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18,068 papers

#machine learning Preprint Open access Oct 2026

Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer

A two-dimensional electron gas in a uniform neutralizing background forms a triangular Wigner crystal at low density. In a strongly mass-asymmetric electron-hole bilayer, localized heavy holes instead provide a nonuniform positive background that supports an electron crystal at densities two orders of magnitude higher...

Shengdu Chai, Wangqian Miao, Yuqiang Li et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates

We study multi-agent terminal interception when one of several moving candidates is the unknown target and both target identity and candidate terminal locations are uncertain. We develop a receding-horizon deferred-decision trajectory optimization (DDTO) controller that combines conformal terminal-location prediction,...

Farbod Siahkali, Mengxue Hou, Vijay Gupta · 0 citations
#machine learning Preprint Oct 2026

Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals

Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which cannot tell whether someone approaching a transition can be identified in advance. We instead frame help-seeking as a time-to-event problem and ask which Reddit users will...

Nisar Ahmed, Ammara Kazmi, Irtaza Bashir Raja et al. · 0 citations
#machine learning Preprint Open access Oct 2026

DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers

Understanding how many aircraft, and which traffic configurations, can be safely and efficiently managed by air traffic controllers within a given airspace is fundamental to air traffic management. Air traffic controllers continuously modify aircraft trajectories through interventions such as speed advisories and radar...

Seokbin Yoon, Kyungmin Kim, Taejung Kwon et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization

We develop a comprehensive theoretical analysis of Matrix Shampoo for tracking time-varying optima in nonstationary stochastic optimization. We derive finite-horizon high-probability bounds for average tracking error that disentangle initialization, optimizer drift, momentum-induced drift penalty, stochastic noise, and...

Abir Sarkar, Nirmalya Mandal, Martin T. Wells · 0 citations
#machine learning Preprint Open access Oct 2026

Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning

Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misaligned with neural events. Existing window optimization methods often evaluate windows independently, overlooking temporal dependencies and potentially producing fragmented o...

Huan Luo, Na Lu, Xiaopeng Wang et al. · 0 citations
#machine learning Review Oct 2026

ML Engineering Agents - A Data Management Perspective

Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language models for code generation and predictive performance as a machine-gradable feedback signal, these agents iteratively synthesize, execute, and refine pipeline code. Yet curren...

Arnab Phani, E. Strauss, Tim Schuster et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers

Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space. Existing VA-based image editors are typically training-based and domain-specific, limiting them to VA regions seen during training and making them brittle across datasets and domains. Meanwhile, despite producing high-q...

Harvey Mannering, Zhiwu Huang, Adam Pr\"ugel-Bennett · 0 citations
#machine learning Preprint Oct 2026

FORGE: Procedural Weight Generation for Sparse MoE Inference

We introduce FORGE (Fast On-chip Rotor Generation for Experts), a procedural weight generation system for Sparse Mixture-of-Experts (MoE) inference. Rather than storing and loading every expert weight tensor from HBM, we show how a compact accelerator inside each Streaming Multiprocessor can regenerate them on-the-fly...

Chan-Kyoung Moon, T. Pence, Daisuke Yamada et al. · 0 citations
#machine learning Preprint Oct 2026

Better together? A performance theory of human-machine skill policies under generalization difficulty

Organizations pairing a human and a machine on a task decide, often by default, which executes each instance and what happens when both do. We account for what this attains under the squared rule, with performance distributed over instances to capture generalization difficulty. A gross stake lies between the skills'mea...

Riccardo Zanardelli · 0 citations
#machine learning Preprint Open access Oct 2026

Feature Normalization under Finite Training Budgets

Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learning. We characterize the lowest rare-subgroup risks attainable by standardized and unscaled training when each chooses a nonnegative constant learning rate and a determin...

Johan Larsson · 0 citations
#machine learning Preprint Oct 2026

Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling

In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets because their parameterizations do not specify how posterior location, uncertainty and shape must change as observations accumulate. We propose center-estimated NPE (CE-NP...

Shi-Yi Sun, Lei Fang, Geoff K. Nicholls · 0 citations

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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