Search Results for author: Xingyou Song

Found 22 papers, 8 papers with code

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

no code implementations11 Jan 2022 Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents.

AutoML Meta-Learning +1

Differentiable Architecture Search for Reinforcement Learning

1 code implementation4 Jun 2021 Yingjie Miao, Xingyou Song, John D. Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust

In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL?

Neural Architecture Search reinforcement-learning

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

1 code implementation4 Jun 2021 Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller

Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops.

Sub-Linear Memory: How to Make Performers SLiM

2 code implementations NeurIPS 2021 Valerii Likhosherstov, Krzysztof Choromanski, Jared Davis, Xingyou Song, Adrian Weller

Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation.

Ode to an ODE

no code implementations NeurIPS 2020 Krzysztof M. Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

Rethinking Attention with Performers

11 code implementations ICLR 2021 Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller

We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.

Ranked #15 on Image Generation on ImageNet 64x64 (Bits per dim metric)

Image Generation

An Ode to an ODE

no code implementations NeurIPS 2020 Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

UFO-BLO: Unbiased First-Order Bilevel Optimization

no code implementations5 Jun 2020 Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller

Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning.

Adversarial Robustness Bilevel Optimization +4

Robotic Table Tennis with Model-Free Reinforcement Learning

no code implementations31 Mar 2020 Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly

We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz.

reinforcement-learning

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

no code implementations2 Mar 2020 Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.

Legged Robots Meta-Learning

Observational Overfitting in Reinforcement Learning

no code implementations ICLR 2020 Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP).

reinforcement-learning

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

no code implementations ICLR 2020 Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi Zhang

Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.

Reinforcement Learning with Chromatic Networks

no code implementations25 Sep 2019 Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang

We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.

Neural Architecture Search reinforcement-learning

ES-MAML: Simple Hessian-Free Meta Learning

1 code implementation ICLR 2020 Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang

We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).

Meta-Learning

Reinforcement Learning with Chromatic Networks for Compact Architecture Search

no code implementations10 Jul 2019 Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang

We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.

Combinatorial Optimization Neural Architecture Search +1

An Empirical Study on Hyperparameters and their Interdependence for RL Generalization

no code implementations2 Jun 2019 Xingyou Song, Yilun Du, Jacob Jackson

Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains.

reinforcement-learning

The Principle of Unchanged Optimality in Reinforcement Learning Generalization

no code implementations2 Jun 2019 Alex Irpan, Xingyou Song

Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments, then benchmarking algorithms and model architectures on those environments.

reinforcement-learning

Sentiment Predictability for Stocks

1 code implementation15 Dec 2017 Jordan Prosky, Xingyou Song, Andrew Tan, Michael Zhao

In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.

Event Extraction Sentiment Analysis +1

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