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More than 3,100 anti-authoritarian protests are scheduled across the US and at least 15 other countries on Saturday.
All these events will take place under a single banner: No Kings.
Formally launched in June to fight back against Trump administrationpolicies, the No Kings movement has grown with astonishing speed
– its second and most recent mass protest in October drew an estimated 7 million participants.
Organizers expect Saturday’s events to be the biggest prote…

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:07:47

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
arxiv.org/abs/2505.19698 mastoxiv.page/@arXiv_csLG_bot/
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
arxiv.org/abs/2506.08660 mastoxiv.page/@arXiv_csLG_bot/
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
arxiv.org/abs/2506.10167 mastoxiv.page/@arXiv_csLG_bot/
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
arxiv.org/abs/2506.10914 mastoxiv.page/@arXiv_csLG_bot/
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
arxiv.org/abs/2506.18481 mastoxiv.page/@arXiv_csLG_bot/
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
arxiv.org/abs/2506.21220 mastoxiv.page/@arXiv_csLG_bot/
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
arxiv.org/abs/2507.04448 mastoxiv.page/@arXiv_csLG_bot/
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
arxiv.org/abs/2508.01115 mastoxiv.page/@arXiv_csLG_bot/
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
arxiv.org/abs/2508.13904 mastoxiv.page/@arXiv_csLG_bot/
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
arxiv.org/abs/2508.16815 mastoxiv.page/@arXiv_csLG_bot/
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
arxiv.org/abs/2508.21785 mastoxiv.page/@arXiv_csLG_bot/
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
arxiv.org/abs/2509.15796 mastoxiv.page/@arXiv_csLG_bot/
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
arxiv.org/abs/2509.19975 mastoxiv.page/@arXiv_csLG_bot/
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
arxiv.org/abs/2509.21895 mastoxiv.page/@arXiv_csLG_bot/
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
arxiv.org/abs/2509.22566 mastoxiv.page/@arXiv_csLG_bot/
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
arxiv.org/abs/2509.23115 mastoxiv.page/@arXiv_csLG_bot/
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
arxiv.org/abs/2509.25424 mastoxiv.page/@arXiv_csLG_bot/
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
arxiv.org/abs/2509.26626 mastoxiv.page/@arXiv_csLG_bot/
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
arxiv.org/abs/2510.12402 mastoxiv.page/@arXiv_csLG_bot/
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
arxiv.org/abs/2510.15425 mastoxiv.page/@arXiv_csLG_bot/
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
arxiv.org/abs/2510.18114 mastoxiv.page/@arXiv_csLG_bot/
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
arxiv.org/abs/2510.22293 mastoxiv.page/@arXiv_csLG_bot/
toXiv_bot_toot

Mixed messaging from the White House comes about three weeks into the conflict which launched on Feb. 28.
Trump had promised the war would be swift.
On Friday evening, March 20, he said the country is "very close to meeting our objectives,"
including "never allowing Iran to get even close to Nuclear Capability."
But it remains unclear what exactly the White House needs to "complete its mission"
-- and whether that will involve depl…

In recent weeks, the U.S. military has built up forces close to Iran, in what Donald Trump has referred to as an “armada.”
Trump is now deciding whether to take military action against Iran as soon as this weekend, officials said.
But unlike last June, when the United States joined Israel in attacking nuclear sites in Iran, the president’s objectives are less clear.
The "armada" includes the aircraft carrier Abraham Lincoln, which is accompanied by three warships …

@v_i_o_l_a@openbiblio.social
2026-03-11 11:06:40

"Science Ltd. – Research Enterprise in the Age of Machines"
routledge.com/Science-Ltd-Rese
[erscheint (wen…

Donald Trump has finally unveiled his long-awaited framework for healthcare affordability,
almost a year and a half after announcing during a pre-election presidential debate that he had the “concepts of a plan” for healthcare reform.
The short document, titled
"the Great Healthcare Plan",
provides four headline objectives,
but few specific details as to how they will be achieved.
The Trump administration says it intends to lower prescription pri…