Representational Difficulties with Classifier Systems. - Dale Schuurmans; Jonathan Schaeffer 001 (ICGA, 1989)

Learning an Optimally Accurate Representation System. - Russell Greiner; Dale Schuurmans (ECAI Workshop on Knowledge Representation and Reasoning, 1992)

Learning Useful Horn Approximations. - Russell Greiner; Dale Schuurmans (KR, 1992)

Sequential PAC Learning. - Dale Schuurmans; Russell Greiner (COLT, 1995)

Practical PAC Learning. - Dale Schuurmans; Russell Greiner (IJCAI, 1995)

Effective classification learning. - Dale Schuurmans (1996)

A New Metric-Based Approach to Model Selection. - Dale Schuurmans (AAAI/IAAI, 1997)

Characterizing the generalization performance of model selection strategies. - Dale Schuurmans; Lyle H. Ungar; Dean P. Foster (ICML, 1997)

Learning Bayesian Nets that Perform Well. - Russell Greiner; Adam J. Grove; Dale Schuurmans (UAI, 1997)

Characterizing Rational Versus Exponential learning Curves. - Dale Schuurmans (J. Comput. Syst. Sci., 1997)

Boosting in the Limit: Maximizing the Margin of Learned Ensembles. - Adam J. Grove; Dale Schuurmans (AAAI/IAAI, 1998)

Efficient exploration for optimizing immediate reward. - Dale Schuurmans; Lloyd G. Greenwald (AAAI/IAAI, 1999)

Greedy Importance Sampling. - Dale Schuurmans (NIPS, 1999)

An Adaptive Regularization Criterion for Supervised Learning. - Dale Schuurmans; Finnegan Southey (ICML, 2000)

Monte Carlo inference via greedy importance sampling. - Dale Schuurmans; Finnegan Southey (UAI, 2000)

Self-Supervised Chinese Word Segmentation. - Fuchun Peng; Dale Schuurmans (IDA, 2001)

The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming. - Dale Schuurmans; Finnegan Southey; Robert C. Holte (IJCAI, 2001)

Direct value-approximation for factored MDPs. - Dale Schuurmans; Relu Patrascu (NIPS, 2001)

A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling. - Fuchun Peng; Dale Schuurmans (NLPRS, 2001)

A Hierarchical EM Approach to Word Segmentation. - Fuchun Peng; Dale Schuurmans (NLPRS, 2001)

Local search characteristics of incomplete SAT procedures. - Dale Schuurmans; Finnegan Southey (Artif. Intell., 2001)

General Convergence Results for Linear Discriminant Updates. - Adam J. Grove; Nick Littlestone; Dale Schuurmans (Mach. Learn., 2001)

Data Perturbation for Escaping Local Maxima in Learning. - Gal Elidan; Matan Ninio; Nir Friedman; Dale Schuurmans (AAAI/IAAI, 2002)

Greedy Linear Value-Approximation for Factored Markov Decision Processes. - Relu Patrascu; Pascal Poupart; Dale Schuurmans; Craig Boutilier; Carlos Guestrin (AAAI/IAAI, 2002)

Piecewise Linear Value Function Approximation for Factored MDPs. - Pascal Poupart; Craig Boutilier; Relu Patrascu; Dale Schuurmans (AAAI/IAAI, 2002)

Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR. - Fuchun Peng; Xiangji Huang 001; Dale Schuurmans; Nick Cercone (COLING, 2002)

Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs. - Carlos Guestrin; Relu Patrascu; Dale Schuurmans (ICML, 2002)

Investigating the Maximum Likelihood Alternative to TD(lambda). - Fletcher Lu; Relu Patrascu; Dale Schuurmans (ICML, 2002)

Regularized Greedy Importance Sampling. - Finnegan Southey; Dale Schuurmans; Ali Ghodsi 001 (NIPS, 2002)

Waterloo at NTCIR-3: Using Self-supervised Word Segmentation. - Xiangji Huang 001; Fuchun Peng; Dale Schuurmans; Nick Cercone (NTCIR, 2002)

Using self-supervised word segmentation in Chinese information retrieval. - Fuchun Peng; Xiangji Huang 001; Dale Schuurmans; Nick Cercone; Stephen E. Robertson (SIGIR, 2002)

Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination. - Yoshua Bengio; Dale Schuurmans (Mach. Learn., 2002)

Metric-Based Methods for Adaptive Model Selection and Regularization. - Dale Schuurmans; Finnegan Southey (Mach. Learn., 2002)

Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. - Xiangji Huang 001; Fuchun Peng; Aijun An; Dale Schuurmans; Nick Cercone (AI, 2003)

Model-Based Least-Squares Policy Evaluation. - Fletcher Lu; Dale Schuurmans (AI, 2003)

Latent Maximum Entropy Approach for Semantic N-gram Language Modeling. - Shaojun Wang; Dale Schuurmans; Fuchun Peng (AISTATS, 2003)

Learning Continuous Latent Variable Models with Bregman Divergences. - Shaojun Wang; Dale Schuurmans (ALT, 2003)

Constraint-Based Optimization with the Minimax Decision Criterion. - Craig Boutilier; Relu Patrascu; Pascal Poupart; Dale Schuurmans (CP, 2003)

Face Alignment Using Statistical Models and Wavelet Features. - Feng Jiao; Stan Z. Li; Heung-Yeung Shum; Dale Schuurmans (CVPR (1), 2003)

Language Independent Authorship Attribution with Character Level N-Grams. - Fuchun Peng; Dale Schuurmans; Vlado Keselj; Shaojun Wang (EACL, 2003)

Combining Naive Bayes and n-Gram Language Models for Text Classification. - Fuchun Peng; Dale Schuurmans (ECIR, 2003)

Semantic n-gram language modeling with the latent maximum entropy principle. - Shaojun Wang; Dale Schuurmans; Fuchun Peng; Yunxin Zhao (ICASSP (1), 2003)

Learning Mixture Models with the Latent Maximum Entropy Principle. - Shaojun Wang; Dale Schuurmans; Fuchun Peng; Yunxin Zhao (ICML, 2003)

Automatic basis selection for RBF networks using Stein's unbiased risk estimator. - Ali Ghodsi 001; Dale Schuurmans (IJCNN, 2003)

Text classification in Asian languages without word segmentation. - Fuchun Peng; Xiangji Huang 001; Dale Schuurmans; Shaojun Wang (IRAL, 2003)

Language and Task Independent Text Categorization with Simple Language Models. - Fuchun Peng; Dale Schuurmans; Shaojun Wang (HLT-NAACL, 2003)

Monte Carlo Matrix Inversion Policy Evaluation. - Fletcher Lu; Dale Schuurmans (UAI, 2003)

Boltzmann Machine Learning with the Latent Maximum Entropy Principle. - Shaojun Wang; Dale Schuurmans; Fuchun Peng; Yunxin Zhao (UAI, 2003)

Applying Machine Learning to Text Segmentation for Information Retrieval. - Xiangji Huang 001; Fuchun Peng; Dale Schuurmans; Nick Cercone; Stephen E. Robertson (Inf. Retr., 2003)

Automatic basis selection techniques for RBF networks. - Ali Ghodsi 001; Dale Schuurmans (Neural Networks, 2003)

Transformation-Invariant Embedding for Image Analysis. - Ali Ghodsi 001; Jiayuan Huang; Dale Schuurmans (ECCV (4), 2004)

Maximum Margin Clustering. - Linli Xu; James Neufeld; Bryce Larson; Dale Schuurmans (NIPS, 2004)

Augmenting Naive Bayes Classifiers with Statistical Language Models. - Fuchun Peng; Dale Schuurmans; Shaojun Wang (Inf. Retr., 2004)

Dynamic Web log session identification with statistical language models. - Xiangji Huang 001; Fuchun Peng; Aijun An; Dale Schuurmans (J. Assoc. Inf. Sci. Technol., 2004)

Learning mixture models with the regularized latent maximum entropy principle. - Shaojun Wang; Dale Schuurmans; Fuchun Peng; Yunxin Zhao (IEEE Trans. Neural Networks, 2004)

Unsupervised and Semi-Supervised Multi-Class Support Vector Machines. - Linli Xu; Dale Schuurmans (AAAI, 2005)

Tangent-Corrected Embedding. - Ali Ghodsi 001; Jiayuan Huang; Finnegan Southey; Dale Schuurmans (CVPR (1), 2005)

Variational Bayesian image modelling. - Li Cheng 001; Feng Jiao; Dale Schuurmans; Shaojun Wang (ICML, 2005)

Bayesian sparse sampling for on-line reward optimization. - Tao Wang; Daniel J. Lizotte; Michael H. Bowling; Dale Schuurmans (ICML, 2005)

Regret-based Utility Elicitation in Constraint-based Decision Problems. - Craig Boutilier; Relu Patrascu; Pascal Poupart; Dale Schuurmans (IJCAI, 2005)

Learning Coordination Classifiers. - Yuhong Guo; Russell Greiner; Dale Schuurmans (IJCAI, 2005)

Strictly Lexical Dependency Parsing. - Qin Iris Wang; Dale Schuurmans; Dekang Lin (IWPT, 2005)

Maximum Margin Bayesian Networks. - Yuhong Guo; Dana F. Wilkinson; Dale Schuurmans (UAI, 2005)

Combining Statistical Language Models via the Latent Maximum Entropy Principle. - Shaojun Wang; Dale Schuurmans; Fuchun Peng; Yunxin Zhao (Mach. Learn., 2005)

Metric-Based Approaches for Semi-Supervised Regression and Classification. - Dale Schuurmans; Finnegan Southey; Dana F. Wilkinson; Yuhong Guo (Semi-Supervised Learning, 2006)

Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. - Tao Wang; Pascal Poupart; Michael H. Bowling; Dale Schuurmans (AAAI, 2006)

Robust Support Vector Machine Training via Convex Outlier Ablation. - Linli Xu; Koby Crammer; Dale Schuurmans (AAAI, 2006)

Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. - Feng Jiao; Shaojun Wang; Chi-Hoon Lee; Russell Greiner; Dale Schuurmans (ACL, 2006)

An Online Discriminative Approach to Background Subtraction. - Li Cheng 001; Shaojun Wang; Dale Schuurmans; Terry Caelli; S. V. N. Vishwanathan (AVSS, 2006)

Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization. - Qin Iris Wang; Colin Cherry; Daniel J. Lizotte; Dale Schuurmans (CoNLL, 2006)

Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. - Shaojun Wang; Shaomin Wang; Li Cheng 001; Russell Greiner; Dale Schuurmans (ICGI, 2006)

Discriminative unsupervised learning of structured predictors. - Linli Xu; Dana F. Wilkinson; Finnegan Southey; Dale Schuurmans (ICML, 2006)

implicit Online Learning with Kernels. - Li Cheng 001; S. V. N. Vishwanathan; Dale Schuurmans; Shaojun Wang; Terry Caelli (NIPS, 2006)

Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. - Chi-Hoon Lee; Shaojun Wang; Feng Jiao; Dale Schuurmans; Russell Greiner (NIPS, 2006)

Information Marginalization on Subgraphs. - Jiayuan Huang; Tingshao Zhu; Russell Greiner; Dengyong Zhou; Dale Schuurmans (PKDD, 2006)

Web Communities Identification from Random Walks. - Jiayuan Huang; Tingshao Zhu; Dale Schuurmans (PKDD, 2006)

Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. - Yuhong Guo; Dale Schuurmans (UAI, 2006)

Constraint-based optimization and utility elicitation using the minimax decision criterion. - Craig Boutilier; Relu Patrascu; Pascal Poupart; Dale Schuurmans (Artif. Intell., 2006)

Graphical Models and Point Pattern Matching. - Tibério S. Caetano; Terry Caelli; Dale Schuurmans; Dante Augusto Couto Barone (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

Automatic Gait Optimization with Gaussian Process Regression. - Daniel J. Lizotte; Tao Wang; Michael H. Bowling; Dale Schuurmans (IJCAI, 2007)

Simple Training of Dependency Parsers via Structured Boosting. - Qin Iris Wang; Dekang Lin; Dale Schuurmans (IJCAI, 2007)

Discriminative Batch Mode Active Learning. - Yuhong Guo; Dale Schuurmans (NIPS, 2007)

Convex Relaxations of Latent Variable Training. - Yuhong Guo; Dale Schuurmans (NIPS, 2007)

Stable Dual Dynamic Programming. - Tao Wang; Daniel J. Lizotte; Michael H. Bowling; Dale Schuurmans (NIPS, 2007)

Learning Gene Regulatory Networks via Globally Regularized Risk Minimization. - Yuhong Guo; Dale Schuurmans (RECOMB-CG, 2007)

Semi-Supervised Convex Training for Dependency Parsing. - Qin Iris Wang; Dale Schuurmans; Dekang Lin (ACL, 2008)

Efficient global optimization for exponential family PCA and low-rank matrix factorization. - Yuhong Guo; Dale Schuurmans (Allerton, 2008)

Policy Iteration for Learning an Exercise Policy for American Options. - Yuxi Li; Dale Schuurmans (EWRL, 2008)

A Reformulation of Support Vector Machines for General Confidence Functions. - Yuhong Guo; Dale Schuurmans (ACML, 2009)

Inference of the structural credit risk model using MLE. - Yuxi Li; Li Cheng 001; Dale Schuurmans (CIFEr, 2009)

Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting. - Yi Shi 005; Zhipeng Cai 001; Guohui Lin; Dale Schuurmans (COCOA, 2009)

Fast normalized cut with linear constraints. - Linli Xu; Wenye Li 001; Dale Schuurmans (CVPR, 2009)

Discriminative Maximum Margin Image Object Categorization with Exact Inference. - Qinfeng Shi; Luping Zhou; Li Cheng 001; Dale Schuurmans (ICIG, 2009)

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. - Linli Xu; Martha White; Dale Schuurmans (ICML, 2009)

Convex Relaxation of Mixture Regression with Efficient Algorithms. - Novi Quadrianto; Tibério S. Caetano; John Lim; Dale Schuurmans (NIPS, 2009)

A General Projection Property for Distribution Families. - Yaoliang Yu; Yuxi Li; Dale Schuurmans; Csaba Szepesvári (NIPS, 2009)

Learning Exercise Policies for American Options. - Yuxi Li; Csaba Szepesvári; Dale Schuurmans (AISTATS, 2009)

Dual Temporal Difference Learning. - Min Yang; Yuxi Li; Dale Schuurmans (AISTATS, 2009)

Strictly Lexicalised Dependency Parsing. - Qin Iris Wang; Dale Schuurmans; Dekang Lin (Trends in Parsing Technology, 2010)

Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set Clustering. - Yi Shi 005; Maryam Hasan; Zhipeng Cai 001; Guohui Lin; Dale Schuurmans (COCOA (1), 2010)

Improved Natural Language Learning via Variance-Regularization Support Vector Machines. - Shane Bergsma; Dekang Lin; Dale Schuurmans (CoNLL, 2010)

Distributed Flow Algorithms for Scalable Similarity Visualization. - Novi Quadrianto; Dale Schuurmans; Alexander J. Smola (ICDM Workshops, 2010)

Relaxed Clipping: A Global Training Method for Robust Regression and Classification. - Yaoliang Yu; Min Yang; Linli Xu; Martha White; Dale Schuurmans (NIPS, 2010)

Adaptive Large Margin Training for Multilabel Classification. - Yuhong Guo; Dale Schuurmans (AAAI, 2011)

Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. - Xinhua Zhang; Yaoliang Yu; Martha White; Ruitong Huang; Dale Schuurmans (AAAI, 2011)

MapReduce for Parallel Reinforcement Learning. - Yuxi Li; Dale Schuurmans (EWRL, 2011)

Modular Community Detection in Networks. - Wenye Li 001; Dale Schuurmans (IJCAI, 2011)

Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. - Yaoliang Yu; Dale Schuurmans (UAI, 2011)

Real-Time Discriminative Background Subtraction. - Li Cheng 001; Minglun Gong; Dale Schuurmans; Terry Caelli (IEEE Trans. Image Process., 2011)

Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. - James Neufeld; Yaoliang Yu; Xinhua Zhang; Ryan Kiros; Dale Schuurmans (ICML, 2012)

Convex Multi-view Subspace Learning. - Martha White; Yaoliang Yu; Xinhua Zhang; Dale Schuurmans (NIPS, 2012)

A Polynomial-time Form of Robust Regression. - Yaoliang Yu; Özlem Aslan; Dale Schuurmans (NIPS, 2012)

Accelerated Training for Matrix-norm Regularization: A Boosting Approach. - Xinhua Zhang; Yaoliang Yu; Dale Schuurmans (NIPS, 2012)

Semi-supervised Multi-label Classification - A Simultaneous Large-Margin, Subspace Learning Approach. - Yuhong Guo; Dale Schuurmans (ECML/PKDD (2), 2012)

Sparse Learning Based Linear Coherent Bi-clustering. - Yi Shi 005; Xiaoping Liao; Xinhua Zhang; Guohui Lin; Dale Schuurmans (WABI, 2012)

Linear Coherent Bi-Clustering via Beam Searching and Sample Set Clustering. - Yi Shi 005; Maryam Hasan; Zhipeng Cai 001; Guohui Lin; Dale Schuurmans (Discret. Math. Algorithms Appl., 2012)

An experimental methodology for response surface optimization methods. - Daniel J. Lizotte; Russell Greiner; Dale Schuurmans (J. Glob. Optim., 2012)

An efficient algorithm for maximal margin clustering. - Jiming Peng; Lopamudra Mukherjee; Vikas Singh; Dale Schuurmans; Linli Xu (J. Glob. Optim., 2012)

Generalized Optimal Reverse Prediction. - Martha White; Dale Schuurmans (AISTATS, 2012)

The Latent Maximum Entropy Principle. - Shaojun Wang; Dale Schuurmans; Yunxin Zhao (ACM Trans. Knowl. Discov. Data, 2012)

Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning. - Karim T. Abou-Moustafa; Dale Schuurmans; Frank P. Ferrie (ACML, 2013)

Divergence based graph estimation for manifold learning. - Karim T. Abou-Moustafa; Frank P. Ferrie; Dale Schuurmans (GlobalSIP, 2013)

Characterizing the Representer Theorem. - Yaoliang Yu; Hao Cheng 002; Dale Schuurmans; Csaba Szepesvári (ICML (1), 2013)

Convex Two-Layer Modeling. - Özlem Aslan; Hao Cheng 002; Xinhua Zhang; Dale Schuurmans (NIPS, 2013)

Polar Operators for Structured Sparse Estimation. - Xinhua Zhang; Yaoliang Yu; Dale Schuurmans (NIPS, 2013)

Multi-label Classification with Output Kernels. - Yuhong Guo; Dale Schuurmans (ECML/PKDD (2), 2013)

Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM. - Yi Shi 005; Xinhua Zhang; Xiaoping Liao; Guohui Lin; Dale Schuurmans (Pacific Symposium on Biocomputing, 2013)

Convex Relaxations of Bregman Divergence Clustering. - Hao Cheng 002; Xinhua Zhang; Dale Schuurmans (UAI, 2013)

Exploiting Syntactic, Semantic, and Lexical Regularities in Language Modeling via Directed Markov Random Fields. - Shaojun Wang; Shaomin Wang; Li Cheng 001; Russell Greiner; Dale Schuurmans (Comput. Intell., 2013)

Reinforcement Ranking - Hengshuai Yao; Dale Schuurmans (CoRR, 2013)

Convex Co-embedding. - Farzaneh Mirzazadeh; Yuhong Guo; Dale Schuurmans (AAAI, 2014)

Adaptive Monte Carlo via Bandit Allocation. - James Neufeld; András György 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2014)

Convex Deep Learning via Normalized Kernels. - Özlem Aslan; Xinhua Zhang; Dale Schuurmans (NIPS, 2014)

Optimal Estimation of Multivariate ARMA Models. - Martha White; Junfeng Wen; Michael Bowling; Dale Schuurmans (AAAI, 2015)

Variance Reduction via Antithetic Markov Chains. - James Neufeld; Dale Schuurmans; Michael H. Bowling (AISTATS, 2015)

Semi-Supervised Zero-Shot Classification with Label Representation Learning. - Xin Li 013; Yuhong Guo; Dale Schuurmans (ICCV, 2015)

Correcting Covariate Shift with the Frank-Wolfe Algorithm. - Junfeng Wen; Russell Greiner; Dale Schuurmans (IJCAI, 2015)

Embedding Inference for Structured Multilabel Prediction. - Farzaneh Mirzazadeh; Siamak Ravanbakhsh; Nan Ding 002; Dale Schuurmans (NIPS, 2015)

Generalization in Unsupervised Learning. - Karim T. Abou-Moustafa; Dale Schuurmans (ECML/PKDD (1), 2015)

Scalable Metric Learning for Co-Embedding. - Farzaneh Mirzazadeh; Martha White; András György 001; Dale Schuurmans (ECML/PKDD (1), 2015)

Frequency analysis of photoplethysmogram and its derivatives. - Mohamed Elgendi; Richard Ribón Fletcher; Ian Norton; Matt Brearley; Derek Abbott; Nigel H. Lovell; Dale Schuurmans (Comput. Methods Programs Biomed., 2015)

Learning with a Strong Adversary. - Ruitong Huang; Bing Xu; Dale Schuurmans; Csaba Szepesvári (CoRR, 2015)

On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress. - Mohamed Elgendi; Rich Fletcher; Ian Norton; Matt Brearley; Derek Abbott; Nigel H. Lovell; Dale Schuurmans (Sensors, 2015)

Scalable and Sound Low-Rank Tensor Learning. - Hao Cheng 002; Yaoliang Yu; Xinhua Zhang; Eric P. Xing; Dale Schuurmans (AISTATS, 2016)

Stochastic Neural Networks with Monotonic Activation Functions. - Siamak Ravanbakhsh; Barnabás Póczos; Jeff G. Schneider; Dale Schuurmans; Russell Greiner (AISTATS, 2016)

Reward Augmented Maximum Likelihood for Neural Structured Prediction. - Mohammad Norouzi 002; Samy Bengio; Zhifeng Chen; Navdeep Jaitly; Mike Schuster; Yonghui Wu; Dale Schuurmans (NIPS, 2016)

Deep Learning Games. - Dale Schuurmans; Martin Zinkevich (NIPS, 2016)

Formalizing Anthropomorphism Through Games: A Study in Deep Neural Networks. - Martin A. Zinkevich; Dale Schuurmans (AAAI Workshops, 2017)

Improving Policy Gradient by Exploring Under-appreciated Rewards. - Ofir Nachum; Mohammad Norouzi 002; Dale Schuurmans (ICLR (Poster), 2017)

Logistic Markov Decision Processes. - Martin Mladenov; Craig Boutilier; Dale Schuurmans; Ofer Meshi; Gal Elidan; Tyler Lu (IJCAI, 2017)

Multi-view Matrix Factorization for Linear Dynamical System Estimation. - Mahdi Karami; Martha White; Dale Schuurmans; Csaba Szepesvári (NIPS, 2017)

Bridging the Gap Between Value and Policy Based Reinforcement Learning. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (NIPS, 2017)

Holographic Feature Representations of Deep Networks. - Martin A. Zinkevich; Alex Davies; Dale Schuurmans (UAI, 2017)

Safe Exploration for Identifying Linear Systems via Robust Optimization. - Tyler Lu; Martin Zinkevich; Craig Boutilier; Binz Roy; Dale Schuurmans (CoRR, 2017)

Generalized Conditional Gradient for Sparse Estimation. - Yaoliang Yu; Xinhua Zhang; Dale Schuurmans (J. Mach. Learn. Res., 2017)

Variational Rejection Sampling. - Aditya Grover; Ramki Gummadi; Miguel Lázaro-Gredilla; Dale Schuurmans; Stefano Ermon (AISTATS, 2018)

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (ICLR (Poster), 2018)

Smoothed Action Value Functions for Learning Gaussian Policies. - Ofir Nachum; Mohammad Norouzi 002; George Tucker; Dale Schuurmans (ICML, 2018)

Planning and Learning with Stochastic Action Sets. - Craig Boutilier; Alon Cohen; Avinatan Hassidim; Yishay Mansour; Ofer Meshi; Martin Mladenov; Dale Schuurmans (IJCAI, 2018)

Non-delusional Q-learning and value-iteration. - Tyler Lu; Dale Schuurmans; Craig Boutilier (NeurIPS, 2018)

Planning and Learning with Stochastic Action Sets. - Craig Boutilier; Alon Cohen; Amit Daniely; Avinatan Hassidim; Yishay Mansour; Ofer Meshi; Martin Mladenov; Dale Schuurmans (CoRR, 2018)

Kernel Exponential Family Estimation via Doubly Dual Embedding. - Bo Dai 001; Hanjun Dai; Arthur Gretton; Le Song; Dale Schuurmans; Niao He (AISTATS, 2019)

Learning to Generalize from Sparse and Underspecified Rewards. - Rishabh Agarwal; Chen Liang; Dale Schuurmans; Mohammad Norouzi 002 (ICML, 2019)

Understanding the Impact of Entropy on Policy Optimization. - Zafarali Ahmed; Nicolas Le Roux; Mohammad Norouzi 002; Dale Schuurmans (ICML, 2019)

The Value Function Polytope in Reinforcement Learning. - Robert Dadashi; Marc G. Bellemare; Adrien Ali Taïga; Nicolas Le Roux; Dale Schuurmans (ICML, 2019)

On Principled Entropy Exploration in Policy Optimization. - Jincheng Mei; Chenjun Xiao; Ruitong Huang; Dale Schuurmans; Martin Müller 003 (IJCAI, 2019)

Advantage Amplification in Slowly Evolving Latent-State Environments. - Martin Mladenov; Ofer Meshi; Jayden Ooi; Dale Schuurmans; Craig Boutilier (IJCAI, 2019)

A Geometric Perspective on Optimal Representations for Reinforcement Learning. - Marc G. Bellemare; Will Dabney; Robert Dadashi; Adrien Ali Taïga; Pablo Samuel Castro; Nicolas Le Roux; Dale Schuurmans; Tor Lattimore; Clare Lyle (NeurIPS, 2019)

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making. - Minmin Chen; Ramki Gummadi; Chris Harris 001; Dale Schuurmans (NeurIPS, 2019)

Exponential Family Estimation via Adversarial Dynamics Embedding. - Bo Dai 001; Zhen Liu 019; Hanjun Dai; Niao He; Arthur Gretton; Le Song; Dale Schuurmans (NeurIPS, 2019)

Invertible Convolutional Flow. - Mahdi Karami; Dale Schuurmans; Jascha Sohl-Dickstein; Laurent Dinh; Daniel Duckworth (NeurIPS, 2019)

Maximum Entropy Monte-Carlo Planning. - Chenjun Xiao; Ruitong Huang; Jincheng Mei; Dale Schuurmans; Martin Müller 003 (NeurIPS, 2019)

Striving for Simplicity in Off-policy Deep Reinforcement Learning. - Rishabh Agarwal; Dale Schuurmans; Mohammad Norouzi 002 (CoRR, 2019)

AlgaeDICE: Policy Gradient from Arbitrary Experience. - Ofir Nachum; Bo Dai 001; Ilya Kostrikov; Yinlam Chow; Lihong Li 001; Dale Schuurmans (CoRR, 2019)

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning. - Chenjun Xiao; Yifan Wu; Chen Ma; Dale Schuurmans; Martin Müller 003 (CoRR, 2019)

GenDICE: Generalized Offline Estimation of Stationary Values. - Ruiyi Zhang; Bo Dai 001; Lihong Li 001; Dale Schuurmans (ICLR, 2020)

An Optimistic Perspective on Offline Reinforcement Learning. - Rishabh Agarwal; Dale Schuurmans; Mohammad Norouzi 002 (ICML, 2020)

Scalable Deep Generative Modeling for Sparse Graphs. - Hanjun Dai; Azade Nazi; Yujia Li; Bo Dai 001; Dale Schuurmans (ICML, 2020)

On the Global Convergence Rates of Softmax Policy Gradient Methods. - Jincheng Mei; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (ICML, 2020)

ConQUR: Mitigating Delusional Bias in Deep Q-Learning. - Dijia Su; Jayden Ooi; Tyler Lu; Dale Schuurmans; Craig Boutilier (ICML, 2020)

Batch Stationary Distribution Estimation. - Junfeng Wen; Bo Dai 001; Lihong Li 001; Dale Schuurmans (ICML, 2020)

Domain Aggregation Networks for Multi-Source Domain Adaptation. - Junfeng Wen; Russell Greiner; Dale Schuurmans (ICML, 2020)

Energy-Based Processes for Exchangeable Data. - Mengjiao Yang 001; Bo Dai 001; Hanjun Dai; Dale Schuurmans (ICML, 2020)

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. - Denny Zhou; Mao Ye 006; Chen Chen; Tianjian Meng; Mingxing Tan; Xiaodan Song; Quoc V. Le; Qiang Liu 001; Dale Schuurmans (ICML, 2020)

CoinDICE: Off-Policy Confidence Interval Estimation. - Bo Dai 001; Ofir Nachum; Yinlam Chow; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. - Hanjun Dai; Rishabh Singh; Bo Dai 001; Charles Sutton; Dale Schuurmans (NeurIPS, 2020)

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs. - Nevena Lazic; Dong Yin; Mehrdad Farajtabar; Nir Levine; Dilan Görür; Chris Harris 001; Dale Schuurmans (NeurIPS, 2020)

Escaping the Gravitational Pull of Softmax. - Jincheng Mei; Chenjun Xiao; Bo Dai 001; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

Off-Policy Evaluation via the Regularized Lagrangian. - Mengjiao Yang 001; Ofir Nachum; Bo Dai 001; Lihong Li 001; Dale Schuurmans (NeurIPS, 2020)

ConQUR: Mitigating Delusional Bias in Deep Q-learning. - Andy Su; Jayden Ooi; Tyler Lu; Dale Schuurmans; Craig Boutilier (CoRR, 2020)

Variational Inference for Deep Probabilistic Canonical Correlation Analysis. - Mahdi Karami; Dale Schuurmans (CoRR, 2020)

Attention that does not Explain Away. - Nan Ding 002; Xinjie Fan; Zhenzhong Lan; Dale Schuurmans; Radu Soricut (CoRR, 2020)

Deep Probabilistic Canonical Correlation Analysis. - Mahdi Karami; Dale Schuurmans (AAAI, 2021)

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. - Seyed Kamyar Seyed Ghasemipour; Dale Schuurmans; Shixiang Shane Gu (ICML, 2021)

Leveraging Non-uniformity in First-order Non-convex Optimization. - Jincheng Mei; Yue Gao; Bo Dai 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. - Hongyu Ren; Hanjun Dai; Bo Dai 001; Xinyun Chen; Michihiro Yasunaga; Haitian Sun; Dale Schuurmans; Jure Leskovec; Denny Zhou (ICML, 2021)

Characterizing the Gap Between Actor-Critic and Policy Gradient. - Junfeng Wen; Saurabh Kumar 004; Ramki Gummadi; Dale Schuurmans (ICML, 2021)

On the Optimality of Batch Policy Optimization Algorithms. - Chenjun Xiao; Yifan Wu; Jincheng Mei; Bo Dai 001; Tor Lattimore; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

Understanding the Effect of Stochasticity in Policy Optimization. - Jincheng Mei; Bo Dai 001; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2021)

Combiner: Full Attention Transformer with Sparse Computation Cost. - Hongyu Ren; Hanjun Dai; Zihang Dai; Mengjiao Yang 001; Jure Leskovec; Dale Schuurmans; Bo Dai 001 (NeurIPS, 2021)

Optimization Issues in KL-Constrained Approximate Policy Iteration. - Nevena Lazic; Botao Hao; Yasin Abbasi-Yadkori; Dale Schuurmans; Csaba Szepesvári (CoRR, 2021)

Joint Attention for Multi-Agent Coordination and Social Learning. - Dennis Lee 003; Natasha Jaques; J. Chase Kew; Douglas Eck; Dale Schuurmans; Aleksandra Faust (CoRR, 2021)

On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. - Chenjun Xiao; Ilbin Lee; Bo Dai 001; Dale Schuurmans; Csaba Szepesvári (CoRR, 2021)

The Curse of Passive Data Collection in Batch Reinforcement Learning. - Chenjun Xiao; Ilbin Lee; Bo Dai 001; Dale Schuurmans; Csaba Szepesvári (AISTATS, 2022)

Offline Policy Selection under Uncertainty. - Mengjiao Yang 001; Bo Dai 001; Ofir Nachum; George Tucker; Dale Schuurmans (AISTATS, 2022)

Neural Stochastic Dual Dynamic Programming. - Hanjun Dai; Yuan Xue; Zia Syed; Dale Schuurmans; Bo Dai 001 (ICLR, 2022)

Understanding and Leveraging Overparameterization in Recursive Value Estimation. - Chenjun Xiao; Bo Dai 001; Jincheng Mei; Oscar A. Ramirez; Ramki Gummadi; Chris Harris 001; Dale Schuurmans (ICLR, 2022)

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. - Hanjun Dai; Mengjiao Yang 001; Yuan Xue; Dale Schuurmans; Bo Dai 001 (ICML, 2022)

A Parametric Class of Approximate Gradient Updates for Policy Optimization. - Ramki Gummadi; Saurabh Kumar 004; Junfeng Wen; Dale Schuurmans (ICML, 2022)

Making Linear MDPs Practical via Contrastive Representation Learning. - Tianjun Zhang; Tongzheng Ren; Mengjiao Yang 001; Joseph Gonzalez 001; Dale Schuurmans; Bo Dai 001 (ICML, 2022)

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. - Hongyu Ren; Hanjun Dai; Bo Dai 001; Xinyun Chen; Denny Zhou; Jure Leskovec; Dale Schuurmans (KDD, 2022)

A Simple Decentralized Cross-Entropy Method. - Zichen Zhang 001; Jun Jin 001; Martin Jägersand; Jun Luo 009; Dale Schuurmans (NeurIPS, 2022)

The Role of Baselines in Policy Gradient Optimization. - Jincheng Mei; Wesley Chung; Valentin Thomas; Bo Dai 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2022)

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. - Haoran Sun; Hanjun Dai; Dale Schuurmans (NeurIPS, 2022)

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. - Jason Wei; Xuezhi Wang 002; Dale Schuurmans; Maarten Bosma; Brian Ichter; Fei Xia 002; Ed H. Chi; Quoc V. Le; Denny Zhou (NeurIPS, 2022)

Chain of Thought Imitation with Procedure Cloning. - Mengjiao Yang 001; Dale Schuurmans; Pieter Abbeel; Ofir Nachum (NeurIPS, 2022)

On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. - Runyu Zhang 001; Jincheng Mei; Bo Dai 001; Dale Schuurmans; Na Li 002 (NeurIPS, 2022)

Chain of Thought Prompting Elicits Reasoning in Large Language Models. - Jason Wei; Xuezhi Wang 002; Dale Schuurmans; Maarten Bosma; Ed H. Chi; Quoc Le 001; Denny Zhou (CoRR, 2022)

On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems. - Runyu Zhang 001; Jincheng Mei; Bo Dai 001; Dale Schuurmans; Na Li 002 (CoRR, 2022)

Self-Consistency Improves Chain of Thought Reasoning in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Denny Zhou (CoRR, 2022)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Olivier Bousquet; Quoc Le 001; Ed H. Chi (CoRR, 2022)

Discrete Langevin Sampler via Wasserstein Gradient Flow. - Haoran Sun; Hanjun Dai; Bo Dai 001; Haomin Zhou 001; Dale Schuurmans (CoRR, 2022)

Rationale-Augmented Ensembles in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Denny Zhou (CoRR, 2022)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Bethany Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (CoRR, 2022)

TEMPERA: Test-Time Prompting via Reinforcement Learning. - Tianjun Zhang; Xuezhi Wang 002; Denny Zhou; Dale Schuurmans; Joseph E. Gonzalez (CoRR, 2022)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Yixin Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (AISTATS, 2023)

Discrete Langevin Samplers via Wasserstein Gradient Flow. - Haoran Sun; Hanjun Dai; Bo Dai 001; Haomin Zhou 001; Dale Schuurmans (AISTATS, 2023)

Self-Consistency Improves Chain of Thought Reasoning in Language Models. - Xuezhi Wang 002; Jason Wei; Dale Schuurmans; Quoc V. Le; Ed H. Chi; Sharan Narang; Aakanksha Chowdhery; Denny Zhou (ICLR, 2023)

What learning algorithm is in-context learning? Investigations with linear models. - Ekin Akyürek; Dale Schuurmans; Jacob Andreas; Tengyu Ma 001; Denny Zhou (ICLR, 2023)

Latent Variable Representation for Reinforcement Learning. - Tongzheng Ren; Chenjun Xiao; Tianjun Zhang; Na Li 002; Zhaoran Wang 001; Sujay Sanghavi; Dale Schuurmans; Bo Dai 001 (ICLR, 2023)

Spectral Decomposition Representation for Reinforcement Learning. - Tongzheng Ren; Tianjun Zhang; Lisa Lee; Joseph E. Gonzalez; Dale Schuurmans; Bo Dai 001 (ICLR, 2023)

Any-scale Balanced Samplers for Discrete Space. - Haoran Sun; Bo Dai 001; Charles Sutton; Dale Schuurmans; Hanjun Dai (ICLR, 2023)

Score-based Continuous-time Discrete Diffusion Models. - Haoran Sun; Lijun Yu; Bo Dai 001; Dale Schuurmans; Hanjun Dai (ICLR, 2023)

Dichotomy of Control: Separating What You Can Control from What You Cannot. - Sherry Yang 001; Dale Schuurmans; Pieter Abbeel; Ofir Nachum (ICLR, 2023)

TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. - Tianjun Zhang; Xuezhi Wang 002; Denny Zhou; Dale Schuurmans; Joseph E. Gonzalez (ICLR, 2023)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Claire Cui; Olivier Bousquet; Quoc V. Le; Ed H. Chi (ICLR, 2023)

Stochastic Gradient Succeeds for Bandits. - Jincheng Mei; Zixin Zhong; Bo Dai 001; Alekh Agarwal; Csaba Szepesvári; Dale Schuurmans (ICML, 2023)

Gradient-Free Structured Pruning with Unlabeled Data. - Azade Nova; Hanjun Dai; Dale Schuurmans (ICML, 2023)

Revisiting Sampling for Combinatorial Optimization. - Haoran Sun; Katayoon Goshvadi; Azade Nova; Dale Schuurmans; Hanjun Dai (ICML, 2023)

Learning Universal Policies via Text-Guided Video Generation. - Yilun Du; Sherry Yang 001; Bo Dai 001; Hanjun Dai; Ofir Nachum; Josh Tenenbaum 001; Dale Schuurmans; Pieter Abbeel (NeurIPS, 2023)

DISCS: A Benchmark for Discrete Sampling. - Katayoon Goshvadi; Haoran Sun; Xingchao Liu; Azade Nova; Ruqi Zhang; Will Grathwohl; Dale Schuurmans; Hanjun Dai (NeurIPS, 2023)

Ordering-based Conditions for Global Convergence of Policy Gradient Methods. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Mohammad Ghavamzadeh; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2023)

Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. - Zichen Vincent Zhang; Johannes Kirschner; Junxi Zhang; Francesco Zanini; Alex Ayoub; Masood Dehghan; Dale Schuurmans (NeurIPS, 2023)

Energy-based Predictive Representations for Partially Observed Reinforcement Learning. - Tianjun Zhang; Tongzheng Ren; Chenjun Xiao; Wenli Xiao; Joseph E. Gonzalez; Dale Schuurmans; Bo Dai 001 (UAI, 2023)

Memory Augmented Large Language Models are Computationally Universal. - Dale Schuurmans (CoRR, 2023)

Foundation Models for Decision Making: Problems, Methods, and Opportunities. - Sherry Yang 001; Ofir Nachum; Yilun Du; Jason Wei; Pieter Abbeel; Dale Schuurmans (CoRR, 2023)

Probabilistic Adaptation of Text-to-Video Models. - Mengjiao Yang 001; Yilun Du; Bo Dai 001; Dale Schuurmans; Joshua B. Tenenbaum; Pieter Abbeel (CoRR, 2023)

Learning Interactive Real-World Simulators. - Mengjiao Yang 001; Yilun Du; Kamyar Ghasemipour; Jonathan Tompson; Dale Schuurmans; Pieter Abbeel (CoRR, 2023)

Large Language Models can Learn Rules. - Zhaocheng Zhu; Yuan Xue; Xinyun Chen; Denny Zhou; Jian Tang 005; Dale Schuurmans; Hanjun Dai (CoRR, 2023)

Curvature Explains Loss of Plasticity. - Alex Lewandowski; Haruto Tanaka; Dale Schuurmans; Marlos C. Machado (CoRR, 2023)

Reinforcement Teaching. - Calarina Muslimani; Alex Lewandowski; Dale Schuurmans; Matthew E. Taylor; Jun Luo 009 (Trans. Mach. Learn. Res., 2023)

Scalable Diffusion for Materials Generation. - Sherry Yang 001; KwangHwan Cho; Amil Merchant; Pieter Abbeel; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (ICLR, 2024)

Probabilistic Adaptation of Black-Box Text-to-Video Models. - Sherry Yang 001; Yilun Du; Bo Dai 001; Dale Schuurmans; Joshua B. Tenenbaum; Pieter Abbeel (ICLR, 2024)

Learning Interactive Real-World Simulators. - Sherry Yang 001; Yilun Du; Seyed Kamyar Seyed Ghasemipour; Jonathan Tompson; Leslie Pack Kaelbling; Dale Schuurmans; Pieter Abbeel (ICLR, 2024)

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. - Fengdi Che; Chenjun Xiao; Jincheng Mei; Bo Dai 001; Ramki Gummadi; Oscar A. Ramirez; Christopher K. Harris; A. Rupam Mahmood; Dale Schuurmans (ICML, 2024)

Position: Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (ICML, 2024)

Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. - Hongming Zhang 012; Tongzheng Ren; Chenjun Xiao; Dale Schuurmans; Bo Dai 001 (ICML, 2024)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

UQE: A Query Engine for Unstructured Databases. - Hanjun Dai; Bethany Wang; Xingchen Wan; Bo Dai 001; Sherry Yang 001; Azade Nova; Pengcheng Yin; Phitchaya Mangpo Phothilimthana; Charles Sutton; Dale Schuurmans (NeurIPS, 2024)

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Sharan Vaswani; Anant Raj; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2024)

Mitigating the Curse of Horizon in Monte-Carlo Returns. - Alex Ayoub; David Szepesvari; Francesco Zanini; Bryan Chan; Dhawal Gupta; Bruno Castro da Silva; Dale Schuurmans (RLJ, 2024)

Beyond Expectations: Learning with Stochastic Dominance Made Practical. - Shicong Cen; Jincheng Mei; Hanjun Dai; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (CoRR, 2024)

Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (CoRR, 2024)

Soft Preference Optimization: Aligning Language Models to Expert Distributions. - Arsalan Sharifnassab; Sina Ghiassian; Saber Salehkaleybar; Surya Kanoria; Dale Schuurmans (CoRR, 2024)

Learning Continually by Spectral Regularization. - Alex Lewandowski; Saurabh Kumar 004; Dale Schuurmans; András György 001; Marlos C. Machado (CoRR, 2024)

Exploring and Benchmarking the Planning Capabilities of Large Language Models. - Bernd Bohnet; Azade Nova; Aaron T. Parisi; Kevin Swersky; Katayoon Goshvadi; Hanjun Dai; Dale Schuurmans; Noah Fiedel; Hanie Sedghi (CoRR, 2024)

Autoregressive Large Language Models are Computationally Universal. - Dale Schuurmans; Hanjun Dai; Francesco Zanini (CoRR, 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (CoRR, 2024)

Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback. - Hiroki Furuta; Heiga Zen; Dale Schuurmans; Aleksandra Faust; Yutaka Matsuo; Percy Liang; Sherry Yang 001 (CoRR, 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (AISTATS, 2025)

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. - Shicong Cen; Jincheng Mei; Katayoon Goshvadi; Hanjun Dai; Tong Yang 007; Sherry Yang 001; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (ICLR, 2025)

Toward Understanding In-context vs. In-weight Learning. - Bryan Chan; Xinyi Chen; András György 001; Dale Schuurmans (ICLR, 2025)

Learning Continually by Spectral Regularization. - Alex Lewandowski; Michal Bortkiewicz; Saurabh Kumar 004; András György 001; Dale Schuurmans; Mateusz Ostaszewski; Marlos C. Machado (ICLR, 2025)

Plastic Learning with Deep Fourier Features. - Alex Lewandowski; Dale Schuurmans; Marlos C. Machado (ICLR, 2025)

Improving Large Language Model Planning with Action Sequence Similarity. - Xinran Zhao; Hanie Sedghi; Bernd Bohnet; Dale Schuurmans; Azade Nova (ICLR, 2025)

Evolving Deeper LLM Thinking. - Kuang-Huei Lee; Ian Fischer; Yueh-Hua Wu; Dave Marwood; Shumeet Baluja; Dale Schuurmans; Xinyun Chen (CoRR, 2025)

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training. - Tianzhe Chu; Yuexiang Zhai; Jihan Yang; Shengbang Tong; Saining Xie; Dale Schuurmans; Quoc V. Le; Sergey Levine; Yi Ma 001 (CoRR, 2025)

Representation Learning via Non-Contrastive Mutual Information. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Khimya Khetarpal; Dale Schuurmans; Bo Dai 001 (CoRR, 2025)

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation. - Max Qiushi Lin; Jincheng Mei; Matin Aghaei; Michael Lu; Bo Dai 001; Alekh Agarwal; Dale Schuurmans; Csaba Szepesvári; Sharan Vaswani (CoRR, 2025)