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Found 17 papers, 2 papers with code
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
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
Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing.
Ranked #1 on
Self-Supervised Learning
on DABS
Attribute-aware Collaborative Filtering: Survey and Classification
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).
The Utility of Sparse Representations for Control in Reinforcement Learning
We investigate sparse representations for control in reinforcement learning.
Recurrent Control Nets for Deep Reinforcement Learning
Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input.
Incrementally Learning Functions of the Return
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.
Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model
Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices.
Towards a practical measure of interference for reinforcement learning
In this work, we provide a definition of interference for control in reinforcement learning.
Training Recurrent Neural Networks Online by Learning Explicit State Variables
Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in partially observable problems.
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning
Offline reinforcement learning -- learning a policy from a batch of data -- is known to be hard for general MDPs.
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned by deep reinforcement learning systems.
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters.
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting.
Measuring and Mitigating Interference in Reinforcement Learning
Lastly, we outline a class of algorithms which we call online-aware that are designed to mitigate interference, and show they do reduce interference according to our measure and that they improve stability and performance in several classic control environments.
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
As a result, no OPS method can be more sample efficient than OPE in the worst case.
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
This work delves into the expanding role of large language models (LLMs) in generating artificial data.
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
