[Paper Review] Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Levine, Sergey. “Reinforcement learning and control as probabilistic inference: Tutorial and review.” arXiv preprint arXiv:1805.00909 (2018).
Posts by Tag
- Machine Learning 13
- Finance 8
- Market 7
- Reinforcement Learning 7
- Trading 6
- Neural Network 5
- Policy Gradient 4
- Autoencoder 3
- Spoken Language Understanding 2
- Portfolio Management 1
- Text Mining 1
- Classic 1
- Gaussian Process 1
- Efficient Sampling 1
- Meta Learning 1
- Group Theory 1
- Natural Language Processing 1
- Probabilistic Inference 1
- Probabilistic Graph Model 1
Machine Learning
[Paper Review] Efficient Exploration via State Marginal Matching
Lee, Lisa, et al. “Efficient exploration via state marginal matching.” arXiv preprint arXiv:1906.05274 (2019).
[Paper Review] Soft Actor-Critic Algorithms and Applications
Haarnoja, Tuomas, et al. “Soft actor-critic algorithms and applications.” arXiv preprint arXiv:1812.05905 (2018).
[Paper Review] Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, Scott, Herke Van Hoof, and David Meger. “Addressing function approximation error in actor-critic methods.” arXiv preprint arXiv:1802.09477 (2018).
[Paper Review] Deterministic Policy Gradient Algorithms
Silver, David, et al. “Deterministic policy gradient algorithms.” 2014.
[Paper Review] Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Zihang, et al. “Transformer-xl: Attentive language models beyond a fixed-length context.” arXiv preprint arXiv:1901.02860 (2019).
[Paper Review] Meta-Learning Symmetries by Reparameterization
Zhou, Allan, Tom Knowles, and Chelsea Finn. “Meta-learning symmetries by reparameterization.” arXiv preprint arXiv:2007.02933 (2020).
[Paper Review] Efficiently Sampling Functions from Gaussian Process Posteriors
BWilson, James T., et al. “Efficiently sampling functions from Gaussian process posteriors.” arXiv preprint arXiv:2002.09309 (2020).
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
[Paper Review] Autoencoder Asset Pricing Models
Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu. “Autoencoder asset pricing models.” Available at SSRN (2019).
[Paper Review] Stock Selection via Nonlinear Multi-Factor Models
Levin, Asriel E. “Stock selection via nonlinear multi-factor models.” Advances in Neural Information Processing Systems. 1996.
[Paper Review] Deep Portfolio Theory
Heaton, J. B., N. G. Polson, and Jan Hendrik Witte. “Deep learning for finance: deep portfolios.” Applied Stochastic Models in Business and Industry 33.1 (20...
Finance
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
[Paper Review] Autoencoder Asset Pricing Models
Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu. “Autoencoder asset pricing models.” Available at SSRN (2019).
Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
Gary Antonacci. McGraw-Hill Education. 2014
Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System
Gray, Wesley R., Vogel, Jack R. Wiley. 2016
[Paper Review] Stock Selection via Nonlinear Multi-Factor Models
Levin, Asriel E. “Stock selection via nonlinear multi-factor models.” Advances in Neural Information Processing Systems. 1996.
[Paper Review] Stock Returns and Future Tense Language in 10-K Reports
Karapandza, Rasa. “Stock returns and future tense language in 10-K reports.” Journal of Banking & Finance 71 (2016): 50-61.
[Paper Review] Deep Portfolio Theory
Heaton, J. B., N. G. Polson, and Jan Hendrik Witte. “Deep learning for finance: deep portfolios.” Applied Stochastic Models in Business and Industry 33.1 (20...
Market
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
[Paper Review] Autoencoder Asset Pricing Models
Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu. “Autoencoder asset pricing models.” Available at SSRN (2019).
Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
Gary Antonacci. McGraw-Hill Education. 2014
Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System
Gray, Wesley R., Vogel, Jack R. Wiley. 2016
[Paper Review] Stock Selection via Nonlinear Multi-Factor Models
Levin, Asriel E. “Stock selection via nonlinear multi-factor models.” Advances in Neural Information Processing Systems. 1996.
[Paper Review] Stock Returns and Future Tense Language in 10-K Reports
Karapandza, Rasa. “Stock returns and future tense language in 10-K reports.” Journal of Banking & Finance 71 (2016): 50-61.
Reinforcement Learning
[Paper Review] Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Levine, Sergey. “Reinforcement learning and control as probabilistic inference: Tutorial and review.” arXiv preprint arXiv:1805.00909 (2018).
[Paper Review] Efficient Exploration via State Marginal Matching
Lee, Lisa, et al. “Efficient exploration via state marginal matching.” arXiv preprint arXiv:1906.05274 (2019).
[Paper Review] Soft Actor-Critic Algorithms and Applications
Haarnoja, Tuomas, et al. “Soft actor-critic algorithms and applications.” arXiv preprint arXiv:1812.05905 (2018).
[Paper Review] Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, Scott, Herke Van Hoof, and David Meger. “Addressing function approximation error in actor-critic methods.” arXiv preprint arXiv:1802.09477 (2018).
[Paper Review] Deterministic Policy Gradient Algorithms
Silver, David, et al. “Deterministic policy gradient algorithms.” 2014.
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
Trading
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk
Gary Antonacci. McGraw-Hill Education. 2014
Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System
Gray, Wesley R., Vogel, Jack R. Wiley. 2016
[Paper Review] Stock Selection via Nonlinear Multi-Factor Models
Levin, Asriel E. “Stock selection via nonlinear multi-factor models.” Advances in Neural Information Processing Systems. 1996.
[Paper Review] Stock Returns and Future Tense Language in 10-K Reports
Karapandza, Rasa. “Stock returns and future tense language in 10-K reports.” Journal of Banking & Finance 71 (2016): 50-61.
Neural Network
[Paper Review] VQ-WAV2VEC: Self-Supervised Learning of Discrete Speech Representations
Baevski, Alexei, Steffen Schneider, and Michael Auli. “vq-wav2vec: Self-supervised learning of discrete speech representations.” arXiv preprint arXiv:1910.05...
[Paper Review] WAV2VEC: Unsupervised Pre-training for Speech Recognition
Schneider, Steffen, et al. “wav2vec: Unsupervised pre-training for speech recognition.” arXiv preprint arXiv:1904.05862 (2019).
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
[Paper Review] Autoencoder Asset Pricing Models
Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu. “Autoencoder asset pricing models.” Available at SSRN (2019).
[Paper Review] Stock Selection via Nonlinear Multi-Factor Models
Levin, Asriel E. “Stock selection via nonlinear multi-factor models.” Advances in Neural Information Processing Systems. 1996.
Policy Gradient
[Paper Review] Efficient Exploration via State Marginal Matching
Lee, Lisa, et al. “Efficient exploration via state marginal matching.” arXiv preprint arXiv:1906.05274 (2019).
[Paper Review] Soft Actor-Critic Algorithms and Applications
Haarnoja, Tuomas, et al. “Soft actor-critic algorithms and applications.” arXiv preprint arXiv:1812.05905 (2018).
[Paper Review] Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, Scott, Herke Van Hoof, and David Meger. “Addressing function approximation error in actor-critic methods.” arXiv preprint arXiv:1802.09477 (2018).
[Paper Review] Deterministic Policy Gradient Algorithms
Silver, David, et al. “Deterministic policy gradient algorithms.” 2014.
Autoencoder
[Paper Review] Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
Wei, Haoran, et al. “Model-based Reinforcement Learning for Predictions and Control for Limit Order Books.” arXiv preprint arXiv:1910.03743 (2019).
[Paper Review] Autoencoder Asset Pricing Models
Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu. “Autoencoder asset pricing models.” Available at SSRN (2019).
[Paper Review] Deep Portfolio Theory
Heaton, J. B., N. G. Polson, and Jan Hendrik Witte. “Deep learning for finance: deep portfolios.” Applied Stochastic Models in Business and Industry 33.1 (20...
Spoken Language Understanding
[Paper Review] VQ-WAV2VEC: Self-Supervised Learning of Discrete Speech Representations
Baevski, Alexei, Steffen Schneider, and Michael Auli. “vq-wav2vec: Self-supervised learning of discrete speech representations.” arXiv preprint arXiv:1910.05...
[Paper Review] WAV2VEC: Unsupervised Pre-training for Speech Recognition
Schneider, Steffen, et al. “wav2vec: Unsupervised pre-training for speech recognition.” arXiv preprint arXiv:1904.05862 (2019).
Portfolio Management
[Paper Review] Deep Portfolio Theory
Heaton, J. B., N. G. Polson, and Jan Hendrik Witte. “Deep learning for finance: deep portfolios.” Applied Stochastic Models in Business and Industry 33.1 (20...
Text Mining
[Paper Review] Stock Returns and Future Tense Language in 10-K Reports
Karapandza, Rasa. “Stock returns and future tense language in 10-K reports.” Journal of Banking & Finance 71 (2016): 50-61.
Classic
Back to top ↑Gaussian Process
[Paper Review] Efficiently Sampling Functions from Gaussian Process Posteriors
BWilson, James T., et al. “Efficiently sampling functions from Gaussian process posteriors.” arXiv preprint arXiv:2002.09309 (2020).
Efficient Sampling
[Paper Review] Efficiently Sampling Functions from Gaussian Process Posteriors
BWilson, James T., et al. “Efficiently sampling functions from Gaussian process posteriors.” arXiv preprint arXiv:2002.09309 (2020).
Meta Learning
[Paper Review] Meta-Learning Symmetries by Reparameterization
Zhou, Allan, Tom Knowles, and Chelsea Finn. “Meta-learning symmetries by reparameterization.” arXiv preprint arXiv:2007.02933 (2020).
Group Theory
[Paper Review] Meta-Learning Symmetries by Reparameterization
Zhou, Allan, Tom Knowles, and Chelsea Finn. “Meta-learning symmetries by reparameterization.” arXiv preprint arXiv:2007.02933 (2020).
Natural Language Processing
[Paper Review] Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Zihang, et al. “Transformer-xl: Attentive language models beyond a fixed-length context.” arXiv preprint arXiv:1901.02860 (2019).
Probabilistic Inference
[Paper Review] Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Levine, Sergey. “Reinforcement learning and control as probabilistic inference: Tutorial and review.” arXiv preprint arXiv:1805.00909 (2018).
Probabilistic Graph Model
[Paper Review] Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Levine, Sergey. “Reinforcement learning and control as probabilistic inference: Tutorial and review.” arXiv preprint arXiv:1805.00909 (2018).