Search Results for author: Vincent Liu

Found 14 papers, 2 papers with code

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments

no code implementations23 Feb 2023 Vincent Liu, Yash Chandak, Philip Thomas, Martha White

In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting.

Multi-Armed Bandits regression +1

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving

1 code implementation22 Feb 2023 Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.

DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning

1 code implementation23 Nov 2021 Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah Goodman

Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing.

Self-Supervised Learning

Measuring and mitigating interference in reinforcement learning

no code implementations1 Jan 2021 Vincent Liu, Adam M White, Hengshuai Yao, Martha White

Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it.

reinforcement-learning Reinforcement Learning (RL) +1

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model

no code implementations2 Jun 2020 Alejandro Schuler, Aashish Bhardwaj, Vincent Liu

Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices.

Training Recurrent Neural Networks Online by Learning Explicit State Variables

no code implementations ICLR 2020 Somjit Nath, Vincent Liu, Alan Chan, Xin Li, Adam White, Martha White

Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in partially observable problems.

Incrementally Learning Functions of the Return

no code implementations5 Jul 2019 Brendan Bennett, Wesley Chung, Muhammad Zaheer, Vincent Liu

Temporal difference methods enable efficient estimation of value functions in reinforcement learning in an incremental fashion, and are of broader interest because they correspond learning as observed in biological systems.

Reinforcement Learning (RL)

Recurrent Control Nets for Deep Reinforcement Learning

no code implementations6 Jan 2019 Vincent Liu, Ademi Adeniji, Nathaniel Lee, Jason Zhao, Mario Srouji

Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input.

reinforcement-learning Reinforcement Learning (RL)

Attribute-aware Collaborative Filtering: Survey and Classification

no code implementations20 Oct 2018 Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e. g. age), items (e. g. price), or even ratings (e. g. rating time).

Classification Collaborative Filtering +1

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