#optimizers — Public Fediverse posts

#Economists and others are used to building #forecasts on the assumption that the agents involved in what they’re forecasting are #rational #optimizers. Makes it difficult when the most important actor is a #narcissist with an inexhaustible need for ego gratification.

#Economists and others are used to building #forecasts on the assumption that the agents involved in what they’re forecasting are #rational #optimizers. Makes it difficult when the most important actor is a #narcissist with an inexhaustible need for ego gratification.

Efficient E-Matching for Super Optimizers

https://blog.vortan.dev/ematching/

#HackerNews #Efficient #E-Matching #Super #Optimizers #E-Matching #Technology #Optimization

Efficient E-Matching for Super Optimizers

https://blog.vortan.dev/ematching/

#HackerNews #Efficient #E-Matching #Super #Optimizers #E-Matching #Technology #Optimization

Efficient E-Matching for Super Optimizers

https://blog.vortan.dev/ematching/

#HackerNews #Efficient #E-Matching #Super #Optimizers #E-Matching #Technology #Optimization

Efficient E-Matching for Super Optimizers

https://blog.vortan.dev/ematching/

#HackerNews #Efficient #E-Matching #Super #Optimizers #E-Matching #Technology #Optimization

The Optimizer Advantage?

This is not how I’d expect an optimizer system to work, at least based on how it’s advertised.

https://solarboi.com/2025/01/23/the-optimizer-advantage/

'PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates', by Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell.

http://jmlr.org/papers/v25/23-1187.html

#optimizers #optimization #preconditioned

'PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates', by Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell.

http://jmlr.org/papers/v25/23-1187.html

#optimizers #optimization #preconditioned

'PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates', by Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell.

http://jmlr.org/papers/v25/23-1187.html

#optimizers #optimization #preconditioned

'PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates', by Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell.

http://jmlr.org/papers/v25/23-1187.html

#optimizers #optimization #preconditioned

'PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates', by Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell.

http://jmlr.org/papers/v25/23-1187.html

#optimizers #optimization #preconditioned

'PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization', by Qiqi Duan et al.

http://jmlr.org/papers/v25/23-0386.html

#optimizers #optimization #pypop7

'PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization', by Qiqi Duan et al.

http://jmlr.org/papers/v25/23-0386.html

#optimizers #optimization #pypop7

'PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization', by Qiqi Duan et al.

http://jmlr.org/papers/v25/23-0386.html

#optimizers #optimization #pypop7

'PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization', by Qiqi Duan et al.

http://jmlr.org/papers/v25/23-0386.html

#optimizers #optimization #pypop7

'PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization', by Qiqi Duan et al.

http://jmlr.org/papers/v25/23-0386.html

#optimizers #optimization #pypop7

'Multi-Objective Neural Architecture Search by Learning Search Space Partitions', by Yiyang Zhao, Linnan Wang, Tian Guo.

http://jmlr.org/papers/v25/23-1013.html

#optimizers #optimizer #optimizations

'Multi-Objective Neural Architecture Search by Learning Search Space Partitions', by Yiyang Zhao, Linnan Wang, Tian Guo.

http://jmlr.org/papers/v25/23-1013.html

#optimizers #optimizer #optimizations

'Multi-Objective Neural Architecture Search by Learning Search Space Partitions', by Yiyang Zhao, Linnan Wang, Tian Guo.

http://jmlr.org/papers/v25/23-1013.html

#optimizers #optimizer #optimizations

'Multi-Objective Neural Architecture Search by Learning Search Space Partitions', by Yiyang Zhao, Linnan Wang, Tian Guo.

http://jmlr.org/papers/v25/23-1013.html

#optimizers #optimizer #optimizations

'Multi-Objective Neural Architecture Search by Learning Search Space Partitions', by Yiyang Zhao, Linnan Wang, Tian Guo.

http://jmlr.org/papers/v25/23-1013.html

#optimizers #optimizer #optimizations

'Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning', by Maximilian Hüttenrauch, Gerhard Neumann.

http://jmlr.org/papers/v25/22-0564.html

#reinforcement #optimizers #optimizes

'Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning', by Maximilian Hüttenrauch, Gerhard Neumann.

http://jmlr.org/papers/v25/22-0564.html

#reinforcement #optimizers #optimizes

'Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning', by Maximilian Hüttenrauch, Gerhard Neumann.

http://jmlr.org/papers/v25/22-0564.html

#reinforcement #optimizers #optimizes

'Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning', by Maximilian Hüttenrauch, Gerhard Neumann.

http://jmlr.org/papers/v25/22-0564.html

#reinforcement #optimizers #optimizes

'Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning', by Maximilian Hüttenrauch, Gerhard Neumann.

http://jmlr.org/papers/v25/22-0564.html

#reinforcement #optimizers #optimizes

'Neural Feature Learning in Function Space', by Xiangxiang Xu, Lizhong Zheng.

http://jmlr.org/papers/v25/23-1202.html

#features #feature #optimizers

'Neural Feature Learning in Function Space', by Xiangxiang Xu, Lizhong Zheng.

http://jmlr.org/papers/v25/23-1202.html

#features #feature #optimizers

'Neural Feature Learning in Function Space', by Xiangxiang Xu, Lizhong Zheng.

http://jmlr.org/papers/v25/23-1202.html

#features #feature #optimizers

'Neural Feature Learning in Function Space', by Xiangxiang Xu, Lizhong Zheng.

http://jmlr.org/papers/v25/23-1202.html

#features #feature #optimizers

'Neural Feature Learning in Function Space', by Xiangxiang Xu, Lizhong Zheng.

http://jmlr.org/papers/v25/23-1202.html

#features #feature #optimizers

'Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training', by Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan.

http://jmlr.org/papers/v25/23-1073.html

#accelerated #optimizers #adaptive

'Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training', by Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan.

http://jmlr.org/papers/v25/23-1073.html

#accelerated #optimizers #adaptive

'Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training', by Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan.

http://jmlr.org/papers/v25/23-1073.html

#accelerated #optimizers #adaptive

'Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training', by Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan.

http://jmlr.org/papers/v25/23-1073.html

#accelerated #optimizers #adaptive

'Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training', by Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan.

http://jmlr.org/papers/v25/23-1073.html

#accelerated #optimizers #adaptive

'Scaling the Convex Barrier with Sparse Dual Algorithms', by Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar.

http://jmlr.org/papers/v25/21-0076.html

#optimizers #sparse #dual

'Scaling the Convex Barrier with Sparse Dual Algorithms', by Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar.

http://jmlr.org/papers/v25/21-0076.html

#optimizers #sparse #dual

'Scaling the Convex Barrier with Sparse Dual Algorithms', by Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar.

http://jmlr.org/papers/v25/21-0076.html

#optimizers #sparse #dual

'Scaling the Convex Barrier with Sparse Dual Algorithms', by Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar.

http://jmlr.org/papers/v25/21-0076.html

#optimizers #sparse #dual

'Scaling the Convex Barrier with Sparse Dual Algorithms', by Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar.

http://jmlr.org/papers/v25/21-0076.html

#optimizers #sparse #dual

'Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks', by Dong-Young Lim, Sotirios Sabanis.

http://jmlr.org/papers/v25/22-0796.html

#langevin #adaptive #optimizers

'Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks', by Dong-Young Lim, Sotirios Sabanis.

http://jmlr.org/papers/v25/22-0796.html

#langevin #adaptive #optimizers

'Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks', by Dong-Young Lim, Sotirios Sabanis.

http://jmlr.org/papers/v25/22-0796.html

#langevin #adaptive #optimizers

'Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks', by Dong-Young Lim, Sotirios Sabanis.

http://jmlr.org/papers/v25/22-0796.html

#langevin #adaptive #optimizers

'Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks', by Dong-Young Lim, Sotirios Sabanis.

http://jmlr.org/papers/v25/22-0796.html

#langevin #adaptive #optimizers

'Improving physics-informed neural networks with meta-learned optimization', by Alex Bihlo.

http://jmlr.org/papers/v25/23-0356.html

#optimizers #learnable #learned

'Improving physics-informed neural networks with meta-learned optimization', by Alex Bihlo.

http://jmlr.org/papers/v25/23-0356.html

#optimizers #learnable #learned

'Improving physics-informed neural networks with meta-learned optimization', by Alex Bihlo.

http://jmlr.org/papers/v25/23-0356.html

#optimizers #learnable #learned