Search Results for author: Michael R. Zhang

Found 6 papers, 5 papers with code

Learning Domain Invariant Representations in Goal-conditioned Block MDPs

1 code implementation NeurIPS 2021 Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba

These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents.

Domain Generalization

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

no code implementations ICLR 2021 Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics.

Continuous Control Data Augmentation

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes

1 code implementation22 Apr 2021 James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard Zemel, Roger Grosse

Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective.

Benchmarks for Deep Off-Policy Evaluation

3 code implementations ICLR 2021 Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.

Continuous Control Decision Making +1

Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes

1 code implementation27 Jan 2020 Silviu Pitis, Michael R. Zhang

Instead, we assume that votes are independent but not necessarily identically distributed and that our ensembling algorithm has access to certain auxiliary information related to the underlying model governing the noise in each vote.

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