D. Schuurmans

发表

The Value Function Polytope in Reinforcement Learning pdf

Nicolas Le Roux, Marc G. Bellemare, Dale Schuurmans, 2019, ICML.

Planning and Learning with Stochastic Action Sets

Craig Boutilier, Avinatan Hassidim, Yishay Mansour, 2018, IJCAI.

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

Quoc V. Le, Xiaodan Song, Mingxing Tan, 2020, ICML.

ALGAE: Policy Gradient from Arbitrary Experience

D. Schuurmans, Ofir Nachum, Ilya Kostrikov, 2019 .

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs

Jure Leskovec, Xinyun Chen, Denny Zhou, 2021, ICML.

Invertible Convolutional Flow

Jascha Sohl-Dickstein, Laurent Dinh, Dale Schuurmans, 2019, NeurIPS.

Adaptive Monte Carlo via Bandit Allocation pdf

Csaba Szepesvári, Dale Schuurmans, András György, 2014, ICML.

Escaping the Gravitational Pull of Softmax

Csaba Szepesvári, Dale Schuurmans, Bo Dai, 2020, NeurIPS.

On the Global Convergence Rates of Softmax Policy Gradient Methods

Dale Schuurmans, Chenjun Xiao, Jincheng Mei, 2020, ICML.

A pilot study: Can heart rate variability (HRV) be determined using short-term photoplethysmograms?

D. Schuurmans, M. Elgendi, S. Dokos, 2016 .

Regularizers versus losses for nonlinear dimensionality reduction

Ryan Kiros, Xinhua Zhang, D. Schuurmans, 2012, ICML 2012.

Reinforcement Teaching

Matthew E. Taylor, Jun Luo, D. Schuurmans, 2022, 2204.11897.

TERIZATION IN RECURSIVE VALUE ESTIMATION

D. Schuurmans, Chris Harris, Jincheng Mei, 2022 .

An Optimistic Perspective on Offline Deep Reinforcement Learning

Mohammad Norouzi, Rishabh Agarwal, Dale Schuurmans, 2020, International Conference on Machine Learning.

An Optimistic Perspective on Offline Reinforcement Learning

Rishabh Agarwal, D. Schuurmans, Mohammad Norouzi, 2019, ICML.

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

D. Schuurmans, M. Elgendi, R. Zemp, 2016, Scientific Reports.

Reinforcement Teaching

Matthew E. Taylor, Jun Luo, D. Schuurmans, 2022, ArXiv.

A Parametric Class of Approximate Gradient Updates for Policy Optimization

Saurabh Kumar, D. Schuurmans, Junfeng Wen, 2022, ICML.

On the Global Convergence Rates of Softmax Policy Gradient Methods

Csaba Szepesvari, D. Schuurmans, Jincheng Mei, 2020, ICML.

Chain of Thought Imitation with Procedure Cloning

P. Abbeel, D. Schuurmans, Ofir Nachum, 2022, NeurIPS.

Rationale-Augmented Ensembles in Language Models

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

Making Linear MDPs Practical via Contrastive Representation Learning

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2022, ICML.

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization

D. Schuurmans, Bo Dai, H. Dai, 2022, ICML.

Spectral Decomposition Representation for Reinforcement Learning

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2022, ArXiv.

Discrete Langevin Sampler via Wasserstein Gradient Flow

D. Schuurmans, Bo Dai, Haomin Zhou, 2022, ArXiv.

Leveraging Non-uniformity in First-order Non-convex Optimization

D. Schuurmans, Bo Dai, Jincheng Mei, 2021, ICML.

Self-Consistency Improves Chain of Thought Reasoning in Language Models

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

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

D. Schuurmans, O. Bousquet, Xuezhi Wang, 2022, International Conference on Learning Representations.

Dichotomy of Control: Separating What You Can Control from What You Cannot

P. Abbeel, D. Schuurmans, Ofir Nachum, 2022, ICLR.

On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games

D. Schuurmans, Bo Dai, Jincheng Mei, 2022, NeurIPS.

Score-based Continuous-time Discrete Diffusion Models

D. Schuurmans, Bo Dai, H. Dai, 2022, ArXiv.

Learning to Optimize with Stochastic Dominance Constraints

D. Schuurmans, Bo Dai, H. Dai, 2022, ArXiv.

What learning algorithm is in-context learning? Investigations with linear models

D. Schuurmans, Jacob Andreas, Denny Zhou, 2022, 2211.15661.

Latent Variable Representation for Reinforcement Learning

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2022, 2212.08765.

Off-Policy Evaluation via the Regularized Lagrangian

Lihong Li, D. Schuurmans, Ofir Nachum, 2020, NeurIPS.

A Simple Decentralized Cross-Entropy Method

D. Schuurmans, Martin Jägersand, Jun Luo, 2022, NeurIPS.

TEMPERA: Test-Time Prompting via Reinforcement Learning

D. Schuurmans, Xuezhi Wang, Joseph Gonzalez, 2022, ArXiv.

The Role of Baselines in Policy Gradient Optimization

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2023, NeurIPS.

On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems

D. Schuurmans, Jincheng Mei, Bo Dai, 2022, ArXiv.

Learning Universal Policies via Text-Guided Video Generation

P. Abbeel, J. Tenenbaum, D. Schuurmans, 2023, NeurIPS.

Attention that does not Explain Away pdf

Radu Soricut, Nan Ding, Dale Schuurmans, 2020, ArXiv.

Self-Consistency Improves Chain of Thought Reasoning in Language Models

D. Schuurmans, Xuezhi Wang, Denny Zhou, 2022, ICLR.

Detection of Heart Sounds in Children with and without Pulmonary Arterial Hypertension―Daubechies Wavelets Approach

Dale Schuurmans, Roger Zemp, Mohamed Elgendi, 2015, PloS one.

Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions

Derek Abbott, Dale Schuurmans, Mohamed Elgendi, 2013, PloS one.

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL pdf

Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu, 2020, ICML.

Score-based Continuous-time Discrete Diffusion Models

D. Schuurmans, Bo Dai, H. Dai, 2022, ICLR.

Learning to Optimize with Stochastic Dominance Constraints

D. Schuurmans, Bo Dai, H. Dai, 2022, AISTATS.

Foundation Models for Decision Making: Problems, Methods, and Opportunities

P. Abbeel, D. Schuurmans, Ofir Nachum, 2023, ArXiv.

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration pdf

Rishabh Singh, Bo Dai, Charles Sutton, 2020, NeurIPS.

Latent Variable Representation for Reinforcement Learning

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2022, ICLR.

Variational Rejection Sampling pdf

Stefano Ermon, Dale Schuurmans, Miguel Lázaro-Gredilla, 2018, AISTATS.

Energy-Based Processes for Exchangeable Data

D. Schuurmans, Bo Dai, H. Dai, 2020, International Conference on Machine Learning.

Spectral Decomposition Representation for Reinforcement Learning

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2022, ICLR.

Metric-Based Approaches for Semi-Supervised Regression and Classification

Finnegan Southey, Dana F. Wilkinson, Yuhong Guo, 2006, Semi-Supervised Learning.

Scalable Deep Generative Modeling for Sparse Graphs

Bo Dai, Dale Schuurmans, Hanjun Dai, 2020, ICML.

What learning algorithm is in-context learning? Investigations with linear models

D. Schuurmans, Jacob Andreas, Denny Zhou, 2022, ArXiv.

Discrete Langevin Sampler via Wasserstein Gradient Flow

D. Schuurmans, Bo Dai, Haomin Zhou, 2022, AISTATS.

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

D. Schuurmans, O. Bousquet, Xuezhi Wang, 2022, ICLR.

Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off

D. Schuurmans, Masood Dehghan, Johannes Kirschner, 2022, NeurIPS.

Optimization Issues in KL-Constrained Approximate Policy Iteration pdf

Csaba Szepesvári, Yasin Abbasi-Yadkori, Dale Schuurmans, 2021, ArXiv.

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making

Minmin Chen, Dale Schuurmans, Christopher K. Harris, 2019, NeurIPS.

Gradient-Free Structured Pruning with Unlabeled Data

D. Schuurmans, H. Dai, Azade Nova, 2023, ArXiv.

What learning algorithm is in-context learning? Investigations with linear models

D. Schuurmans, Jacob Andreas, Denny Zhou, 2022, ICLR.

Combiner: Full Attention Transformer with Sparse Computation Cost pdf

Jure Leskovec, Dale Schuurmans, Hongyu Ren, 2021, NeurIPS.

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs pdf

Jure Leskovec, Xinyun Chen, Denny Zhou, 2021, KDD.

AlgaeDICE: Policy Gradient from Arbitrary Experience pdf

Ilya Kostrikov, Bo Dai, Lihong Li, 2019, ArXiv.

CoinDICE: Off-Policy Confidence Interval Estimation pdf

Csaba Szepesvári, Bo Dai, Lihong Li, 2020, NeurIPS.

GenDICE: Generalized Offline Estimation of Stationary Values pdf

Bo Dai, Dale Schuurmans, Ruiyi Zhang, 2020, ICLR.

Probabilistic Adaptation of Text-to-Video Models

P. Abbeel, J. Tenenbaum, D. Schuurmans, 2023, ArXiv.

Domain Aggregation Networks for Multi-Source Domain Adaptation pdf

Russell Greiner, Dale Schuurmans, Junfeng Wen, 2019, ICML.

Neural Stochastic Dual Dynamic Programming pdf

Dale Schuurmans, Hanjun Dai, Yuan Xue, 2021, ICLR.

On the Optimality of Batch Policy Optimization Algorithms

Tor Lattimore, Dale Schuurmans, Chenjun Xiao, 2021, ICML.

On Principled Entropy Exploration in Policy Optimization

Martin Müller, Dale Schuurmans, Ruitong Huang, 2019, IJCAI.

Offline Policy Selection under Uncertainty pdf

Bo Dai, George Tucker, Ofir Nachum, 2020, AISTATS.

Batch Stationary Distribution Estimation pdf

Bo Dai, Dale Schuurmans, Junfeng Wen, 2020, ICML.

Joint Attention for Multi-Agent Coordination and Social Learning

D. Schuurmans, Dennis Lee, Aleksandra Faust, 2021, ArXiv.

W HAT LEARNING ALGORITHM IS IN - CONTEXT LEARN - ING ? I NVESTIGATIONS WITH LINEAR MODELS

D. Schuurmans, Jacob Andreas, Denny Zhou, 2023 .

Characterizing the Gap Between Actor-Critic and Policy Gradient pdf

Dale Schuurmans, Junfeng Wen, Ramki Gummadi, 2021, ICML.

Gradient-Free Structured Pruning with Unlabeled Data

D. Schuurmans, H. Dai, Azade Nova, 2023, ICML.

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks pdf

Qiang Liu, Quoc V. Le, Denny Zhou, 2020, ICML.

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning pdf

Yifan Wu, Chen Ma, Dale Schuurmans, 2019, ArXiv.

Understanding and Leveraging Overparameterization in Recursive Value Estimation

D. Schuurmans, Chris Harris, Bo Dai, 2022, ICLR.

S PECTRAL D ECOMPOSITION R EPRESENTATION FOR R EINFORCEMENT L EARNING

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2023 .

L ATENT V ARIABLE R EPRESENTATION FOR R EINFORCE - MENT L EARNING

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2023 .

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2021, AISTATS.

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2021, 2106.09973.

A maximum-entropy approach to off-policy evaluation in average-reward MDPs pdf

Dilan Görür, Nevena Lazic, Nir Levine, 2020, NeurIPS.

Understanding the Effect of Stochasticity in Policy Optimization pdf

Dale Schuurmans, Jincheng Mei, Csaba Szepesvari, 2021, NeurIPS.

TEMPERA: Test-Time Prompting via Reinforcement Learning

D. Schuurmans, Joseph E. Gonzalez, Denny Zhou, 2022, ICLR.

TEMPERA: T EST -T IME P ROMPT E DITING VIA R EIN - FORCEMENT L EARNING

D. Schuurmans, Joseph E. Gonzalez, Denny Zhou, 2023 .

Maximum Entropy Monte-Carlo Planning

Dale Schuurmans, Ruitong Huang, Chenjun Xiao, 2019, NeurIPS.

ConQUR: Mitigating Delusional Bias in Deep Q-learning

Craig Boutilier, Dale Schuurmans, Tyler Lu, 2020, ICML.

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2022, AISTATS.

Learning Interactive Real-World Simulators

D. Schuurmans, Jonathan Tompson, Yilun Du, 2023, ArXiv.

Large Language Models can Learn Rules

D. Schuurmans, Zhaocheng Zhu, Xinyun Chen, 2023, ArXiv.

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu, 2020, ICML.

CoinDICE: Off-Policy Conﬁdence Interval Estimation

D. Schuurmans, Ofir Nachum, Yinlam Chow, 2022 .

Ordering-based Conditions for Global Convergence of Policy Gradient Methods

D. Schuurmans, Jincheng Mei, Google Deepmind, 2022 .

Efficient Reinforcement Learning from Partial Observability

D. Schuurmans, Chenjun Xiao, Bo Dai, 2023, 2311.12244.

Energy-based Predictive Representations for Partially Observed Reinforcement Learning

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2023, UAI.

Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning

D. Schuurmans, Chenjun Xiao, Bo Dai, 2023, ArXiv.

Revisiting Sampling for Combinatorial Optimization

D. Schuurmans, H. Dai, Haoran Sun, 2023, ICML.

Sources of Hallucination by Large Language Models on Inference Tasks Anonymous EMNLP

Dat Ba Nguyen, Naman Goyal, D. Schuurmans, 2022 .

Variational Inference for Deep Probabilistic Canonical Correlation Analysis pdf

Dale Schuurmans, Mahdi Karami, D. Schuurmans, 2020, ArXiv.