Xingyou Song | DeepAI

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Featured Co-authors

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Arnaud Doucet
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Nando de Freitas
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Krzysztof Choromanski
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Aldo Pacchiano
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∙ 07/27/2022

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization

Vizier is the de-facto blackbox and hyperparameter optimization service ...

0 Xingyou Song, et al. ∙

∙ 05/26/2022

Towards Learning Universal Hyperparameter Optimizers with Transformers

Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...

9 Yutian Chen, et al. ∙

∙ 01/11/2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

The combination of Reinforcement Learning (RL) with deep learning has le...

38 Jack Parker-Holder, et al. ∙

∙ 06/04/2021

Debiasing a First-order Heuristic for Approximate Bi-level Optimization

Approximate bi-level optimization (ABLO) consists of (outer-level) optim...

0 Valerii Likhosherstov, et al. ∙

∙ 06/04/2021

RL-DARTS: Differentiable Architecture Search for Reinforcement Learning

We introduce RL-DARTS, one of the first applications of Differentiable A...

0 Yingjie Miao, et al. ∙

∙ 02/08/2021

Unlocking Pixels for Reinforcement Learning via Implicit Attention

There has recently been significant interest in training reinforcement l...

1 Krzysztof Choromanski, et al. ∙

∙ 01/19/2021

ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning

We introduce ES-ENAS, a simple neural architecture search (NAS) algorith...

16 Xingyou Song, et al. ∙

∙ 12/21/2020

Sub-Linear Memory: How to Make Performers SLiM

The Transformer architecture has revolutionized deep learning on sequent...

0 Valerii Likhosherstov, et al. ∙

∙ 09/30/2020

Rethinking Attention with Performers

We introduce Performers, Transformer architectures which can estimate re...

5 Krzysztof Choromanski, et al. ∙

∙ 06/19/2020

An Ode to an ODE

We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...

18 Krzysztof Choromanski, et al. ∙

∙ 06/05/2020

UFO-BLO: Unbiased First-Order Bilevel Optimization

Bilevel optimization (BLO) is a popular approach with many applications ...

7 Valerii Likhosherstov, et al. ∙

∙ 06/05/2020

Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers

Transformer models have achieved state-of-the-art results across a diver...

10 Krzysztof Choromanski, et al. ∙

∙ 03/31/2020

Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capabl...

18 Wenbo Gao, et al. ∙

∙ 03/30/2020

Stochastic Flows and Geometric Optimization on the Orthogonal Group

We present a new class of stochastic, geometrically-driven optimization ...

2 Krzysztof Choromanski, et al. ∙

∙ 03/02/2020

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

Learning adaptable policies is crucial for robots to operate autonomousl...

6 Xingyou Song, et al. ∙

∙ 12/06/2019

Observational Overfitting in Reinforcement Learning

A major component of overfitting in model-free reinforcement learning (R...

21 Xingyou Song, et al. ∙

∙ 11/14/2019

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Zeroth-order optimization is the process of minimizing an objective f(x)...

0 Daniel Golovin, et al. ∙

∙ 09/25/2019

ES-MAML: Simple Hessian-Free Meta Learning

We introduce ES-MAML, a new framework for solving the model agnostic met...

16 Xingyou Song, et al. ∙

∙ 07/10/2019

Reinforcement Learning with Chromatic Networks

We present a new algorithm for finding compact neural networks encoding ...

6 Xingyou Song, et al. ∙

∙ 06/02/2019

An Empirical Study on Hyperparameters and their Interdependence for RL Generalization

Recent results in Reinforcement Learning (RL) have shown that agents wit...

0 Xingyou Song, et al. ∙

∙ 06/02/2019

The Principle of Unchanged Optimality in Reinforcement Learning Generalization

Several recent papers have examined generalization in reinforcement lear...

2 Alex Irpan, et al. ∙

∙ 12/15/2017

Sentiment Predictability for Stocks

In this work, we present our findings and experiments for stock-market p...

0 Jordan Prosky, et al. ∙