Search Results for author: Michael R. Zhang

Found 10 papers, 6 papers with code

Unlearnable Algorithms for In-context Learning

no code implementations1 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.

In-Context Learning Language Modelling +2

Using Large Language Models for Hyperparameter Optimization

no code implementations7 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).

Bayesian Optimization Decision Making +1

Boosted Prompt Ensembles for Large Language Models

1 code implementation12 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.

GSM8K Language Modelling

Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve

no code implementations7 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.

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 +1

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.

Benchmarking Continuous Control +3

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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