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We introduce BatchGFN -- a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward.
A sequential algorithm for training text classifiers: Corrigendum and additional data
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Active learning literature survey
Settles, B · 2009
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Gaussian process optimization in the bandit setting: no regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M · 2010
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Bayesian active learning for classification and preference learning
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Entropy search for information-efficient global optimization
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Adam: A method for stochastic optimization
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Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G · 2018
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Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
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Efficient nonmyopic bayesian optimization via one-shot multi-step trees
Jiang, S., Jiang, D., Balandat, M., Karrer, B., Gardner, J., and Garnett, R · 2020
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Gone fishing: Neural active learning with fisher embeddings
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Beyond marginal uncertainty: How accurately can bayesian regression models estimate posterior predictive correlations?
Wang, C., Sun, S., and Grosse, R · 2021
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A framework and benchmark for deep batch active learning for regression
Holzmüller, D., Zaverkin, V., Kästner, J., and Steinwart, I · 2022
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Unifying approaches in active learning and active sampling via fisher information and information-theoretic quantities
Kirsch, A. and Gal, Y · 2022
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Trajectory balance: Improved credit assignment in gflownets
Malkin, N., Jain, M., Bengio, E., Sun, C., and Bengio, Y · 2022
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torchgfn: A pytorch gflownet library
Lahlou, S., Viviano, J. D., and Schmidt, V · 2023
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Kirsch, A., Farquhar, S., Atighehchian, P., Jesson, A., Branchaud-Charron, F., and Gal, Y · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Kossen, J., Band, N., Lyle, C., Gomez, A. N., Rainforth, T., and Gal, Y · 2021
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Conditioning sparse variational gaussian processes for online decision-making
Maddox, W. J., Stanton, S., and Wilson, A. G · 2021
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Flow network based generative models for non-iterative diverse candidate generation
Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y
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Gflownet foundations, 2021b
Bengio, Y., Deleu, T., Hu, E. J., Lahlou, S., Tiwari, M., and Bengio, E
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Biological sequence design with gflownets
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Jain, M., Raparthy, S. C., Hernandez-Garcia, A., Rector-Brooks, J., Bengio, Y., Miret, S., and Bengio, E
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Learning gflownets from partial episodes for improved convergence and stability
Madan, K., Rector-Brooks, J., Korablyov, M., Bengio, E., Jain, M., Nica, A., Bosc, T., Bengio, Y., and Malkin, N · 2023
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Better training of gflownets with local credit and incomplete trajectories
Pan, L., Malkin, N., Zhang, D., and Bengio, Y · 2023
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Robust scheduling with gflownets
Zhang, D. W., Rainone, C., Peschl, M., and Bondesan, R · 2023
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