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ORCIDDale Schuurmans
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- affiliation: University of Alberta
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2020 – today
- 2024
[j24]Alex Ayoub, David Szepesvari, Francesco Zanini, Bryan Chan, Dhawal Gupta, Bruno Castro da Silva, Dale Schuurmans:
Mitigating the Curse of Horizon in Monte-Carlo Returns. RLJ 2: 563-572 (2024)- [c208]Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk:
Scalable Diffusion for Materials Generation. ICLR 2024 - [c207]Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Black-Box Text-to-Video Models. ICLR 2024 - [c206]Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. ICLR 2024 - [c205]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. ICML 2024 - [c204]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Position: Video as the New Language for Real-World Decision Making. ICML 2024 - [c203]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. ICML 2024 - [i103]Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai:
Beyond Expectations: Learning with Stochastic Dominance Made Practical. CoRR abs/2402.02698 (2024) - [i102]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Video as the New Language for Real-World Decision Making. CoRR abs/2402.17139 (2024) - [i101]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. CoRR abs/2402.17235 (2024) - [i100]Arsalan Sharifnassab, Sina Ghiassian, Saber Salehkaleybar, Surya Kanoria, Dale Schuurmans:
Soft Preference Optimization: Aligning Language Models to Expert Distributions. CoRR abs/2405.00747 (2024) - [i99]Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai:
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. CoRR abs/2405.19320 (2024) - [i98]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. CoRR abs/2405.21043 (2024) - [i97]Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado:
Learning Continually by Spectral Regularization. CoRR abs/2406.06811 (2024) - [i96]Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi:
Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR abs/2406.13094 (2024) - [i95]Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans:
UQE: A Query Engine for Unstructured Databases. CoRR abs/2407.09522 (2024) - [i94]Sherry Yang, Simon L. Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk:
Generative Hierarchical Materials Search. CoRR abs/2409.06762 (2024) - [i93]Dale Schuurmans, Hanjun Dai, Francesco Zanini:
Autoregressive Large Language Models are Computationally Universal. CoRR abs/2410.03170 (2024) - [i92]Alex Lewandowski, Dale Schuurmans, Marlos C. Machado:
Plastic Learning with Deep Fourier Features. CoRR abs/2410.20634 (2024) - [i91]Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai:
Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. CoRR abs/2410.20727 (2024) - [i90]Bryan Chan, Xinyi Chen, András György, Dale Schuurmans:
Toward Understanding In-context vs. In-weight Learning. CoRR abs/2410.23042 (2024) - [i89]Hiroki Furuta, Heiga Zen, Dale Schuurmans, Aleksandra Faust, Yutaka Matsuo, Percy Liang, Sherry Yang:
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback. CoRR abs/2412.02617 (2024) - 2023
- [j23]Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo:
Reinforcement Teaching. Trans. Mach. Learn. Res. 2023 (2023) - [c202]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Samplers via Wasserstein Gradient Flow. AISTATS 2023: 6290-6313 - [c201]Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. AISTATS 2023: 8991-9009 - [c200]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. ICLR 2023 - [c199]Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou:
What learning algorithm is in-context learning? Investigations with linear models. ICLR 2023 - [c198]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. ICLR 2023 - [c197]Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Spectral Decomposition Representation for Reinforcement Learning. ICLR 2023 - [c196]Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai:
Any-scale Balanced Samplers for Discrete Space. ICLR 2023 - [c195]Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai:
Score-based Continuous-time Discrete Diffusion Models. ICLR 2023 - [c194]Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. ICLR 2023 - [c193]Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez:
TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. ICLR 2023 - [c192]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ICLR 2023 - [c191]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. ICML 2023: 24325-24360 - [c190]Azade Nova, Hanjun Dai, Dale Schuurmans:
Gradient-Free Structured Pruning with Unlabeled Data. ICML 2023: 26326-26341 - [c189]Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai:
Revisiting Sampling for Combinatorial Optimization. ICML 2023: 32859-32874 - [c188]Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. NeurIPS 2023 - [c187]Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai:
DISCS: A Benchmark for Discrete Sampling. NeurIPS 2023 - [c186]Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans:
Ordering-based Conditions for Global Convergence of Policy Gradient Methods. NeurIPS 2023 - [c185]Zichen Vincent Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans:
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. NeurIPS 2023 - [c184]Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Energy-based Predictive Representations for Partially Observed Reinforcement Learning. UAI 2023: 2477-2487 - [i88]Dale Schuurmans:
Memory Augmented Large Language Models are Computationally Universal. CoRR abs/2301.04589 (2023) - [i87]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. CoRR abs/2301.06276 (2023) - [i86]Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. CoRR abs/2302.00111 (2023) - [i85]Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel, Dale Schuurmans:
Foundation Models for Decision Making: Problems, Methods, and Opportunities. CoRR abs/2303.04129 (2023) - [i84]Azade Nova, Hanjun Dai, Dale Schuurmans:
Gradient-Free Structured Pruning with Unlabeled Data. CoRR abs/2303.04185 (2023) - [i83]Mengjiao Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Text-to-Video Models. CoRR abs/2306.01872 (2023) - [i82]Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. CoRR abs/2310.06114 (2023) - [i81]Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, Hanjun Dai:
Large Language Models can Learn Rules. CoRR abs/2310.07064 (2023) - [i80]Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk:
Scalable Diffusion for Materials Generation. CoRR abs/2311.09235 (2023) - [i79]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. CoRR abs/2311.12244 (2023) - [i78]Alex Lewandowski, Haruto Tanaka, Dale Schuurmans, Marlos C. Machado:
Curvature Explains Loss of Plasticity. CoRR abs/2312.00246 (2023) - 2022
- [c183]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. AISTATS 2022: 4376-4396 - [c182]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
The Curse of Passive Data Collection in Batch Reinforcement Learning. AISTATS 2022: 8413-8438 - [c181]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. ICLR 2022 - [c180]Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A. Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Understanding and Leveraging Overparameterization in Recursive Value Estimation. ICLR 2022 - [c179]Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai:
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. ICML 2022: 4605-4617 - [c178]Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans:
A Parametric Class of Approximate Gradient Updates for Policy Optimization. ICML 2022: 7998-8015 - [c177]Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai:
Making Linear MDPs Practical via Contrastive Representation Learning. ICML 2022: 26447-26466 - [c176]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans:
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. KDD 2022: 1472-1482 - [c175]Zichen Zhang, Jun Jin, Martin Jägersand, Jun Luo, Dale Schuurmans:
A Simple Decentralized Cross-Entropy Method. NeurIPS 2022 - [c174]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. NeurIPS 2022 - [c173]Haoran Sun, Hanjun Dai, Dale Schuurmans:
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. NeurIPS 2022 - [c172]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou:
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022 - [c171]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. NeurIPS 2022 - [c170]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. NeurIPS 2022 - [i77]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, Denny Zhou:
Chain of Thought Prompting Elicits Reasoning in Large Language Models. CoRR abs/2201.11903 (2022) - [i76]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems. CoRR abs/2202.00872 (2022) - [i75]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. CoRR abs/2203.11171 (2022) - [i74]Alex Lewandowski, Calarina Muslimani, Matthew E. Taylor, Jun Luo, Dale Schuurmans:
Reinforcement Teaching. CoRR abs/2204.11897 (2022) - [i73]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. CoRR abs/2205.10625 (2022) - [i72]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. CoRR abs/2205.10816 (2022) - [i71]Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans:
A Parametric Class of Approximate Gradient Updates for Policy Optimization. CoRR abs/2206.08499 (2022) - [i70]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Sampler via Wasserstein Gradient Flow. CoRR abs/2206.14897 (2022) - [i69]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Rationale-Augmented Ensembles in Language Models. CoRR abs/2207.00747 (2022) - [i68]Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph E. Gonzalez
, Dale Schuurmans, Bo Dai:
Making Linear MDPs Practical via Contrastive Representation Learning. CoRR abs/2207.07150 (2022) - [i67]Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Spectral Decomposition Representation for Reinforcement Learning. CoRR abs/2208.09515 (2022) - [i66]Haoran Sun, Hanjun Dai, Dale Schuurmans:
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. CoRR abs/2209.08183 (2022) - [i65]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. CoRR abs/2210.13435 (2022) - [i64]Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. CoRR abs/2211.07767 (2022) - [i63]Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez:
TEMPERA: Test-Time Prompting via Reinforcement Learning. CoRR abs/2211.11890 (2022) - [i62]Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou:
What learning algorithm is in-context learning? Investigations with linear models. CoRR abs/2211.15661 (2022) - [i61]Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai:
Score-based Continuous-time Discrete Diffusion Models. CoRR abs/2211.16750 (2022) - [i60]Zichen Zhang, Jun Jin, Martin Jägersand, Jun Luo, Dale Schuurmans:
A Simple Decentralized Cross-Entropy Method. CoRR abs/2212.08235 (2022) - [i59]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. CoRR abs/2212.08765 (2022) - [i58]Zichen Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans:
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. CoRR abs/2212.08949 (2022) - 2021
- [c169]Mahdi Karami, Dale Schuurmans:
Deep Probabilistic Canonical Correlation Analysis. AAAI 2021: 8055-8063 - [c168]Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu:
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. ICML 2021: 3682-3691 - [c167]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. ICML 2021: 7555-7564 - [c166]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou:
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. ICML 2021: 8959-8970 - [c165]Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans:
Characterizing the Gap Between Actor-Critic and Policy Gradient. ICML 2021: 11101-11111 - [c164]Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. ICML 2021: 11362-11371 - [c163]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. NeurIPS 2021: 19339-19351 - [c162]Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai:
Combiner: Full Attention Transformer with Sparse Computation Cost. NeurIPS 2021: 22470-22482 - [i57]Nevena Lazic, Botao Hao, Yasin Abbasi-Yadkori, Dale Schuurmans, Csaba Szepesvári:
Optimization Issues in KL-Constrained Approximate Policy Iteration. CoRR abs/2102.06234 (2021) - [i56]Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. CoRR abs/2104.02293 (2021) - [i55]Dennis Lee, Natasha Jaques, J. Chase Kew, Douglas Eck, Dale Schuurmans, Aleksandra Faust:
Joint Attention for Multi-Agent Coordination and Social Learning. CoRR abs/2104.07750 (2021) - [i54]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. CoRR abs/2105.06072 (2021) - [i53]Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans:
Characterizing the Gap Between Actor-Critic and Policy Gradient. CoRR abs/2106.06932 (2021) - [i52]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. CoRR abs/2106.09973 (2021) - [i51]Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai:
Combiner: Full Attention Transformer with Sparse Computation Cost. CoRR abs/2107.05768 (2021) - [i50]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans:
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. CoRR abs/2110.14890 (2021) - [i49]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. CoRR abs/2110.15572 (2021) - [i48]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. CoRR abs/2112.00874 (2021) - 2020
- [c161]Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans:
GenDICE: Generalized Offline Estimation of Stationary Values. ICLR 2020 - [c160]Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi:
An Optimistic Perspective on Offline Reinforcement Learning. ICML 2020: 104-114 - [c159]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. ICML 2020: 2302-2312 - [c158]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. ICML 2020: 6820-6829 - [c157]Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier:
ConQUR: Mitigating Delusional Bias in Deep Q-Learning. ICML 2020: 9187-9195 - [c156]Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans:
Batch Stationary Distribution Estimation. ICML 2020: 10203-10213 - [c155]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Domain Aggregation Networks for Multi-Source Domain Adaptation. ICML 2020: 10214-10224 - [c154]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. ICML 2020: 10681-10692 - [c153]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c152]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS 2020 - [c151]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [c150]Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans:
A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs. NeurIPS 2020 - [c149]Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
Escaping the Gravitational Pull of Softmax. NeurIPS 2020 - [c148]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. NeurIPS 2020 - [i47]Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans:
GenDICE: Generalized Offline Estimation of Stationary Values. CoRR abs/2002.09072 (2020) - [i46]Andy Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier:
ConQUR: Mitigating Delusional Bias in Deep Q-learning. CoRR abs/2002.12399 (2020) - [i45]Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans:
Batch Stationary Distribution Estimation. CoRR abs/2003.00722 (2020) - [i44]Mahdi Karami, Dale Schuurmans:
Variational Inference for Deep Probabilistic Canonical Correlation Analysis. CoRR abs/2003.04292 (2020) - [i43]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. CoRR abs/2003.07521 (2020) - [i42]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. CoRR abs/2005.06392 (2020) - [i41]Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans:
A maximum-entropy approach to off-policy evaluation in average-reward MDPs. CoRR abs/2006.12620 (2020) - [i40]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. CoRR abs/2006.15502 (2020) - [i39]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i38]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. CoRR abs/2007.03438 (2020) - [i37]Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu:
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. CoRR abs/2007.11091 (2020) - [i36]Nan Ding, Xinjie Fan, Zhenzhong Lan, Dale Schuurmans, Radu Soricut:
Attention that does not Explain Away. CoRR abs/2009.14308 (2020) - [i35]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. CoRR abs/2010.11652 (2020) - [i34]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR abs/2011.05363 (2020) - [i33]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. CoRR abs/2012.06919 (2020)
2010 – 2019
- 2019
- [c147]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS 2019: 2321-2330 - [c146]Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. ICML 2019: 130-140 - [c145]Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the Impact of Entropy on Policy Optimization. ICML 2019: 151-160 - [c144]Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans:
The Value Function Polytope in Reinforcement Learning. ICML 2019: 1486-1495 - [c143]Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller:
On Principled Entropy Exploration in Policy Optimization. IJCAI 2019: 3130-3136 - [c142]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI 2019: 3165-3172 - [c141]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS 2019: 4360-4371 - [c140]Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth:
Invertible Convolutional Flow. NeurIPS 2019: 5636-5646 - [c139]Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making. NeurIPS 2019: 8825-8835 - [c138]Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller:
Maximum Entropy Monte-Carlo Planning. NeurIPS 2019: 9516-9524 - [c137]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS 2019: 10977-10988 - [i32]Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare:
The Value Function Polytope in Reinforcement Learning. CoRR abs/1901.11524 (2019) - [i31]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR abs/1901.11530 (2019) - [i30]Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. CoRR abs/1902.07198 (2019) - [i29]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR abs/1904.12083 (2019) - [i28]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. CoRR abs/1905.13559 (2019) - [i27]Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi:
Striving for Simplicity in Off-policy Deep Reinforcement Learning. CoRR abs/1907.04543 (2019) - [i26]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Domain Aggregation Networks for Multi-Source Domain Adaptation. CoRR abs/1909.05352 (2019) - [i25]Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, Dale Schuurmans:
AlgaeDICE: Policy Gradient from Arbitrary Experience. CoRR abs/1912.02074 (2019) - [i24]Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Müller:
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning. CoRR abs/1912.11206 (2019) - 2018
- [c136]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. AISTATS 2018: 823-832 - [c135]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. ICLR (Poster) 2018 - [c134]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. ICML 2018: 3689-3697 - [c133]Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. IJCAI 2018: 4674-4682 - [c132]Tyler Lu, Dale Schuurmans, Craig Boutilier:
Non-delusional Q-learning and value-iteration. NeurIPS 2018: 9971-9981 - [i23]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. CoRR abs/1803.02348 (2018) - [i22]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. CoRR abs/1804.01712 (2018) - [i21]Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. CoRR abs/1805.02363 (2018) - [i20]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR abs/1811.02228 (2018) - [i19]Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the impact of entropy on policy optimization. CoRR abs/1811.11214 (2018) - 2017
- [j22]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. J. Mach. Learn. Res. 18: 144:1-144:46 (2017) - [c131]Martin A. Zinkevich, Dale Schuurmans:
Formalizing Anthropomorphism Through Games: A Study in Deep Neural Networks. AAAI Workshops 2017 - [c130]Ofir Nachum, Mohammad Norouzi, Dale Schuurmans:
Improving Policy Gradient by Exploring Under-appreciated Rewards. ICLR (Poster) 2017 - [c129]Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu:
Logistic Markov Decision Processes. IJCAI 2017: 2486-2493 - [c128]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Bridging the Gap Between Value and Policy Based Reinforcement Learning. NIPS 2017: 2775-2785 - [c127]Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvári:
Multi-view Matrix Factorization for Linear Dynamical System Estimation. NIPS 2017: 7092-7101 - [c126]Martin A. Zinkevich, Alex Davies, Dale Schuurmans:
Holographic Feature Representations of Deep Networks. UAI 2017 - [i18]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Bridging the Gap Between Value and Policy Based Reinforcement Learning. CoRR abs/1702.08892 (2017) - [i17]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. CoRR abs/1707.01891 (2017) - [i16]Tyler Lu, Martin Zinkevich, Craig Boutilier, Binz Roy, Dale Schuurmans:
Safe Exploration for Identifying Linear Systems via Robust Optimization. CoRR abs/1711.11165 (2017) - 2016
- [c125]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c124]Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans:
Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016: 1114-1123 - [c123]Dale Schuurmans, Martin Zinkevich:
Deep Learning Games. NIPS 2016: 1678-1686 - [c122]Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans:
Reward Augmented Maximum Likelihood for Neural Structured Prediction. NIPS 2016: 1723-1731 - [e3]Dale Schuurmans, Michael P. Wellman:
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. AAAI Press 2016, ISBN 978-1-57735-760-5 [contents] - [i15]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. CoRR abs/1601.00034 (2016) - [i14]Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans:
Reward Augmented Maximum Likelihood for Neural Structured Prediction. CoRR abs/1609.00150 (2016) - [i13]Ofir Nachum, Mohammad Norouzi, Dale Schuurmans:
Improving Policy Gradient by Exploring Under-appreciated Rewards. CoRR abs/1611.09321 (2016) - 2015
- [j21]Mohamed Elgendi, Richard Ribón Fletcher, Ian Norton, Matt Brearley, Derek Abbott, Nigel H. Lovell, Dale Schuurmans:
Frequency analysis of photoplethysmogram and its derivatives. Comput. Methods Programs Biomed. 122(3): 503-512 (2015) - [j20]Mohamed Elgendi, Rich Fletcher, Ian Norton, Matt Brearley, Derek Abbott, Nigel H. Lovell, Dale Schuurmans:
On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress. Sensors 15(10): 24716-24734 (2015) - [c121]Martha White, Junfeng Wen, Michael Bowling, Dale Schuurmans:
Optimal Estimation of Multivariate ARMA Models. AAAI 2015: 3080-3086 - [c120]James Neufeld, Dale Schuurmans, Michael H. Bowling:
Variance Reduction via Antithetic Markov Chains. AISTATS 2015 - [c119]Xin Li, Yuhong Guo, Dale Schuurmans:
Semi-Supervised Zero-Shot Classification with Label Representation Learning. ICCV 2015: 4211-4219 - [c118]Junfeng Wen, Russell Greiner, Dale Schuurmans:
Correcting Covariate Shift with the Frank-Wolfe Algorithm. IJCAI 2015: 1010-1016 - [c117]Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans:
Embedding Inference for Structured Multilabel Prediction. NIPS 2015: 3555-3563 - [c116]Karim T. Abou-Moustafa, Dale Schuurmans:
Generalization in Unsupervised Learning. ECML/PKDD (1) 2015: 300-317 - [c115]Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans:
Scalable Metric Learning for Co-Embedding. ECML/PKDD (1) 2015: 625-642 - [i12]Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvári:
Learning with a Strong Adversary. CoRR abs/1511.03034 (2015) - 2014
- [c114]Farzaneh Mirzazadeh, Yuhong Guo, Dale Schuurmans:
Convex Co-embedding. AAAI 2014: 1989-1996 - [c113]James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans:
Adaptive Monte Carlo via Bandit Allocation. ICML 2014: 1944-1952 - [c112]Özlem Aslan, Xinhua Zhang, Dale Schuurmans:
Convex Deep Learning via Normalized Kernels. NIPS 2014: 3275-3283 - [i11]James Neufeld, András György, Dale Schuurmans, Csaba Szepesvári:
Adaptive Monte Carlo via Bandit Allocation. CoRR abs/1405.3318 (2014) - [i10]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. CoRR abs/1410.4828 (2014) - 2013
- [j19]Shaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans:
Exploiting Syntactic, Semantic, and Lexical Regularities in Language Modeling via Directed Markov Random Fields. Comput. Intell. 29(4): 649-679 (2013) - [c111]Karim T. Abou-Moustafa, Dale Schuurmans, Frank P. Ferrie:
Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning. ACML 2013: 341-356 - [c110]Karim T. Abou-Moustafa, Frank P. Ferrie, Dale Schuurmans:
Divergence based graph estimation for manifold learning. GlobalSIP 2013: 447-450 - [c109]Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári:
Characterizing the Representer Theorem. ICML (1) 2013: 570-578 - [c108]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Polar Operators for Structured Sparse Estimation. NIPS 2013: 82-90 - [c107]Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Two-Layer Modeling. NIPS 2013: 2985-2993 - [c106]Yuhong Guo, Dale Schuurmans:
Multi-label Classification with Output Kernels. ECML/PKDD (2) 2013: 417-432 - [c105]Yi Shi, Xinhua Zhang, Xiaoping Liao, Guohui Lin, Dale Schuurmans:
Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM. Pacific Symposium on Biocomputing 2013: 41-52 - [c104]Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Relaxations of Bregman Divergence Clustering. UAI 2013 - [i9]Dale Schuurmans, Finnegan Southey:
Monte Carlo Inference via Greedy Importance Sampling. CoRR abs/1301.3890 (2013) - [i8]Russell Greiner, Adam J. Grove, Dale Schuurmans:
Learning Bayesian Nets that Perform Well. CoRR abs/1302.1542 (2013) - [i7]Hengshuai Yao, Dale Schuurmans:
Reinforcement Ranking. CoRR abs/1303.5988 (2013) - [i6]Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Relaxations of Bregman Divergence Clustering. CoRR abs/1309.6823 (2013) - 2012
- [j18]Yi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans:
Linear Coherent Bi-Clustering via Beam Searching and Sample Set Clustering. Discret. Math. Algorithms Appl. 4(2) (2012) - [j17]Jiming Peng, Lopamudra Mukherjee, Vikas Singh, Dale Schuurmans, Linli Xu:
An efficient algorithm for maximal margin clustering. J. Glob. Optim. 52(1): 123-137 (2012) - [j16]Daniel J. Lizotte, Russell Greiner, Dale Schuurmans:
An experimental methodology for response surface optimization methods. J. Glob. Optim. 53(4): 699-736 (2012) - [j15]Shaojun Wang, Dale Schuurmans, Yunxin Zhao:
The Latent Maximum Entropy Principle. ACM Trans. Knowl. Discov. Data 6(2): 8:1-8:42 (2012) - [c103]James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans:
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. ICML 2012 - [c102]Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Convex Multi-view Subspace Learning. NIPS 2012: 1682-1690 - [c101]Yaoliang Yu, Özlem Aslan, Dale Schuurmans:
A Polynomial-time Form of Robust Regression. NIPS 2012: 2492-2500 - [c100]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012: 2915-2923 - [c99]Yuhong Guo, Dale Schuurmans:
Semi-supervised Multi-label Classification - A Simultaneous Large-Margin, Subspace Learning Approach. ECML/PKDD (2) 2012: 355-370 - [c98]Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, Dale Schuurmans:
Sparse Learning Based Linear Coherent Bi-clustering. WABI 2012: 346-364 - [c97]Martha White, Dale Schuurmans:
Generalized Optimal Reverse Prediction. AISTATS 2012: 1305-1313 - [i5]Yaoliang Yu, Dale Schuurmans:
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. CoRR abs/1202.3772 (2012) - [i4]Yuhong Guo, Dale Schuurmans:
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. CoRR abs/1206.6832 (2012) - [i3]Yuhong Guo, Dana F. Wilkinson, Dale Schuurmans:
Maximum Margin Bayesian Networks. CoRR abs/1207.1382 (2012) - [i2]Fletcher Lu, Dale Schuurmans:
Monte Carlo Matrix Inversion Policy Evaluation. CoRR abs/1212.2471 (2012) - [i1]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Boltzmann Machine Learning with the Latent Maximum Entropy Principle. CoRR abs/1212.2514 (2012) - 2011
- [j14]Li Cheng, Minglun Gong, Dale Schuurmans, Terry Caelli:
Real-Time Discriminative Background Subtraction. IEEE Trans. Image Process. 20(5): 1401-1414 (2011) - [c96]Yuhong Guo, Dale Schuurmans:
Adaptive Large Margin Training for Multilabel Classification. AAAI 2011: 374-379 - [c95]Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans:
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. AAAI 2011: 567-573 - [c94]Yuxi Li, Dale Schuurmans:
MapReduce for Parallel Reinforcement Learning. EWRL 2011: 309-320 - [c93]Wenye Li, Dale Schuurmans:
Modular Community Detection in Networks. IJCAI 2011: 1366-1371 - [c92]Yaoliang Yu, Dale Schuurmans:
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. UAI 2011: 778-785 - 2010
- [c91]Yi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans:
Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set Clustering. COCOA (1) 2010: 85-103 - [c90]Shane Bergsma, Dekang Lin, Dale Schuurmans:
Improved Natural Language Learning via Variance-Regularization Support Vector Machines. CoNLL 2010: 172-181 - [c89]Novi Quadrianto, Dale Schuurmans, Alexander J. Smola:
Distributed Flow Algorithms for Scalable Similarity Visualization. ICDM Workshops 2010: 1220-1227 - [c88]Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans:
Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS 2010: 2532-2540 - [p2]Qin Iris Wang, Dale Schuurmans, Dekang Lin:
Strictly Lexicalised Dependency Parsing. Trends in Parsing Technology 2010: 105-120
2000 – 2009
- 2009
- [c87]Yuhong Guo, Dale Schuurmans:
A Reformulation of Support Vector Machines for General Confidence Functions. ACML 2009: 109-119 - [c86]Yuxi Li, Li Cheng, Dale Schuurmans:
Inference of the structural credit risk model using MLE. CIFEr 2009: 8-13 - [c85]Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans:
Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting. COCOA 2009: 73-84 - [c84]Linli Xu, Wenye Li, Dale Schuurmans:
Fast normalized cut with linear constraints. CVPR 2009: 2866-2873 - [c83]Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurmans:
Discriminative Maximum Margin Image Object Categorization with Exact Inference. ICIG 2009: 232-237 - [c82]Linli Xu, Martha White, Dale Schuurmans:
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. ICML 2009: 1137-1144 - [c81]Novi Quadrianto, Tibério S. Caetano, John Lim, Dale Schuurmans:
Convex Relaxation of Mixture Regression with Efficient Algorithms. NIPS 2009: 1491-1499 - [c80]Yaoliang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári:
A General Projection Property for Distribution Families. NIPS 2009: 2232-2240 - [c79]Yuxi Li, Csaba Szepesvári, Dale Schuurmans:
Learning Exercise Policies for American Options. AISTATS 2009: 352-359 - [c78]Min Yang, Yuxi Li, Dale Schuurmans:
Dual Temporal Difference Learning. AISTATS 2009: 631-638 - [e2]Daphne Koller, Dale Schuurmans, Yoshua Bengio, Léon Bottou:
Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8-11, 2008. Curran Associates, Inc. 2009 [contents] - [e1]Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta:
Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, ISBN 9781615679119 [contents] - 2008
- [c77]Qin Iris Wang, Dale Schuurmans, Dekang Lin:
Semi-Supervised Convex Training for Dependency Parsing. ACL 2008: 532-540 - [c76]Yuhong Guo, Dale Schuurmans:
Efficient global optimization for exponential family PCA and low-rank matrix factorization. Allerton 2008: 1100-1107 - [c75]Yuxi Li, Dale Schuurmans:
Policy Iteration for Learning an Exercise Policy for American Options. EWRL 2008: 165-178 - 2007
- [c74]Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans:
Automatic Gait Optimization with Gaussian Process Regression. IJCAI 2007: 944-949 - [c73]Qin Iris Wang, Dekang Lin, Dale Schuurmans:
Simple Training of Dependency Parsers via Structured Boosting. IJCAI 2007: 1756-1762 - [c72]Yuhong Guo, Dale Schuurmans:
Discriminative Batch Mode Active Learning. NIPS 2007: 593-600 - [c71]Yuhong Guo, Dale Schuurmans:
Convex Relaxations of Latent Variable Training. NIPS 2007: 601-608 - [c70]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Stable Dual Dynamic Programming. NIPS 2007: 1569-1576 - [c69]Yuhong Guo, Dale Schuurmans:
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization. RECOMB-CG 2007: 83-95 - 2006
- [j13]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif. Intell. 170(8-9): 686-713 (2006) - [j12]Tibério S. Caetano, Terry Caelli, Dale Schuurmans, Dante Augusto Couto Barone:
Graphical Models and Point Pattern Matching. IEEE Trans. Pattern Anal. Mach. Intell. 28(10): 1646-1663 (2006) - [c68]Linli Xu, Koby Crammer, Dale Schuurmans:
Robust Support Vector Machine Training via Convex Outlier Ablation. AAAI 2006: 536-542 - [c67]Tao Wang, Pascal Poupart, Michael H. Bowling, Dale Schuurmans:
Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. AAAI 2006: 1245-1252 - [c66]Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans:
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. ACL 2006 - [c65]Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli, S. V. N. Vishwanathan:
An Online Discriminative Approach to Background Subtraction. AVSS 2006: 2 - [c64]Qin Iris Wang, Colin Cherry, Daniel J. Lizotte, Dale Schuurmans:
Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization. CoNLL 2006: 21-28 - [c63]Shaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans:
Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. ICGI 2006: 97-111 - [c62]Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans:
Discriminative unsupervised learning of structured predictors. ICML 2006: 1057-1064 - [c61]Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli:
implicit Online Learning with Kernels. NIPS 2006: 249-256 - [c60]Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner:
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. NIPS 2006: 793-800 - [c59]Jiayuan Huang, Tingshao Zhu, Dale Schuurmans:
Web Communities Identification from Random Walks. PKDD 2006: 187-198 - [c58]Jiayuan Huang, Tingshao Zhu, Russell Greiner, Dengyong Zhou, Dale Schuurmans:
Information Marginalization on Subgraphs. PKDD 2006: 199-210 - [c57]Yuhong Guo, Dale Schuurmans:
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. UAI 2006 - [p1]Dale Schuurmans, Finnegan Southey, Dana F. Wilkinson, Yuhong Guo:
Metric-Based Approaches for Semi-Supervised Regression and Classification. Semi-Supervised Learning 2006: 420-451 - 2005
- [j11]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Combining Statistical Language Models via the Latent Maximum Entropy Principle. Mach. Learn. 60(1-3): 229-250 (2005) - [c56]Linli Xu, Dale Schuurmans:
Unsupervised and Semi-Supervised Multi-Class Support Vector Machines. AAAI 2005: 904-910 - [c55]Ali Ghodsi, Jiayuan Huang, Finnegan Southey, Dale Schuurmans:
Tangent-Corrected Embedding. CVPR (1) 2005: 518-525 - [c54]Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang:
Variational Bayesian image modelling. ICML 2005: 129-136 - [c53]Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ICML 2005: 948-955 - [c52]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Bayesian sparse sampling for on-line reward optimization. ICML 2005: 956-963 - [c51]Yuhong Guo, Russell Greiner, Dale Schuurmans:
Learning Coordination Classifiers. IJCAI 2005: 714-721 - [c50]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Regret-based Utility Elicitation in Constraint-based Decision Problems. IJCAI 2005: 929-934 - [c49]Qin Iris Wang, Dale Schuurmans, Dekang Lin:
Strictly Lexical Dependency Parsing. IWPT 2005: 152-159 - [c48]Yuhong Guo, Dana F. Wilkinson, Dale Schuurmans:
Maximum Margin Bayesian Networks. UAI 2005: 233-242 - 2004
- [j10]Fuchun Peng, Dale Schuurmans, Shaojun Wang:
Augmenting Naive Bayes Classifiers with Statistical Language Models. Inf. Retr. 7(3-4): 317-345 (2004) - [j9]Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans:
Dynamic Web log session identification with statistical language models. J. Assoc. Inf. Sci. Technol. 55(14): 1290-1303 (2004) - [j8]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Learning mixture models with the regularized latent maximum entropy principle. IEEE Trans. Neural Networks 15(4): 903-916 (2004) - [c47]Ali Ghodsi, Jiayuan Huang, Dale Schuurmans:
Transformation-Invariant Embedding for Image Analysis. ECCV (4) 2004: 519-530 - [c46]Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ISCSLP 2004: 305-308 - [c45]Linli Xu, James Neufeld, Bryce Larson, Dale Schuurmans:
Maximum Margin Clustering. NIPS 2004: 1537-1544 - 2003
- [j7]Xiangji Huang, Fuchun Peng, Dale Schuurmans, Nick Cercone, Stephen E. Robertson:
Applying Machine Learning to Text Segmentation for Information Retrieval. Inf. Retr. 6(3-4): 333-362 (2003) - [j6]Ali Ghodsi, Dale Schuurmans:
Automatic basis selection techniques for RBF networks. Neural Networks 16(5-6): 809-816 (2003) - [c44]Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, Nick Cercone:
Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. AI 2003: 237-251 - [c43]Fletcher Lu, Dale Schuurmans:
Model-Based Least-Squares Policy Evaluation. AI 2003: 342-352 - [c42]Shaojun Wang, Dale Schuurmans, Fuchun Peng:
Latent Maximum Entropy Approach for Semantic N-gram Language Modeling. AISTATS 2003: 316-322 - [c41]Shaojun Wang, Dale Schuurmans:
Learning Continuous Latent Variable Models with Bregman Divergences. ALT 2003: 190-204 - [c40]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Constraint-Based Optimization with the Minimax Decision Criterion. CP 2003: 168-182 - [c39]Feng Jiao, Stan Z. Li, Heung-Yeung Shum, Dale Schuurmans:
Face Alignment Using Statistical Models and Wavelet Features. CVPR (1) 2003: 321-327 - [c38]Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang:
Language Independent Authorship Attribution with Character Level N-Grams. EACL 2003: 267-274 - [c37]Fuchun Peng, Dale Schuurmans:
Combining Naive Bayes and n-Gram Language Models for Text Classification. ECIR 2003: 335-350 - [c36]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Semantic n-gram language modeling with the latent maximum entropy principle. ICASSP (1) 2003: 376-379 - [c35]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Learning Mixture Models with the Latent Maximum Entropy Principle. ICML 2003: 784-791 - [c34]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Shaojun Wang:
Text classification in Asian languages without word segmentation. IRAL 2003: 41-48 - [c33]Fuchun Peng, Dale Schuurmans, Shaojun Wang:
Language and Task Independent Text Categorization with Simple Language Models. HLT-NAACL 2003 - [c32]Fletcher Lu, Dale Schuurmans:
Monte Carlo Matrix Inversion Policy Evaluation. UAI 2003: 386-393 - [c31]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Boltzmann Machine Learning with the Latent Maximum Entropy Principle. UAI 2003: 567-574 - 2002
- [j5]Yoshua Bengio, Dale Schuurmans:
Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination. Mach. Learn. 48(1-3): 5-7 (2002) - [j4]Dale Schuurmans, Finnegan Southey:
Metric-Based Methods for Adaptive Model Selection and Regularization. Mach. Learn. 48(1-3): 51-84 (2002) - [c30]Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans:
Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002: 132-139 - [c29]Relu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin:
Greedy Linear Value-Approximation for Factored Markov Decision Processes. AAAI/IAAI 2002: 285-291 - [c28]Pascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans:
Piecewise Linear Value Function Approximation for Factored MDPs. AAAI/IAAI 2002: 292-299 - [c27]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone:
Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR. COLING 2002 - [c26]Carlos Guestrin, Relu Patrascu, Dale Schuurmans:
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs. ICML 2002: 235-242 - [c25]Fletcher Lu, Relu Patrascu, Dale Schuurmans:
Investigating the Maximum Likelihood Alternative to TD(lambda). ICML 2002: 403-410 - [c24]Finnegan Southey, Dale Schuurmans, Ali Ghodsi:
Regularized Greedy Importance Sampling. NIPS 2002: 753-760 - [c23]Xiangji Huang, Fuchun Peng, Dale Schuurmans, Nick Cercone:
Waterloo at NTCIR-3: Using Self-supervised Word Segmentation. NTCIR 2002 - [c22]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone, Stephen E. Robertson:
Using self-supervised word segmentation in Chinese information retrieval. SIGIR 2002: 349-350 - 2001
- [j3]Dale Schuurmans, Finnegan Southey:
Local search characteristics of incomplete SAT procedures. Artif. Intell. 132(2): 121-150 (2001) - [j2]Adam J. Grove, Nick Littlestone, Dale Schuurmans:
General Convergence Results for Linear Discriminant Updates. Mach. Learn. 43(3): 173-210 (2001) - [c21]Fuchun Peng, Dale Schuurmans:
Self-Supervised Chinese Word Segmentation. IDA 2001: 238-247 - [c20]Dale Schuurmans, Finnegan Southey, Robert C. Holte:
The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming. IJCAI 2001: 334-341 - [c19]Dale Schuurmans, Relu Patrascu:
Direct value-approximation for factored MDPs. NIPS 2001: 1579-1586 - [c18]Fuchun Peng, Dale Schuurmans:
A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling. NLPRS 2001: 145-152 - [c17]Fuchun Peng, Dale Schuurmans:
A Hierarchical EM Approach to Word Segmentation. NLPRS 2001: 475-480 - 2000
- [c16]Dale Schuurmans, Finnegan Southey:
Local Search Characteristics of Incomplete SAT Procedures. AAAI/IAAI 2000: 297-302 - [c15]Dale Schuurmans, Finnegan Southey:
An Adaptive Regularization Criterion for Supervised Learning. ICML 2000: 847-854 - [c14]Dale Schuurmans, Finnegan Southey:
Monte Carlo inference via greedy importance sampling. UAI 2000: 523-532
1990 – 1999
- 1999
- [c13]Dale Schuurmans, Lloyd G. Greenwald:
Efficient exploration for optimizing immediate reward. AAAI/IAAI 1999: 385-392 - [c12]Dale Schuurmans:
Greedy Importance Sampling. NIPS 1999: 596-602 - 1998
- [c11]Adam J. Grove, Dale Schuurmans:
Boosting in the Limit: Maximizing the Margin of Learned Ensembles. AAAI/IAAI 1998: 692-699 - 1997
- [j1]Dale Schuurmans:
Characterizing Rational Versus Exponential learning Curves. J. Comput. Syst. Sci. 55(1): 140-160 (1997) - [c10]Dale Schuurmans:
A New Metric-Based Approach to Model Selection. AAAI/IAAI 1997: 552-558 - [c9]Adam J. Grove, Nick Littlestone, Dale Schuurmans:
General Convergence Results for Linear Discriminant Updates. COLT 1997: 171-183 - [c8]Dale Schuurmans, Lyle H. Ungar, Dean P. Foster:
Characterizing the generalization performance of model selection strategies. ICML 1997: 340-348 - [c7]Russell Greiner, Adam J. Grove, Dale Schuurmans:
Learning Bayesian Nets that Perform Well. UAI 1997: 198-207 - 1996
- [b1]Dale Schuurmans:
Effective classification learning. University of Toronto, Canada, 1996 - 1995
- [c6]Dale Schuurmans, Russell Greiner:
Sequential PAC Learning. COLT 1995: 377-384 - [c5]Dale Schuurmans:
Characterizing rational versus exponential learning curves. EuroCOLT 1995: 272-286 - [c4]Dale Schuurmans, Russell Greiner:
Practical PAC Learning. IJCAI 1995: 1169-1177 - 1992
- [c3]Russell Greiner, Dale Schuurmans:
Learning an Optimally Accurate Representation System. ECAI Workshop on Knowledge Representation and Reasoning 1992: 145-159 - [c2]Russell Greiner, Dale Schuurmans:
Learning Useful Horn Approximations. KR 1992: 383-392
1980 – 1989
- 1989
- [c1]Dale Schuurmans, Jonathan Schaeffer:
Representational Difficulties with Classifier Systems. ICGA 1989: 328-333
Coauthor Index
[c207] [c205] [c203] [i103] [i101] [i99] [i98] [i95] [i91] [c202] [c201] [c198] [c197] [c196] [c195] [c191] [c188] [c186] [c184] [i87] [i86] [i83] [i79] [c183] [c182] [c181] [c180] [c179] [c177] [c176] [c174] [c170] [i76] [i70] [i68] [i67] [i64] [i61] [i59] [c167] [c166] [c164] [c163] [c162] [i56] [i54] [i52] [i51] [i50] [i49] [i48] [c161] [c159] [c156] [c154] [c152] [c151] [c149] [c148] [i47] [i45] [i43] [i40] [i38] [i35] [i34] [i33] [c147] [c137] [i29] [i25] [i20]
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