Tootfinder

I've only just learned of the death of our former colleague Chris Gathercole last April. I knew him as an advocate for learning disabled people, taking their place, with support, in the life of the community. He had a profound influence on provision in the North West, where with Tom Mclean, who died the previous year, and others, he steered into being a radical policy framework that shaped the pattern of de-institutionalisation in the region.

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Rohith Mahadevan
https://arxiv.org/abs/2510.06987 https://arxiv.org/pdf/2510.06987…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is…

Organic geochemical evidence for life in Archean rocks identified by pyrolysis–GC–MS and supervised machine learning: #life in ancient rocks: https://gizmodo.com/ai-uncovers-evidence-of-life-in-3-3-billion-year-old-rocks-2000687539 / https://www.reuters.com/science/new-method-spots-signs-earths-primordial-life-ancient-rocks-2025-11-18/ - a machine-learning-enhanced approach to chemical analysis is drastically expanding the chemical record of life on Earth, and it could help us find evidence of life on other planets too.

I think the root of the “AI” evil is when AI researchers in the 1960s recognized that they outrageously underestimated the complexity of the human mind.
They became humiliated by their promises that AGI was just a few years away—and then went full goblin mode that’s lasting to this day.
Some of the OG researchers took it quite badly that they stalled and weren’t in the limelight anymore.
‣ Marvin Minsky (co-founder of MIT AI lab and arguably the most important early AI bro) went on to visit Epstein’s island multiple times.
‣ Karl Steinbuch, who came up with the German term for computer science ("Informatik")—who also was a literal Nazi (and likely war criminal) in World War II—later wrote articles in ultra-right magazines about things like “equal rights rob women of their children”.
‣ John McCarthy (inventor of Lisp, co-authored document that coined the term “Artificial Intelligence”) was a staunch Republican who years later claimed (in a serious article) that “thermostats have beliefs”.
[one moment, I am receiving more information]
‣ There’s a second Epstein Island AI pioneer? Who also was Chief Learning Officer at… Trump University? That would be Roger Schank (founded one of the first AI companies in the 1980s AI boom, it even had an IPO. Of course the 1980s AI bubble burst).
Obviously all of the above received all the awards in computer science and are very revered people.

AI in data science education: experiences from the classroom
J. A. Hageman, C. F. W. Peeters
https://arxiv.org/abs/2510.00793 https://arxiv.org/pdf/2510.00…

AI in data science education: experiences from the classroom
This study explores the integration of AI, particularly large language models (LLMs) like ChatGPT, into educational settings, focusing on the implications for teaching and learning. Through interviews with course coordinators from data science courses at Wageningen University, this research identifies both the benefits and challenges associated with AI in the classroom. While AI tools can streamline tasks and enhance learning, concerns arise regarding students' overreliance on these technologie…

"All in all, these machine learning models are so good that they’ve almost completely supplanted traditional methods for detecting and phase-picking earthquakes, especially for smaller magnitudes."
"In the last five years, an AI-based workflow has almost completely replaced one of the fundamental tasks in seismology for the better."
(It's not LLMs)

“Like putting on glasses for the first time”—how AI improves earthquake detection
AI is “comically good” at detecting small earthquakes—here’s why that matters.

🌡️ Taking the moon's temperature with beeswax
#moon

Taking the moon's temperature with beeswax
Sometimes space exploration doesn't go as planned. But even in failure, engineers can learn, adapt, and try again. One of the best ways to do that is to share the learning, and allow others to reproduce the work that might not have succeeded, allowing them to try again. A group from MIT's Space Enabled Research Group, part of its Media Lab, recently released a paper in Space Science Reviews that describes the design and testing results of a pair of passive sensors sent to the moon on the ill-fa…

Want to break into climate work but don't know where to start? Terra.do's Learning for Action fellowship might be your answer.
This 12-week program goes deep on real-world climate solutions—beyond just clean energy. You'll learn the science, explore diverse solutions, and connect with a global community, all while working full-time (6-10 hrs/week).
Financial aid available.

Answering Your LFA Questions
Terra.do's Learning for Action fellowship (LFA) is our flagship 12-week course. Here, we'll answer some common questions about the program.

End of an era. I remember when that exhibit went in. That said, it’s in bad shape and has been for some time. #okc #oklahoma

Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning
Alvaro Belmonte-Baeza, Miguel Cazorla, Gabriel J. Garc\'ia, Carlos J. P\'erez-Del-Pulgar, Jorge Pomares
https://arxiv.org/abs/2510.12684

Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning
Robotics plays a pivotal role in planetary science and exploration, where autonomous and reliable systems are crucial due to the risks and challenges inherent to space environments. The establishment of permanent lunar bases demands robotic platforms capable of navigating and manipulating in the harsh lunar terrain. While wheeled rovers have been the mainstay for planetary exploration, their limitations in unstructured and steep terrains motivate the adoption of legged robots, which offer super…

Please sign this petition
I'm a chemist. As a grad student I was a TA and a tutor. I taught college chemistry classes.
That's teaching a subject, not communicating about a technical issue (yes, these really are different skills)
And climate change advocacy has highlighted how difficult communicating is.
I wish this proposal had been enacted when I was a student.
Please sign.

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan, Shizhe Diao, Tong Zhang
https://arxiv.org/abs/2510.11769 https://

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models are often trained with expensive online Reinforcement Learning (RL) or expert iteration. However, these approaches rely on fixed problem sets, which causes inefficient training and limits the model to tackle complex problems. To overcome these limitations, we propose GAR: Generative Adversarial Reinforcement learning, a…

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
Javed Ali Khan, Muhammad Yaqoob, Mamoona Tasadduq, Hafsa Shareef Dar, Aitezaz Ahsan
https://arxiv.org/abs/2510.11556

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
The evolving pedagogy paradigms are leading toward educational transformations. One fundamental aspect of effective learning is relevant, immediate, and constructive feedback to students. Providing constructive feedback to large cohorts in academia is an ongoing challenge. Therefore, academics are moving towards automated assessment to provide immediate feedback. However, current approaches are often limited in scope, offering simplistic responses that do not provide students with personalized …

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Mario Medrano-Paredes, Carmen Fern\'andez-Gonz\'alez, Francisco-Javier D\'iaz-Pernas, Hichem Saoudi, Javier Gonz\'alez-Alonso, Mario Mart\'inez-Zarzuela

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Advances in machine learning and wearable sensors offer new opportunities for capturing and analyzing human movement outside specialized laboratories. Accurate assessment of human movement under real-world conditions is essential for telemedicine, sports science, and rehabilitation. This preclinical benchmark compares monocular video-based 3D human pose estimation models with inertial measurement units (IMUs), leveraging the VIDIMU dataset containing a total of 13 clinically relevant daily acti…

Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning
Xinying Hou, Ruiwei Xiao, Runlong Ye, Michael Liut, John Stamper
https://arxiv.org/abs/2510.05417

Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning
The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and w…

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck, Edward Cripps, David S. Leslie
https://arxiv.org/abs/2509.26005

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagra…

If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics
Ben Blum-Smith, Claudia Brugman, Thomas Conners, Soledad Villar
https://arxiv.org/abs/2510.09902

If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics
This essay develops a parallel between the Fundamental Theorem of Galois Theory and the Stone--Weierstrass theorem: both can be viewed as assertions that tie the distinguishing power of a class of objects to their expressive power. We provide an elementary theorem connecting the relevant notions of "distinguishing power". We also discuss machine learning and data science contexts in which these theorems, and more generally the theme of links between distinguishing power and expressive power, ap…

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
Nada Alghamdi, Paolo de Angelis, Pietro Asinari, Eliodoro Chiavazzo
https://arxiv.org/abs/2510.05020

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
Machine learning interatomic potentials (MLIPs) are transforming materials science and engineering by enabling the study of complex phenomena, such as those critical to battery operation. In this work, we benchmark the MACE machine learning model against a well-trained DeePMD potential for predicting interstitial lithium diffusivity in LiF, a key component in the solid electrolyte interphase in Li ion batteries. Our results demonstrate that the MACE-MPA-0 foundational model achieves comparable …

"Coch and Daniel do not call for more neuroscience in classrooms, but for better scientific literacy among teachers, school leaders, and teacher educators. They demonstrate that anyone seeking to apply brain research to enhance teaching must first comprehend what that research can – and cannot – reveal."

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui

Physics-Informed Machine Learning in Biomedical Science and Engineering
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey, George Em Karniadakis
https://arxiv.org/abs/2510.05433 htt…

Physics-Informed Machine Learning in Biomedical Science and Engineering
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations…

Machine Learning-based Path Loss Prediction in Suburban Environment in the Sub-6 GHz Band
Ferdaous Tarhouni, Muneer AlZubi, Mohamed-Slim Alouini
https://arxiv.org/abs/2510.00696

Machine Learning-based Path Loss Prediction in Suburban Environment in the Sub-6 GHz Band
Accurate path loss (PL) prediction is crucial for successful network planning, antenna design, and performance optimization in wireless communication systems. Several conventional approaches for PL prediction have been adopted, but they have been demonstrated to lack flexibility and accuracy. In this work, we investigate the effectiveness of Machine Learning (ML) models in predicting PL, particularly for the sub-6 GHz band in a suburban campus of King Abdullah University of Science and Technolo…

Quantum Probe Tomography
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang
https://arxiv.org/abs/2510.08499 https://arxiv.org/pdf/2510.08499

Quantum Probe Tomography
Characterizing quantum many-body systems is a fundamental problem across physics, chemistry, and materials science. While significant progress has been made, many existing Hamiltonian learning protocols demand digital quantum control over the entire system, creating a disconnect from many real-world settings that provide access only through small, local probes. Motivated by this, we introduce and formalize the problem of quantum probe tomography, where one seeks to learn the parameters of a man…

LAPIS: A Performance Portable, High Productivity Compiler Framework
Brian Kelley, Sivasankaran Rajamanickam
https://arxiv.org/abs/2509.25605 https://arxiv.…

LAPIS: A Performance Portable, High Productivity Compiler Framework
Portability, performance, and productivity are three critical dimensions for evaluating a programming model or compiler infrastructure. Several modern programming models for computational science focus on performance and portability. On the other end, several machine learning focused programming models focus on portability and productivity. A clear solution that is strong in all three dimensions has yet to emerge. A second related problem arises when use cases from computational science converg…

Enhancing Software Testing Education: Understanding Where Students Struggle
Shiza Andleeb, Teo Mendoza, Lucas Cordova, Gursimran Walia, Jeffrey C. Carver
https://arxiv.org/abs/2510.00957

Enhancing Software Testing Education: Understanding Where Students Struggle
Effective software testing is critical for producing reliable and secure software, yet many computer science students struggle to master the foundational concepts required to construct comprehensive test suites. While automated feedback tools are widely used to support student learning, it remains unclear which testing concepts are most frequently misunderstood and how these misunderstandings are reflected in students' test suite revisions. This study examines the specific testing concepts that…

🌟 Astrocytes are superstars in the game of long-term memory
#memory

Astrocytes are superstars in the game of long-term memory
Why are we able to recall only some of our past experiences? A new study led by Jun Nagai at the RIKEN Center for Brain Science in Japan has an answer. Surprisingly, it turns out that the brain cells responsible for stabilizing memories aren't neurons. Rather, they are astrocytes, a type of glial cell that is usually thought of as a role player in the game of learning and memory.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

Exploring Teachers' Perceptions of ChatGPT Through Prompt Engineering
Dimitrios Gousopoulos
https://arxiv.org/abs/2510.08634 https://arxiv.org/pdf/2510…

Exploring Teachers' Perceptions of ChatGPT Through Prompt Engineering
Artificial Intelligence and especially Large Language Models (LLM), such as ChatGPT has revolutionized the way educators work. The results we get from LLMs depend on how we ask them to help us. The process and the technique behind an effective input is called prompt engineering. The aim of this study is to investigate whether science educators in secondary education improve their attitude toward ChatGPT as a learning assistant after appropriate training in prompt engineering. The results of the…

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Machine Learning-Driven Insights into Excitonic Effects in 2D Materials
Ahsan Javed, Sajid Ali

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization a...
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert Nowak, Yixuan Li
https://arxiv.org/abs/2306.09158
- Sparse, Efficient and Explainable Data Attribution with DualXDA
Galip \"Umit Yolcu, Moritz Weckbecker, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
https://arxiv.org/abs/2402.12118 https://mastoxiv.page/@arXiv_csLG_bot/111962593972369958
- HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Sun, Que, {\AA}rrestad, Loncar, Ngadiuba, Luk, Spiropulu
https://arxiv.org/abs/2405.00645 https://mastoxiv.page/@arXiv_csLG_bot/112370274737558603
- On the Identification of Temporally Causal Representation with Instantaneous Dependence
Li, Shen, Zheng, Cai, Song, Gong, Chen, Zhang
https://arxiv.org/abs/2405.15325 https://mastoxiv.page/@arXiv_csLG_bot/112511890051553111
- Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Yang Li, Daniel Agyei Asante, Changsheng Zhao, Ernie Chang, Yangyang Shi, Vikas Chandra
https://arxiv.org/abs/2405.15877 https://mastoxiv.page/@arXiv_csLG_bot/112517547424098076
- Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
Yan Shvartzshnaider, Vasisht Duddu
https://arxiv.org/abs/2409.03735 https://mastoxiv.page/@arXiv_csLG_bot/113089789682783135
- Low-Rank Filtering and Smoothing for Sequential Deep Learning
Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
https://arxiv.org/abs/2410.06800 https://mastoxiv.page/@arXiv_csLG_bot/113283021321510736
- Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng, Yucong Luo, Qi Liu, Enhong Chen
https://arxiv.org/abs/2410.18686 https://mastoxiv.page/@arXiv_csLG_bot/113367101100828901
- Fairness via Independence: A (Conditional) Distance Covariance Framework
Ruifan Huang, Haixia Liu
https://arxiv.org/abs/2412.00720 https://mastoxiv.page/@arXiv_csLG_bot/113587817648503815
- Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, et al.
https://arxiv.org/abs/2412.15184 https://mastoxiv.page/@arXiv_csLG_bot/113683924322164777
- Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy
Ishank Juneja, Carlee Joe-Wong, Osman Ya\u{g}an
https://arxiv.org/abs/2501.10290 https://mastoxiv.page/@arXiv_csLG_bot/113859392622871057
- Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar
https://arxiv.org/abs/2501.10321 https://mastoxiv.page/@arXiv_csLG_bot/113859392688054204
- Regularized Langevin Dynamics for Combinatorial Optimization
Shengyu Feng, Yiming Yang
https://arxiv.org/abs/2502.00277
- Generating Samples to Probe Trained Models
Eren Mehmet K{\i}ral, Nur\c{s}en Ayd{\i}n, \c{S}. \.Ilker Birbil
https://arxiv.org/abs/2502.06658 https://mastoxiv.page/@arXiv_csLG_bot/113984059089245671
- On Agnostic PAC Learning in the Small Error Regime
Julian Asilis, Mikael M{\o}ller H{\o}gsgaard, Grigoris Velegkas
https://arxiv.org/abs/2502.09496 https://mastoxiv.page/@arXiv_csLG_bot/114000974082372598
- Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo, Yongxu Zhu, Geoffrey Ye Li
https://arxiv.org/abs/2502.10784 https://mastoxiv.page/@arXiv_csLG_bot/114023667639951005
- On the Effect of Sampling Diversity in Scaling LLM Inference
Wang, Liu, Chen, Light, Liu, Chen, Zhang, Cheng
https://arxiv.org/abs/2502.11027 https://mastoxiv.page/@arXiv_csLG_bot/114023688225233656
- How to use score-based diffusion in earth system science: A satellite nowcasting example
Randy J. Chase, Katherine Haynes, Lander Ver Hoef, Imme Ebert-Uphoff
https://arxiv.org/abs/2505.10432 https://mastoxiv.page/@arXiv_csLG_bot/114516300594057680
- PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Christoffer Petersson, Daniel Persson, Jan E. Gerken
https://arxiv.org/abs/2505.17720 https://mastoxiv.page/@arXiv_csLG_bot/114572963019603744
- Train Sparse Autoencoders Efficiently by Utilizing Features Correlation
Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov, Nikita Balagansky
https://arxiv.org/abs/2505.22255 https://mastoxiv.page/@arXiv_csLG_bot/114589956040892075
- A Certified Unlearning Approach without Access to Source Data
Umit Yigit Basaran, Sk Miraj Ahmed, Amit Roy-Chowdhury, Basak Guler
https://arxiv.org/abs/2506.06486 https://mastoxiv.page/@arXiv_csLG_bot/114658421178857085
toXiv_bot_toot

The analogy theorem in Hoare logic
Nikitin Nikita
https://arxiv.org/abs/2510.03685 https://arxiv.org/pdf/2510.03685…

The analogy theorem in Hoare logic
The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification. The key problem is the lack of formal criteria to guarantee that a model trained on one type of data will retain its properties on another.This paper proposes a so…

msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
Florian Buchner, Johannes Sch\"orghuber, Nico Unglert, Jes\'us Carrete, Georg K. H. Madsen
https://arxiv.org/abs/2510.05961 …

msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
We present msmJAX, a Python package implementing the multilevel summation method with B-spline interpolation, a linear-scaling algorithm for efficiently evaluating electrostatic and other long-range interactions in particle-based simulations. Built on the JAX framework, msmJAX integrates naturally with the machine-learning methods that are transforming chemistry and materials science, while also serving as a powerful tool in its own right. It combines high performance with Python's accessibilit…

Quantum Fisher information matrices from R\'enyi relative entropies
Mark M. Wilde
https://arxiv.org/abs/2510.02218 https://arxiv.org/pdf/2510.02218

Quantum Fisher information matrices from Rényi relative entropies
Quantum generalizations of the Fisher information are important in quantum information science, with applications in high energy and condensed matter physics and in quantum estimation theory, machine learning, and optimization. One can derive a quantum generalization of the Fisher information matrix in a natural way as the Hessian matrix arising in a Taylor expansion of a smooth divergence. Such an approach is appealing for quantum information theorists, given the ubiquity of divergences in qua…

Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Vaishnavi Sharma, Rakesh Thakur, Shashwat Sharma, Kritika Panjanani
https://arxiv.org/abs/2509.25258

Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Practical lab education in computer science often faces challenges such as plagiarism, lack of proper lab records, unstructured lab conduction, inadequate execution and assessment, limited practical learning, low student engagement, and absence of progress tracking for both students and faculties, resulting in graduates with insufficient hands-on skills. In this paper, we introduce AsseslyAI, which addresses these challenges through online lab allocation, a unique lab problem for each student, …

Efficient Learning-based Graph Simulation for Temporal Graphs
Sheng Xiang, Chenhao Xu, Dawei Cheng, Xiaoyang Wang, Ying Zhang
https://arxiv.org/abs/2510.05569 https://

Efficient Learning-based Graph Simulation for Temporal Graphs
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed…

A PCA-based Data Prediction Method
Peteris Daugulis, Vija Vagale, Emiliano Mancini, Filippo Castiglione
https://arxiv.org/abs/2510.09246 https://arxiv.org/…

A PCA-based Data Prediction Method
The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of th…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/8]:
- Zephyrus: An Agentic Framework for Weather Science
Sumanth Varambally, et al.
ht…

ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Luke Thompson, Davy Guan, Dai Shi, Slade Matthews, Junbin Gao, Andi Han
https://arxiv.org/abs/2510.05482 http…

ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Molecular dynamics (MD) simulations underpin modern computational drug dis- covery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predictions without the need to repeatedly solve quantum mechanical forces, enabling significant speedups over conventional pipelines. Yet many such methods typically enforce strict equivariance and rely on sequential rollouts, thus limiting their flexibility and simulation efficiency. They are also com- monly single-tas…

Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
Yang Cao, Zhao Song, Jiahao Zhang, Jiale Zhao
https://arxiv.org/abs/2510.05494

Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
Graph neural networks (GNNs) have become a core paradigm for learning on relational data. In materials science, equivariant GNNs (EGNNs) have emerged as a compelling backbone for crystalline-structure prediction, owing to their ability to respect Euclidean symmetries and periodic boundary conditions. Despite strong empirical performance, their expressive power in periodic, symmetry-constrained settings remains poorly understood. This work characterizes the intrinsic computational and expressive…