simple Language Models

Dale Schuurmans, Fuchun Peng, Shaojun Wang, 2003, NAACL.

Combining Naive Bayes and n-Gram Language Models for Text Classification

Dale Schuurmans, Fuchun Peng, Dale Schuurmans, 2003, ECIR.

On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress

Nigel H. Lovell, Dale Schuurmans, Derek Abbott, 2015, Sensors.

Deep Learning Games

Dale Schuurmans, Martin Zinkevich, Dale Schuurmans, 2016, NIPS.

Learning with a Strong Adversary

Csaba Szepesvari, Dale Schuurmans, Ruitong Huang, 2015, ArXiv.

Holographic Feature Representations of Deep Networks

Dale Schuurmans, Alex Davies, Martin A. Zinkevich, 2017, UAI.

Kernel Exponential Family Estimation via Doubly Dual Embedding pdf

Le Song, Dale Schuurmans, Bo Dai, 2018, AISTATS.

Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM

Xinhua Zhang, Dale Schuurmans, Yi Shi, 2012, Pacific Symposium on Biocomputing.

A Geometric Perspective on Optimal Representations for Reinforcement Learning

Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, 2019, NeurIPS.

Session Boundary Detection for Association Rule Learning Using n-Gram Language Models

Dale Schuurmans, Xiangji Huang, Nick Cercone, 2003, Canadian Conference on AI.

Strictly Lexicalised Dependency Parsing

Dale Schuurmans, Qin Iris Wang, Dekang Lin, 2010, Trends in Parsing Technology.

Detection of a and b waves in the acceleration photoplethysmogram

Dale Schuurmans, Derek Abbott, Mohamed Elgendi, 2014, Biomedical engineering online.

Semi-Supervised Zero-Shot Classification with Label Representation Learning

Dale Schuurmans, Xin Li, Yuhong Guo, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Learning mixture models with the regularized latent maximum entropy principle

Dale Schuurmans, Fuchun Peng, Yunxin Zhao, 2004, IEEE Transactions on Neural Networks.

Stable Dual Dynamic Programming

Tao Wang, Dale Schuurmans, Michael H. Bowling, 2007, NIPS.

Smoothed Action Value Functions for Learning Gaussian Policies

Dale Schuurmans, Mohammad Norouzi, George Tucker, 2018, ICML.

Simple Training of Dependency Parsers via Structured Boosting

Dale Schuurmans, Qin Iris Wang, Dekang Lin, 2007, IJCAI.

Improved estimation for unsupervised part-of-speech tagging

Q.I. Wang, D. Schuurmans, Dale Schuurmans, 2005, 2005 International Conference on Natural Language Processing and Knowledge Engineering.

Protein fold recognition using the gradient boost algorithm.

Dale Schuurmans, Jinbo Xu, Feng Jiao, 2006, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set Clustering

Dale Schuurmans, Yi Shi, Guohui Lin, 2010, COCOA.

Characterizing the generalization performance of model selection strategies

Dale Schuurmans, Dean P. Foster, Lyle H. Ungar, 1997, ICML.

Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model

Dale Schuurmans, Russell Greiner, Shaojun Wang, 2006, ICGI.

Dual Temporal Difference Learning

Dale Schuurmans, Min Yang, Yuxi Li, 2009, AISTATS.

Correcting Covariate Shift with the Frank-Wolfe Algorithm

Dale Schuurmans, Russell Greiner, Junfeng Wen, 2015, IJCAI.

Learning Bayesian Nets that Perform Well

Dale Schuurmans, Russell Greiner, Adam J. Grove, 1997, UAI.

Learning Useful Horn Approximations

Dale Schuurmans, Russell Greiner, Dale Schuurmans, 1992, KR.

Policy Iteration for Learning an Exercise Policy for American Options

Dale Schuurmans, Yuxi Li, Dale Schuurmans, 2008, EWRL.

Increase in body temperature during carrageenin-induced paw oedema in the rat. Effect of non-steroid anti-inflammatory substances and essential fatty acid deficiency.

Dale Schuurmans, I. Bonta, J. Bruinvels, 1979, Prostaglandins and medicine.

Multi-label Classification with Output Kernels

Dale Schuurmans, Yuhong Guo, Dale Schuurmans, 2013, ECML/PKDD.

Maximum Margin Bayesian Networks

Dale Schuurmans, Dana F. Wilkinson, Yuhong Guo, 2005, UAI.

An efficient algorithm for maximal margin clustering

Dale Schuurmans, Vikas Singh, Jiming Peng, 2012, J. Glob. Optim..

Striving for Simplicity in Off-policy Deep Reinforcement Learning pdf

Dale Schuurmans, Mohammad Norouzi, Rishabh Agarwal, 2019, ArXiv.

Safe Exploration for Identifying Linear Systems via Robust Optimization pdf

Craig Boutilier, Dale Schuurmans, Martin Zinkevich, 2017, ArXiv.

Metric-Based Methods for Adaptive Model Selection and Regularization

Dale Schuurmans, F. Southey, 2002, Machine Learning.

Metric-Based Methods for Adaptive Model Selection and Regularization

Dale Schuurmans, F. Southey, 2002 .

Embedding Inference for Structured Multilabel Prediction

Dale Schuurmans, Siamak Ravanbakhsh, Nan Ding, 2015, NIPS.

Convex Co-embedding

Dale Schuurmans, Yuhong Guo, Farzaneh Mirzazadeh, 2014, AAAI.

Generalized Support Vector Machines

Dale Schuurmans, Bernhard Schölkopf, A. J. Smola, 2000 .

Divergence based graph estimation for manifold learning

Dale Schuurmans, Frank P. Ferrie, Karim T. Abou-Moustafa, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

Pacific symposium on biocomputing 2006

Lawrence Hunter, Russ B. Altman, Teri E. Klein, 2005, PSB 2016.

The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming

Dale Schuurmans, Finnegan Southey, Robert C. Holte, 2001, IJCAI.

Learning an Optimally Accurate Representation System

Dale Schuurmans, Russell Greiner, Dale Schuurmans, 1992, ECAI Workshop on Knowledge Representation and Reasoning.

Language independent authorship attribution using character level language models

Dale Schuurmans, Vlado Keselj, Fuchun Peng, 2003, Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - EACL '03.

Language Independent Authorship Attribution with Character Level N-Grams

Dale Schuurmans, Vlado Keselj, Fuchun Peng, 2003, EACL.

Greedy Importance Sampling

Dale Schuurmans, Dale Schuurmans, 1999, NIPS.

Scalable Metric Learning for Co-Embedding

Martha White, Dale Schuurmans, András György, 2015, ECML/PKDD.

Facility locations revisited: An efficient belief propagation approach

Dale Schuurmans, Linli Xu, Wenye Li, 2010, 2010 IEEE International Conference on Automation and Logistics.

Loca lSearc hCharacteristic so fIncomplet eSA TProcedures

Dale Schuurmans, F. Southey, 2000 .

Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning

Dale Schuurmans, Frank P. Ferrie, Karim T. Abou-Moustafa, 2013, ACML.

Transformation-Invariant Embedding for Image Analysis

Dale Schuurmans, Ali Ghodsi, Jiayuan Huang, 2004, ECCV.

Frequency analysis of photoplethysmogram and its derivatives

Dale Schuurmans, M. Elgendi, N. Lovell, 2015, Comput. Methods Programs Biomed..

Augmenting Naive Bayes Classifiers with Statistical Language Models

Dale Schuurmans, Fuchun Peng, Shaojun Wang, 2004, Information Retrieval.

Real-Time Discriminative Background Subtraction

Dale Schuurmans, T. Caelli, Minglun Gong, 2011, IEEE Transactions on Image Processing.