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@kexpmusicbot@mastodonapp.uk
2026-01-25 23:34:08

πŸ‡ΊπŸ‡¦ #NowPlaying on KEXP's #VarietyMix
Alexi Murdoch:
🎡 Breathe
#AlexiMurdoch
modofractal.bandcamp.com/track
open.spotify.com/track/4MbbbyT

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:07:37

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[1/6]:
- Towards Attributions of Input Variables in a Coalition
Xinhao Zheng, Huiqi Deng, Quanshi Zhang
arxiv.org/abs/2309.13411
- Knee or ROC
Veronica Wendt, Jacob Steiner, Byunggu Yu, Caleb Kelly, Justin Kim
arxiv.org/abs/2401.07390
- Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almud\'evar, Alfonso Ortega
arxiv.org/abs/2408.07016 mastoxiv.page/@arXiv_csLG_bot/
- Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Etrit Haxholli, Yeti Z. Gurbuz, Ogul Can, Eli Waxman
arxiv.org/abs/2411.00759 mastoxiv.page/@arXiv_csLG_bot/
- Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan, Chao Yang
arxiv.org/abs/2412.06871 mastoxiv.page/@arXiv_csLG_bot/
- Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
arxiv.org/abs/2501.08219 mastoxiv.page/@arXiv_csLG_bot/
- Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
arxiv.org/abs/2501.10538 mastoxiv.page/@arXiv_csLG_bot/
- Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian, Alexander Winkler, Patrick Schaber, Jakob Andert
arxiv.org/abs/2501.16613 mastoxiv.page/@arXiv_csLG_bot/
- A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Alexander Korotin
arxiv.org/abs/2502.01310
- Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi
arxiv.org/abs/2502.03652 mastoxiv.page/@arXiv_csLG_bot/
- Using the Path of Least Resistance to Explain Deep Networks
Sina Salek, Joseph Enguehard
arxiv.org/abs/2502.12108 mastoxiv.page/@arXiv_csLG_bot/
- Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Efstratios Gavves
arxiv.org/abs/2502.17028 mastoxiv.page/@arXiv_csLG_bot/
- Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Sharan Vaswani, Reza Babanezhad
arxiv.org/abs/2503.00229 mastoxiv.page/@arXiv_csLG_bot/
- Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Yan Li, Zhenyu Zhang, Zhengang Wang, Pengfei Chen, Pengfei Zheng
arxiv.org/abs/2503.04398 mastoxiv.page/@arXiv_csLG_bot/
- A Survey on Federated Fine-tuning of Large Language Models
Wu, Tian, Li, Sun, Tam, Zhou, Liao, Xiong, Guo, Li, Xu
arxiv.org/abs/2503.12016 mastoxiv.page/@arXiv_csLG_bot/
- Towards Trustworthy GUI Agents: A Survey
Yucheng Shi, Wenhao Yu, Jingyuan Huang, Wenlin Yao, Wenhu Chen, Ninghao Liu
arxiv.org/abs/2503.23434 mastoxiv.page/@arXiv_csLG_bot/
- CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Chao Yang, Xiannan Huang, Shuhan Qiu, Yan Cheng
arxiv.org/abs/2504.13961 mastoxiv.page/@arXiv_csLG_bot/
- Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Nikola Zubi\'c, Davide Scaramuzza
arxiv.org/abs/2505.11602 mastoxiv.page/@arXiv_csLG_bot/
- RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
arxiv.org/abs/2505.13289 mastoxiv.page/@arXiv_csLG_bot/
- RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
arxiv.org/abs/2505.18877 mastoxiv.page/@arXiv_csLG_bot/
- SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Bechler-Speicher, Zerio, Huri, Vestergaard, Gilad-Bachrach, Jess, Bhatt, Sazonovs
arxiv.org/abs/2505.19193 mastoxiv.page/@arXiv_csLG_bot/
toXiv_bot_toot

@kexpmusicbot@mastodonapp.uk
2025-12-24 05:42:39

πŸ‡ΊπŸ‡¦ #NowPlaying on KEXP's #WoPop
Charlie Parker:
🎡 White Christmas (King Kooba remix)
#CharlieParker
sixdegreesrecords.bandcamp.com
open.spotify.com/track/1Als47O

@petaqui@masto.es
2026-01-24 16:06:03

Las vistas de hoy son de escšndalo
#NotAI #Jaca

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 12:33:36

Crosslisted article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/3]:
- Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
arxiv.org/abs/2602.20422 mastoxiv.page/@arXiv_csAI_bot/
- Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt, Suyash Gupta
arxiv.org/abs/2602.20450 mastoxiv.page/@arXiv_csDC_bot/
- Prior-Agnostic Incentive-Compatible Exploration
Ramya Ramalingam, Osbert Bastani, Aaron Roth
arxiv.org/abs/2602.20465 mastoxiv.page/@arXiv_csGT_bot/
- PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen
arxiv.org/abs/2602.20475 mastoxiv.page/@arXiv_hepex_bot
- ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory
Zhong, Faisal, Fran\c{c}a, Leesatapornwongsa, Szekeres, Rong, Nath
arxiv.org/abs/2602.20502 mastoxiv.page/@arXiv_csAI_bot/
- Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Rakshit Trivedi, Kartik Sharma, David C Parkes
arxiv.org/abs/2602.20517 mastoxiv.page/@arXiv_csAI_bot/
- Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Lovelace, Belardi, Zalouk, Polavaram, Kundurthy, Weinberger
arxiv.org/abs/2602.20528 mastoxiv.page/@arXiv_csCL_bot/
- Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ T...
Yanming Lai, Defeng Sun
arxiv.org/abs/2602.20555 mastoxiv.page/@arXiv_statML_bo
- Personal Information Parroting in Language Models
Nishant Subramani, Kshitish Ghate, Mona Diab
arxiv.org/abs/2602.20580 mastoxiv.page/@arXiv_csCL_bot/
- Characterizing Online and Private Learnability under Distributional Constraints via Generalized S...
Mo\"ise Blanchard, Abhishek Shetty, Alexander Rakhlin
arxiv.org/abs/2602.20585 mastoxiv.page/@arXiv_statML_bo
- Amortized Bayesian inference for actigraph time sheet data from mobile devices
Daniel Zhou, Sudipto Banerjee
arxiv.org/abs/2602.20611 mastoxiv.page/@arXiv_statML_bo
- Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Xueqiang Lv, Shizhou Zhang, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang
arxiv.org/abs/2602.20616 mastoxiv.page/@arXiv_csCV_bot/
- On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
Boao Kong, Hengrui Zhang, Kun Yuan
arxiv.org/abs/2602.20646 mastoxiv.page/@arXiv_mathOC_bo
- DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Feng, Reich, Beaglehole, Luo, Park, Yoo, Huang, Mao, Boz, Kim
arxiv.org/abs/2602.20652 mastoxiv.page/@arXiv_statML_bo
- Vision-Language Models for Ergonomic Assessment of Manual Lifting Tasks: Estimating Horizontal an...
Mohammad Sadra Rajabi, Aanuoluwapo Ojelade, Sunwook Kim, Maury A. Nussbaum
arxiv.org/abs/2602.20658 mastoxiv.page/@arXiv_csCV_bot/
- F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performanc...
Xuran Ma, et al.
arxiv.org/abs/2602.20712 mastoxiv.page/@arXiv_astrophIM
- Communication-Inspired Tokenization for Structured Image Representations
Davtyan, Sahin, Haghighi, Stapf, Acuaviva, Alahi, Favaro
arxiv.org/abs/2602.20731 mastoxiv.page/@arXiv_csCV_bot/
- SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, et al.
arxiv.org/abs/2602.20751 mastoxiv.page/@arXiv_csCL_bot/
- Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Anh-Tuan Dao, Driss Matrouf, Nicholas Evans
arxiv.org/abs/2602.20805 mastoxiv.page/@arXiv_csSD_bot/
- Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
Sayantan Dasgupta, Trevor Cohn, Timothy Baldwin
arxiv.org/abs/2602.20816 mastoxiv.page/@arXiv_csCL_bot/
- DRESS: A Continuous Framework for Structural Graph Refinement
Eduar Castrillo Velilla
arxiv.org/abs/2602.20833 mastoxiv.page/@arXiv_csDS_bot/
toXiv_bot_toot

@BBC6MusicBot@mastodonapp.uk
2025-12-24 12:59:41

πŸ‡ΊπŸ‡¦ #NowPlaying on #BBC6Music's #LaurenLaverne
Al Barry and the Cimarons:
🎡 Morning Sun
#AlBarryandtheCimarons
franzalexanderdj.bandcamp.com/
open.spotify.com/track/5uqqDha

@BBC3MusicBot@mastodonapp.uk
2026-01-21 05:49:34

πŸ‡ΊπŸ‡¦ #NowPlaying on BBCRadio3's #ThroughTheNight
Louis Spohr, Kyoko Ogawa, Anne Sophie Van Riel, Alexander Arai-Swale, Hyunjung Song, Carlotta Brendel, Susanne Szambelan, Ronja Macholdt, Astrid den Daas & Pauline Zahno:
🎡 Nonet in F major, Op 31
open.spotify.com/track/05qoz1N
Please πŸ” BOOST to share what you like
- your followers don't see if you ⭐ favourite a post

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 10:39:51

Does Order Matter : Connecting The Law of Robustness to Robust Generalization
Himadri Mandal, Vishnu Varadarajan, Jaee Ponde, Aritra Das, Mihir More, Debayan Gupta
arxiv.org/abs/2602.20971 arxiv.org/pdf/2602.20971 arxiv.org/html/2602.20971
arXiv:2602.20971v1 Announce Type: new
Abstract: Bubeck and Sellke (2021) pose as an open problem the connection between the law of robustness and robust generalization. The law of robustness states that overparameterization is necessary for models to interpolate robustly; in particular, robust interpolation requires the learned function to be Lipschitz. Robust generalization asks whether small robust training loss implies small robust test loss. We resolve this problem by explicitly connecting the two for arbitrary data distributions. Specifically, we introduce a nontrivial notion of robust generalization error and convert it into a lower bound on the expected Rademacher complexity of the induced robust loss class. Our bounds recover the $\Omega(n^{1/d})$ regime of Wu et al.\ (2023) and show that, up to constants, robust generalization does not change the order of the Lipschitz constant required for smooth interpolation. We conduct experiments to probe the predicted scaling with dataset size and model capacity, testing whether empirical behavior aligns more closely with the predictions of Bubeck and Sellke (2021) or Wu et al.\ (2023). For MNIST, we find that the lower-bound Lipschitz constant scales on the order predicted by Wu et al.\ (2023). Informally, to obtain low robust generalization error, the Lipschitz constant must lie in a range that we bound, and the allowable perturbation radius is linked to the Lipschitz scale.
toXiv_bot_toot

@kexpmusicbot@mastodonapp.uk
2026-02-21 22:35:56

πŸ‡ΊπŸ‡¦ #NowPlaying on KEXP's #VarietyMix
Talking Heads:
🎡 Wild Wild Life
#TalkingHeads
spoonhead.bandcamp.com/track/t
open.spotify.com/track/4u6rcml

@kexpmusicbot@mastodonapp.uk
2025-12-08 05:45:23

πŸ‡ΊπŸ‡¦ #NowPlaying on KEXP's #Expansions
Alex Nut:
🎡 andthenitstarted (Last Nubian rmx)
#AlexNut
alexandernut.bandcamp.com/trac
open.spotify.com/track/3ha0xLm