no code implementations • 1 Feb 2024 • Andrei Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot
Machine unlearning is a desirable operation as models get increasingly deployed on data with unknown provenance.
no code implementations • 7 Dec 2023 • Michael R. Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, Jimmy Ba
This paper studies using foundational large language models (LLMs) to make decisions during hyperparameter optimization (HPO).
1 code implementation • 12 Apr 2023 • Silviu Pitis, Michael R. Zhang, Andrew Wang, Jimmy Ba
Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training.
no code implementations • 7 Dec 2022 • Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Grosse
Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications.
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.
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.
1 code implementation • 22 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.
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.
1 code implementation • 27 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.
19 code implementations • NeurIPS 2019 • Michael R. Zhang, James Lucas, Geoffrey Hinton, Jimmy Ba
The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms.