Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
arXiv:2602.20344v1 Announce Type: new Abstract: Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However,...
Momentum Guidance: Plug-and-Play Guidance for Flow Models
arXiv:2602.20360v1 Announce Type: new Abstract: Flow-based generative models have become a strong framework for high-quality generative modeling, yet pretrained models are rarely used in their vanilla conditional form: conditional samples without guidance often appear diffuse and lack fine-grained detail due...
Quantitative Approximation Rates for Group Equivariant Learning
arXiv:2602.20370v1 Announce Type: new Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitative approximation rates for ReLU networks on the class of $\alpha$-H\"older functions...
cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that...
Wasserstein Distributionally Robust Online Learning
arXiv:2602.20403v1 Announce Type: new Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambiguity set centered at past observations. While this paradigm is well understood in...
CITED: A Decision Boundary-Aware Signature for GNNs Towards Model Extraction Defense
arXiv:2602.20418v1 Announce Type: new Abstract: Graph neural networks (GNNs) have demonstrated superior performance in various applications, such as recommendation systems and financial risk management. However, deploying large-scale GNN models locally is particularly challenging for users, as it requires significant computational...
CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
arXiv:2602.20419v1 Announce Type: new Abstract: Machine Learning as a Service (MLaaS) has emerged as a widely adopted paradigm for providing access to deep neural network (DNN) models, enabling users to conveniently leverage these models through standardized APIs. However, such services...
GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
arXiv:2602.20427v1 Announce Type: new Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on...
Imputation of Unknown Missingness in Sparse Electronic Health Records
arXiv:2602.20442v1 Announce Type: new Abstract: Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges...
Oracle-Robust Online Alignment for Large Language Models
arXiv:2602.20457v1 Announce Type: new Abstract: We study online alignment of large language models under misspecified preference feedback, where the observed preference oracle deviates from an ideal but unknown ground-truth oracle. The online LLM alignment problem is a bi-level reinforcement problem...
Nonparametric Teaching of Attention Learners
arXiv:2602.20461v1 Announce Type: new Abstract: Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences to their corresponding properties, e.g., mapping a given sequence of tokens to the probability of...
A Long-Short Flow-Map Perspective for Drifting Models
arXiv:2602.20463v1 Announce Type: new Abstract: This paper provides a reinterpretation of the Drifting Model~\cite{deng2026generative} through a semigroup-consistent long-short flow-map factorization. We show that a global transport process can be decomposed into a long-horizon flow map followed by a short-time terminal...
Elimination-compensation pruning for fully-connected neural networks
arXiv:2602.20467v1 Announce Type: new Abstract: The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to the vast amount of parameters that characterize model...
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
arXiv:2602.20468v1 Announce Type: new Abstract: Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover,...
Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
arXiv:2602.20492v1 Announce Type: new Abstract: Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via...
A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies
arXiv:2602.20527v1 Announce Type: new Abstract: Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) have advanced rapidly in recent years and have been successfully applied to e-learning environments like intelligent tutoring systems (ITSs). Despite great success, the broader application of DRL...
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences,...
Sample-efficient evidence estimation of score based priors for model selection
arXiv:2602.20549v1 Announce Type: new Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measurements $y$ to avoid severe bias. In Bayesian inverse problems, this could be achieved...
GENSR: Symbolic Regression Based in Equation Generative Space
arXiv:2602.20557v1 Announce Type: new Abstract: Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modifications of equations rarely align with their numerical behavior, leaving fitting...
Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphs
arXiv:2602.20567v1 Announce Type: new Abstract: Push-Sum-based decentralized learning enables optimization over directed communication networks, where information exchange may be asymmetric. While convergence properties of such methods are well understood, their finite-iteration stability and generalization behavior remain unclear due to structural...
Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis
arXiv:2602.20573v1 Announce Type: new Abstract: Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as feature vectors to train ML/DL models for molecular property prediction tasks in the field of...
Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
arXiv:2602.20578v1 Announce Type: new Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer from suboptimal regret and limited feedback guarantees. Our main contribution is a new structural result...
Justices send litigation about tainted baby food back to state court
Yesterday’s decision in The Hain Celestial Group v Palmquist resolves a technical problem about what to do when district courts make a mistaken ruling about their own jurisdiction. The final […]The postJustices send litigation about tainted baby food back to...
Justices reveal little about whether the deadline for removing cases to federal court can be excused
When a plaintiff files a lawsuit in state court asserting a claim that could be brought in federal court, federal law gives the defendant 30 days to remove the case […]The postJustices reveal little about whether the deadline for removing...
SCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Union
Another day, another live blog. Join us to discuss the possible announcement of opinions this morning beginning at 9:30 a.m. EST.The postSCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Unionappeared first onSCOTUSblog.
Judge doesn't trust DOJ with search of devices seized from Wash. Post reporter
Court to search devices itself instead of letting government have full access.
Salesforce CEO Marc Benioff: This isn’t our first SaaSpocalypse
Salesforce reported a solid year-end earnings and then pulled out all the stops to ward off more talk of the death of its business to AI.
Gushwork bets on AI search for customer leads — and early results are emerging
Gushwork has raised $9 million in a seed round led by SIG and Lightspeed. The startup has seen early customer traction from AI search tools like ChatGPT.
Nvidia has another record quarter amid record capex spends
"The demand for tokens in the world has gone completely exponential," Nvidia CEO Jensen Huang said about the company's earnings.
Alphabet-owned robotics software company Intrinsic joins Google
Nearly five years after graduating into an independent Alphabet company, Intrinsic is moving under Google's domain.