| 2025 | AISTATS | Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. | Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai |
| 2025 | ICLR | Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. | Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai |
| 2025 | ICLR | Toward Understanding In-context vs. In-weight Learning. | Bryan Chan, Xinyi Chen, Andrs Gyrgy, Dale Schuurmans |
| 2025 | ICLR | Learning Continually by Spectral Regularization. | Alex Lewandowski, Michal Bortkiewicz, Saurabh Kumar, Andrs Gyrgy, Dale Schuurmans, Mateusz Ostaszewski, Marlos C. Machado |
| 2025 | ICLR | Plastic Learning with Deep Fourier Features. | Alex Lewandowski, Dale Schuurmans, Marlos C. Machado |
| 2025 | ICLR | Improving Large Language Model Planning with Action Sequence Similarity. | Xinran Zhao, Hanie Sedghi, Bernd Bohnet, Dale Schuurmans, Azade Nova |
| 2025 | ICML | 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 |
| 2024 | ICLR | Scalable Diffusion for Materials Generation. | Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk |
| 2024 | ICLR | Probabilistic Adaptation of Black-Box Text-to-Video Models. | Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel |
| 2024 | ICLR | Learning Interactive Real-World Simulators. | Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel |
| 2024 | ICML | Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. | Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans |
| 2024 | ICML | Position: Video as the New Language for Real-World Decision Making. | Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, Andr Barreto, Pieter Abbeel, Dale Schuurmans |
| 2024 | ICML | Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. | Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai |
| 2023 | AISTATS | Learning to Optimize with Stochastic Dominance Constraints. | Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai |
| 2023 | AISTATS | Discrete Langevin Samplers via Wasserstein Gradient Flow. | Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans |
| 2023 | ICLR | Self-Consistency Improves Chain of Thought Reasoning in Language Models. | Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou |
| 2023 | ICLR | What learning algorithm is in-context learning? Investigations with linear models. | Ekin Akyrek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou |
| 2023 | ICLR | Latent Variable Representation for Reinforcement Learning. | Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai |
| 2023 | ICLR | Spectral Decomposition Representation for Reinforcement Learning. | Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai |
| 2023 | ICLR | Any-scale Balanced Samplers for Discrete Space. | Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai |
| 2023 | ICLR | Score-based Continuous-time Discrete Diffusion Models. | Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai |
| 2023 | ICLR | Dichotomy of Control: Separating What You Can Control from What You Cannot. | Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum |
| 2023 | ICLR | TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. | Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez |
| 2023 | ICLR | Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. | Denny Zhou, Nathanael Schrli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi |
| 2023 | ICML | Stochastic Gradient Succeeds for Bandits. | Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvri, Dale Schuurmans |
| 2023 | ICML | Gradient-Free Structured Pruning with Unlabeled Data. | Azade Nova, Hanjun Dai, Dale Schuurmans |
| 2023 | ICML | Revisiting Sampling for Combinatorial Optimization. | Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai |
| 2023 | UAI | Energy-based Predictive Representations for Partially Observed Reinforcement Learning. | Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai |
| 2022 | AISTATS | The Curse of Passive Data Collection in Batch Reinforcement Learning. | Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvri |
| 2022 | AISTATS | Offline Policy Selection under Uncertainty. | Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans |
| 2022 | ICLR | Neural Stochastic Dual Dynamic Programming. | Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai |
| 2022 | ICLR | Understanding and Leveraging Overparameterization in Recursive Value Estimation. | Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A. Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans |
| 2022 | ICML | Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. | Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai |
| 2022 | ICML | A Parametric Class of Approximate Gradient Updates for Policy Optimization. | Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans |
| 2022 | ICML | Making Linear MDPs Practical via Contrastive Representation Learning. | Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai |
| 2022 | KDD | SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. | Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans |
| 2021 | AAAI | Deep Probabilistic Canonical Correlation Analysis. | Mahdi Karami, Dale Schuurmans |
| 2021 | ICML | EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. | Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu |
| 2021 | ICML | Leveraging Non-uniformity in First-order Non-convex Optimization. | Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvri, Dale Schuurmans |
| 2021 | ICML | LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. | Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou |
| 2021 | ICML | Characterizing the Gap Between Actor-Critic and Policy Gradient. | Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans |
| 2021 | ICML | On the Optimality of Batch Policy Optimization Algorithms. | Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvri, Dale Schuurmans |
| 2020 | ICLR | GenDICE: Generalized Offline Estimation of Stationary Values. | Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans |
| 2020 | ICML | An Optimistic Perspective on Offline Reinforcement Learning. | Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi |
| 2020 | ICML | Scalable Deep Generative Modeling for Sparse Graphs. | Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans |
| 2020 | ICML | On the Global Convergence Rates of Softmax Policy Gradient Methods. | Jincheng Mei, Chenjun Xiao, Csaba Szepesvri, Dale Schuurmans |
| 2020 | ICML | ConQUR: Mitigating Delusional Bias in Deep Q-Learning. | Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier |
| 2020 | ICML | Batch Stationary Distribution Estimation. | Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans |
| 2020 | ICML | Domain Aggregation Networks for Multi-Source Domain Adaptation. | Junfeng Wen, Russell Greiner, Dale Schuurmans |
| 2020 | ICML | Energy-Based Processes for Exchangeable Data. | Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans |
| 2020 | ICML | Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. | Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans |
| 2019 | AISTATS | Kernel Exponential Family Estimation via Doubly Dual Embedding. | Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He |
| 2019 | ICML | Learning to Generalize from Sparse and Underspecified Rewards. | Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi |
| 2019 | ICML | Understanding the Impact of Entropy on Policy Optimization. | Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans |
| 2019 | ICML | The Value Function Polytope in Reinforcement Learning. | Robert Dadashi, Marc G. Bellemare, Adrien Ali Taga, Nicolas Le Roux, Dale Schuurmans |
| 2019 | IJCAI | On Principled Entropy Exploration in Policy Optimization. | Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Mller |
| 2019 | IJCAI | Advantage Amplification in Slowly Evolving Latent-State Environments. | Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier |
| 2018 | AISTATS | Variational Rejection Sampling. | Aditya Grover, Ramki Gummadi, Miguel Lzaro-Gredilla, Dale Schuurmans, Stefano Ermon |
| 2018 | ICLR | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. | Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans |
| 2018 | ICML | Smoothed Action Value Functions for Learning Gaussian Policies. | Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans |
| 2018 | IJCAI | Planning and Learning with Stochastic Action Sets. | Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans |
| 2017 | AAAI | Formalizing Anthropomorphism Through Games: A Study in Deep Neural Networks. | Martin A. Zinkevich, Dale Schuurmans |
| 2017 | ICLR | Improving Policy Gradient by Exploring Under-appreciated Rewards. | Ofir Nachum, Mohammad Norouzi, Dale Schuurmans |
| 2017 | IJCAI | Logistic Markov Decision Processes. | Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu |
| 2017 | UAI | Holographic Feature Representations of Deep Networks. | Martin A. Zinkevich, Alex Davies, Dale Schuurmans |
| 2016 | AISTATS | Scalable and Sound Low-Rank Tensor Learning. | Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans |
| 2016 | AISTATS | Stochastic Neural Networks with Monotonic Activation Functions. | Siamak Ravanbakhsh, Barnabs Pczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner |
| 2015 | AAAI | Optimal Estimation of Multivariate ARMA Models. | Martha White, Junfeng Wen, Michael Bowling, Dale Schuurmans |
| 2015 | AISTATS | Variance Reduction via Antithetic Markov Chains. | James Neufeld, Dale Schuurmans, Michael H. Bowling |
| 2015 | ICCV | Semi-Supervised Zero-Shot Classification with Label Representation Learning. | Xin Li, Yuhong Guo, Dale Schuurmans |
| 2015 | IJCAI | Correcting Covariate Shift with the Frank-Wolfe Algorithm. | Junfeng Wen, Russell Greiner, Dale Schuurmans |
| 2014 | AAAI | Convex Co-embedding. | Farzaneh Mirzazadeh, Yuhong Guo, Dale Schuurmans |
| 2014 | ICML | Adaptive Monte Carlo via Bandit Allocation. | James Neufeld, Andrs Gyrgy, Csaba Szepesvri, Dale Schuurmans |
| 2013 | ACML | Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning. | Karim T. Abou-Moustafa, Dale Schuurmans, Frank P. Ferrie |
| 2013 | ICML | Characterizing the Representer Theorem. | Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvri |
| 2013 | PSB | Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM. | Yi Shi, Xinhua Zhang, Xiaoping Liao, Guohui Lin, Dale Schuurmans |
| 2013 | UAI | Convex Relaxations of Bregman Divergence Clustering. | Hao Cheng, Xinhua Zhang, Dale Schuurmans |
| 2012 | ICML | Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. | James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans |
| 2012 | WABI | Sparse Learning Based Linear Coherent Bi-clustering. | Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, Dale Schuurmans |
| 2011 | AAAI | Adaptive Large Margin Training for Multilabel Classification. | Yuhong Guo, Dale Schuurmans |
| 2011 | AAAI | Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. | Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans |
| 2011 | IJCAI | Modular Community Detection in Networks. | Wenye Li, Dale Schuurmans |
| 2011 | UAI | Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. | Yaoliang Yu, Dale Schuurmans |
| 2010 | COCOA | Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set Clustering. | Yi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans |
| 2010 | CoNLL | Improved Natural Language Learning via Variance-Regularization Support Vector Machines. | Shane Bergsma, Dekang Lin, Dale Schuurmans |
| 2010 | ICDM | Distributed Flow Algorithms for Scalable Similarity Visualization. | Novi Quadrianto, Dale Schuurmans, Alexander J. Smola |
| 2009 | ACML | A Reformulation of Support Vector Machines for General Confidence Functions. | Yuhong Guo, Dale Schuurmans |
| 2009 | COCOA | Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting. | Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans |
| 2009 | CVPR | Fast normalized cut with linear constraints. | Linli Xu, Wenye Li, Dale Schuurmans |
| 2009 | ICIG | Discriminative Maximum Margin Image Object Categorization with Exact Inference. | Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurmans |
| 2009 | ICML | Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. | Linli Xu, Martha White, Dale Schuurmans |
| 2008 | ACL | Semi-Supervised Convex Training for Dependency Parsing. | Qin Iris Wang, Dale Schuurmans, Dekang Lin |
| 2007 | IJCAI | Automatic Gait Optimization with Gaussian Process Regression. | Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans |
| 2007 | IJCAI | Simple Training of Dependency Parsers via Structured Boosting. | Qin Iris Wang, Dekang Lin, Dale Schuurmans |
| 2007 | RECOMB | Learning Gene Regulatory Networks via Globally Regularized Risk Minimization. | Yuhong Guo, Dale Schuurmans |
| 2006 | AAAI | Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. | Tao Wang, Pascal Poupart, Michael H. Bowling, Dale Schuurmans |
| 2006 | AAAI | Robust Support Vector Machine Training via Convex Outlier Ablation. | Linli Xu, Koby Crammer, Dale Schuurmans |
| 2006 | ACL | Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. | Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans |
| 2006 | AVSS | An Online Discriminative Approach to Background Subtraction. | Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli, S. V. N. Vishwanathan |
| 2006 | CoNLL | Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization. | Qin Iris Wang, Colin Cherry, Daniel J. Lizotte, Dale Schuurmans |
| 2006 | ICML | Discriminative unsupervised learning of structured predictors. | Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans |
| 2006 | UAI | Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. | Yuhong Guo, Dale Schuurmans |
| 2005 | AAAI | Unsupervised and Semi-Supervised Multi-Class Support Vector Machines. | Linli Xu, Dale Schuurmans |
| 2005 | CVPR | Tangent-Corrected Embedding. | Ali Ghodsi, Jiayuan Huang, Finnegan Southey, Dale Schuurmans |
| 2005 | ICML | Variational Bayesian image modelling. | Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang |
| 2005 | ICML | Bayesian sparse sampling for on-line reward optimization. | Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans |
| 2005 | ICML | Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. | Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng |
| 2005 | IJCAI | Regret-based Utility Elicitation in Constraint-based Decision Problems. | Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans |
| 2005 | IJCAI | Learning Coordination Classifiers. | Yuhong Guo, Russell Greiner, Dale Schuurmans |
| 2005 | UAI | Maximum Margin Bayesian Networks. | Yuhong Guo, Dana F. Wilkinson, Dale Schuurmans |
| 2004 | ECCV | Transformation-Invariant Embedding for Image Analysis. | Ali Ghodsi, Jiayuan Huang, Dale Schuurmans |
| 2003 | AI | Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. | Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, Nick Cercone |
| 2003 | AI | Model-Based Least-Squares Policy Evaluation. | Fletcher Lu, Dale Schuurmans |
| 2003 | AISTATS | Latent Maximum Entropy Approach for Semantic N-gram Language Modeling. | Shaojun Wang, Dale Schuurmans, Fuchun Peng |
| 2003 | ALT | Learning Continuous Latent Variable Models with Bregman Divergences. | Shaojun Wang, Dale Schuurmans |
| 2003 | CP | Constraint-Based Optimization with the Minimax Decision Criterion. | Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans |
| 2003 | CVPR | Face Alignment Using Statistical Models and Wavelet Features. | Feng Jiao, Stan Z. Li, Heung-Yeung Shum, Dale Schuurmans |
| 2003 | EACL | Language Independent Authorship Attribution with Character Level N-Grams. | Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang |
| 2003 | ECIR | Combining Naive Bayes and n-Gram Language Models for Text Classification. | Fuchun Peng, Dale Schuurmans |
| 2003 | ICASSP | Semantic n-gram language modeling with the latent maximum entropy principle. | Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao |
| 2003 | ICML | Learning Mixture Models with the Latent Maximum Entropy Principle. | Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao |
| 2003 | IJCNN | Automatic basis selection for RBF networks using Stein's unbiased risk estimator. | Ali Ghodsi, Dale Schuurmans |
| 2003 | NAACL | Language and Task Independent Text Categorization with Simple Language Models. | Fuchun Peng, Dale Schuurmans, Shaojun Wang |
| 2003 | UAI | Monte Carlo Matrix Inversion Policy Evaluation. | Fletcher Lu, Dale Schuurmans |
| 2003 | UAI | Boltzmann Machine Learning with the Latent Maximum Entropy Principle. | Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao |
| 2002 | AAAI | Data Perturbation for Escaping Local Maxima in Learning. | Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans |
| 2002 | AAAI | Greedy Linear Value-Approximation for Factored Markov Decision Processes. | Relu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin |
| 2002 | AAAI | Piecewise Linear Value Function Approximation for Factored MDPs. | Pascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans |
| 2002 | COLING | Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR. | Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone |
| 2002 | ICML | Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs. | Carlos Guestrin, Relu Patrascu, Dale Schuurmans |
| 2002 | ICML | Investigating the Maximum Likelihood Alternative to TD(lambda). | Fletcher Lu, Relu Patrascu, Dale Schuurmans |
| 2002 | SIGIR | Using self-supervised word segmentation in Chinese information retrieval. | Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone, Stephen E. Robertson |
| 2001 | IJCAI | The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming. | Dale Schuurmans, Finnegan Southey, Robert C. Holte |
| 2001 | IDA | Self-Supervised Chinese Word Segmentation. | Fuchun Peng, Dale Schuurmans |
| 2000 | AAAI | Local Search Characteristics of Incomplete SAT Procedures. | Dale Schuurmans, Finnegan Southey |
| 2000 | ICML | An Adaptive Regularization Criterion for Supervised Learning. | Dale Schuurmans, Finnegan Southey |
| 2000 | UAI | Monte Carlo inference via greedy importance sampling. | Dale Schuurmans, Finnegan Southey |
| 1999 | AAAI | Efficient exploration for optimizing immediate reward. | Dale Schuurmans, Lloyd G. Greenwald |
| 1998 | AAAI | Boosting in the Limit: Maximizing the Margin of Learned Ensembles. | Adam J. Grove, Dale Schuurmans |
| 1997 | AAAI | A New Metric-Based Approach to Model Selection. | Dale Schuurmans |
| 1997 | COLT | General Convergence Results for Linear Discriminant Updates. | Adam J. Grove, Nick Littlestone, Dale Schuurmans |
| 1997 | ICML | Characterizing the generalization performance of model selection strategies. | Dale Schuurmans, Lyle H. Ungar, Dean P. Foster |
| 1997 | UAI | Learning Bayesian Nets that Perform Well. | Russell Greiner, Adam J. Grove, Dale Schuurmans |
| 1995 | COLT | Sequential PAC Learning. | Dale Schuurmans, Russell Greiner |
| 1995 | IJCAI | Practical PAC Learning. | Dale Schuurmans, Russell Greiner |
| 1992 | ECAI | Learning an Optimally Accurate Representation System. | Russell Greiner, Dale Schuurmans |
| 1992 | KR | Learning Useful Horn Approximations. | Russell Greiner, Dale Schuurmans |