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Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm. Our approach builds upon a recent connection of supervised learning and reinforcement learning (RL), and adapts an off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training. Different parameterization of the ``data reward'' function instantiates different manipulation schemes. We showcase data augmentation that learns a text transformation network, and data weighting that dynamically adapts the data sample importance. Experiments show the resulting algorithms significantly improve the image and text classification performance in low data regime and class-imbalance problems.
Author Information
Zhiting Hu (Carnegie Mellon University)
Bowen Tan (CMU)
Russ Salakhutdinov (Carnegie Mellon University)
Tom Mitchell (Carnegie Mellon University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
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2011 Poster: Large-Scale Category Structure Aware Image Categorization »
Bin Zhao · Li Fei-Fei · Eric Xing -
2011 Poster: Learning with the weighted trace-norm under arbitrary sampling distributions »
Rina Foygel · Russ Salakhutdinov · Ohad Shamir · Nati Srebro -
2011 Poster: Transfer Learning by Borrowing Examples »
Joseph Lim · Russ Salakhutdinov · Antonio Torralba -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm »
Russ Salakhutdinov · Nati Srebro -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Practical Large-Scale Optimization for Max-norm Regularization »
Jason D Lee · Benjamin Recht · Russ Salakhutdinov · Nati Srebro · Joel A Tropp -
2010 Poster: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification »
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2009 Workshop: Approximate Learning of Large Scale Graphical Models »
Russ Salakhutdinov · Amir Globerson · David Sontag -
2009 Poster: Replicated Softmax: an Undirected Topic Model »
Russ Salakhutdinov · Geoffrey E Hinton -
2009 Poster: Heterogeneous multitask learning with joint sparsity constraints »
Xiaolin Yang · Seyoung Kim · Eric Xing -
2009 Poster: Learning in Markov Random Fields using Tempered Transitions »
Russ Salakhutdinov -
2009 Poster: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Spotlight: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Poster: Zero-shot Learning with Semantic Output Codes »
Mark M Palatucci · Dean Pomerleau · Geoffrey E Hinton · Tom Mitchell -
2009 Poster: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
2009 Spotlight: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2008 Workshop: Parallel Implementations of Learning Algorithms: What have you done for me lately? »
Robert Thibadeau · Dan Hammerstrom · David S Touretzky · Tom Mitchell -
2008 Workshop: Parallel Implementations of Learning Algorithms: What have you done for me lately? »
Robert Thibadeau · David S Touretzky · Dan Hammerstrom · Tom Mitchell -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2008 Poster: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2008 Spotlight: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2007 Workshop: Statistical Network Models »
Kevin Murphy · Lise Getoor · Eric Xing · Raphael Gottardo -
2007 Poster: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Oral: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Poster: HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation »
Bing Zhao · Eric Xing -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton -
2006 Workshop: New directions on decoding mental states from fMRI data »
John-Dylan Haynes · Tom Mitchell · Francisco Pereira -
2006 Poster: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Talk: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing