Search Results for author: Yutian Chen

Found 31 papers, 6 papers with code

Towards Learning Universal Hyperparameter Optimizers with Transformers

no code implementations26 May 2022 Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas

Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution.

Hyperparameter Optimization Meta-Learning

Importance Weighting Approach in Kernel Bayes' Rule

no code implementations5 Feb 2022 Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected posterior features, based on regression from kernel or neural net features of the observations.

Introducing Symmetries to Black Box Meta Reinforcement Learning

no code implementations22 Sep 2021 Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, Yutian Chen

We show that a recent successful meta RL approach that meta-learns an objective for backpropagation-based learning exhibits certain symmetries (specifically the reuse of the learning rule, and invariance to input and output permutations) that are not present in typical black-box meta RL systems.

Meta-Learning Meta Reinforcement Learning +1

Active Offline Policy Selection

1 code implementation NeurIPS 2021 Ksenia Konyushkova, Yutian Chen, Tom Le Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas

We use multiple benchmarks, including real-world robotics, with a large number of candidate policies to show that the proposed approach improves upon state-of-the-art OPE estimates and pure online policy evaluation.

On Instrumental Variable Regression for Deep Offline Policy Evaluation

1 code implementation21 May 2021 Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques.

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.

Continuous Control Decision Making +1

Regularized Behavior Value Estimation

no code implementations17 Mar 2021 Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, Nando de Freitas

Due to bootstrapping, these errors get amplified during training and can lead to divergence, thereby crippling learning.

Offline RL

Addressing Extrapolation Error in Deep Offline Reinforcement Learning

no code implementations1 Jan 2021 Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, Nando de Freitas

These errors can be compounded by bootstrapping when the function approximator overestimates, leading the value function to *grow unbounded*, thereby crippling learning.

Offline RL reinforcement-learning

Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis

no code implementations29 Dec 2020 Yutian Chen, Xiaowei Xu, Dewen Zeng, Yiyu Shi, Haiyun Yuan, Jian Zhuang, Yuhao Dong, Qianjun Jia, Meiping Huang

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.

Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech

no code implementations24 Nov 2020 Yiling Huang, Yutian Chen, Jason Pelecanos, Quan Wang

In recent years, Text-To-Speech (TTS) has been used as a data augmentation technique for speech recognition to help complement inadequacies in the training data.

Data Augmentation Speaker Recognition +1

Towards transformation-resilient provenance detection of digital media

no code implementations14 Nov 2020 Jamie Hayes, Krishnamurthy, Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.

Large-scale multilingual audio visual dubbing

no code implementations6 Nov 2020 Yi Yang, Brendan Shillingford, Yannis Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, Nando de Freitas

The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language.

Translation

Learning Deep Features in Instrumental Variable Regression

1 code implementation ICLR 2021 Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear.

Sequential Changepoint Detection in Neural Networks with Checkpoints

no code implementations6 Oct 2020 Michalis K. Titsias, Jakub Sygnowski, Yutian Chen

We introduce a framework for online changepoint detection and simultaneous model learning which is applicable to highly parametrized models, such as deep neural networks.

Continual Learning

Provenance detection through learning transformation-resilient watermarking

no code implementations25 Sep 2019 Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.

Modular Meta-Learning with Shrinkage

no code implementations NeurIPS 2020 Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew W. Hoffman, Nando de Freitas

Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.

Image Classification Meta-Learning +2

Bayesian Optimization in AlphaGo

no code implementations17 Dec 2018 Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas

During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times.

Herding as a Learning System with Edge-of-Chaos Dynamics

no code implementations9 Feb 2016 Yutian Chen, Max Welling

Herding defines a deterministic dynamical system at the edge of chaos.

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

no code implementations30 Jun 2015 Yutian Chen, Zoubin Ghahramani

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods.

Bayesian Inference Multi-Armed Bandits

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

1 code implementation7 Mar 2015 Yarin Gal, Yutian Chen, Zoubin Ghahramani

Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data.

Gaussian Processes Imputation +1

Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

no code implementations6 Nov 2014 Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani

Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable.

Probabilistic Programming

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior

no code implementations9 Aug 2014 Yutian Chen, Max Welling

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields.

Variational Gaussian Process State-Space Models

no code implementations NeurIPS 2014 Roger Frigola, Yutian Chen, Carl E. Rasmussen

State-space models have been successfully used for more than fifty years in different areas of science and engineering.

Gaussian Processes online learning +2

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

no code implementations19 Apr 2013 Anoop Korattikara, Yutian Chen, Max Welling

Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets?

Super-Samples from Kernel Herding

1 code implementation15 Mar 2012 Yutian Chen, Max Welling, Alex Smola

We extend the herding algorithm to continuous spaces by using the kernel trick.

On Herding and the Perceptron Cycling Theorem

no code implementations NeurIPS 2010 Andrew Gelfand, Yutian Chen, Laurens Maaten, Max Welling

The paper develops a connection between traditional perceptron algorithms and recently introduced herding algorithms.

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