Recent agentic time series forecasting (TSF) approaches use large language models (LLMs) to autonomously orchestrate analytical and forecasting tools and reason about temporal patterns using the resulting evidence. However, many such approaches rely on powerful LLMs in training-free, prompt-driven workflows, leaving op...
Xiaoyu Tao, Mingyue Cheng, Yaguo Liu et al.· 0 citations
An algorithm to fit Poisson NMF with the shifted-log link function by maximum likelihood, and an approximation that substantially reduces computation time for large, sparse datasets (computations scale with the number of non-zero entries in the data matrix).
Eric Weine, P. Carbonetto, Rafael A. Irizarry et al.· arXiv.org· 0 citations
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in safety-critical settings. Traditional neural networks often fail to detect out-of-domain (OOD) data and may output confident yet incorrect pr...
Bernhard Klein, Falk Selker, Hendrik Borras et al.· 0 citations
Multi-task learning in contextual bandits has attracted significant research interest due to its potential to enhance decision-making across multiple related tasks by leveraging shared structures and task-specific heterogeneity. In this article, we propose a novel hierarchical Bayesian framework for learning in various...
Xia Jiang, Rong J. B. Zhu· 0 citations
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Image- and audio-based remote IoT applications offer a substantial opportunity to address ecological changes. However, to be effective, they must operate for long periods on limited power while also providing data via LPWAN. Previous studies have investigated the energy efficiency of isolated components such as radio t...
Benjamin Karic, Nina Herrmann, Jan Stenkamp et al.· 0 citations
Neural operators (NOs) are a class of deep learning models designed to simultaneously solve infinitely many related problems by casting them into an infinite-dimensional space, whereon these NOs operate. Such operator learning problems arise naturally in mathematical finance, for instance, when learning families of opt...
Anastasis Kratsios, Ariel Neufeld, Philipp Schmocker· 0 citations
Recent breakthroughs in generative models have shown great promise for drug discovery, yet Structure-Based Drug Design (SBDD) faces two critical challenges: the scarcity of high-quality protein-ligand complex data and the practical synthesizability of generated molecules. While ligand-based approaches benefit from bill...
Recent work shows that distinct inputs produce distinct hidden representations in LLMs, so the input can in principle be recovered from them. Yet, no existing method can reliably do so from the model's output text or logits alone. We introduce Sparse One-hot Discrete Adam (SODA), a white-box search-based algorithm that...
Adrians Skapars, Edoardo Manino, Youcheng Sun et al.· 0 citations
Path reasoning graph neural networks have demonstrated state-of-the-art performance on knowledge graph completion tasks such as link prediction. However, interpreting their predictions remains a challenging open problem, as most GNN explainability methods target node-level or graph-level tasks. In this paper, we propos...
Predictors often exploit spurious correlations that fail under distribution shift, undermining out-of-distribution (OOD) robustness. Invariant pairs, such as the same object photographed against different backgrounds, directly indicate which variation should not affect prediction. Yet real pair collections are finite a...
Ruqi Bai, Easton Currie, Yao Ji et al.· 0 citations
Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress, we propose a novel diffusion-based approach for symbolic regression. We construct a random mask-based diffusion and denoising process to ge...
Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter et al.· 0 citations
This work proposes TRIP-Shapley, a propagation-aware Shapley-inspired surrogate for contribution attribution in P2PFL with a model-free coordinator used solely for auditing, and introduces lightweight mechanisms to detect and mitigate misreporting attacks.
Honoka Anada, Tatsuya Kaneko, Ya-Fei Ou et al.· 0 citations
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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