Enterprise adoption is shifting from “capability” to “credibility.” Organizations without strong oversight, documentation, and risk management risk losing trust and market momentum. Are you ready?
arXiv:2603.02365v1 Announce Type: new
Abstract: The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distingu...
Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach
arXiv:2603.02359v1 Announce Type: new
Abstract: Digital advertising increasingly relies on visual content, yet marketers lack rigorous methods for understanding how specific visual attributes causally affect consumer engagement. This paper addresses a fundamental methodological challenge: estimatin...
Microsoft research lead Doug Burger introduces his new podcast series, The Shape of Things to Come, an exploration into the fundamental truths about AI and how the technology will reshape the future.
The post Trailer: The Shape of Things to Come appeared first on Microsoft Research.
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
arXiv:2603.00041v1 Announce Type: new
Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time ser...
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
arXiv:2603.00037v1 Announce Type: new
Abstract: Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that deviates from the near noise assump...
EMBridge: Enhancing Gesture Generalization from EMG Signals through Cross-Modal Representation Learning
Hand gesture classification using high-quality structured data such as videos, im-
ages, and hand skeletons is a well-explored problem in computer vision. Alterna-
tively, leveraging low-power, cost-effective bio-signals, e.g., surface electromyo-
graphy (sEMG), allows for continuous gesture predict...
ChatGPT as a therapist? New study reveals serious ethical risks
As millions turn to ChatGPT and other AI chatbots for therapy-style advice, new research from Brown University raises a serious red flag: even when instructed to act like trained therapists, these systems routinely break core ethical standards of mental health care. In side-by-side evaluations with ...
Construct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman Problem
arXiv:2602.23579v1 Announce Type: new
Abstract: The Multiple Traveling Salesman Problem (mTSP) extends the Traveling Salesman Problem to m tours that start and end at a common depot and jointly visit all customers exactly once. In the min-max variant, the objective is to minimize the longest tour, ...
Causal Identification from Counterfactual Data: Completeness and Bounding Results
arXiv:2602.23541v1 Announce Type: new
Abstract: Previous work establishing completeness results for $\textit{counterfactual identification}$ has been circumscribed to the setting where the input data belongs to observational or interventional distributions (Layers 1 and 2 of Pearl's Causal Hierarch...
Causal Direction from Convergence Time: Faster Training in the True Causal Direction
arXiv:2602.22254v1 Announce Type: new
Abstract: We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics in which one neural network is trained to predict $Y$ from $X$ and another to predict $X$ from $Y$, and the direction tha...
Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals
arXiv:2602.22228v1 Announce Type: new
Abstract: Stroke affected millions annually, yet poor symptom recognition often delayed care-seeking. To address risk recognition gap, we developed a passive surveillance system for early stroke risk detection using patient-reported symptoms among individuals w...
From Privacy to ‘Glass Box’ AI, Stanford Students Are Targeting Real-World Problems
An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.
Urban Vibrancy Embedding and Application on Traffic Prediction
arXiv:2602.21232v1 Announce Type: new
Abstract: Urban vibrancy reflects the dynamic human activity within urban spaces and is often measured using mobile data that captures floating population trends. This study proposes a novel approach to derive Urban Vibrancy embeddings from real-time floating p...
Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children's Health
arXiv:2602.20303v1 Announce Type: new
Abstract: Background: Childhood and adolescent overweight and obesity remain major public health concerns in the United States and are shaped by behavioral, household, and community factors. Their joint predictive structure at the population level remains incom...
Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series
arXiv:2602.18473v1 Announce Type: new
Abstract: Accurate analysis of medical time series (MedTS) data, such as electroencephalography (EEG) and electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhib...
Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting
arXiv:2602.18465v1 Announce Type: new
Abstract: Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series f...
Support Vector Data Description for Radar Target Detection
arXiv:2602.18486v1 Announce Type: new
Abstract: Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better mode...
arXiv:2602.18494v1 Announce Type: new
Abstract: A dominant paradigm in visual intelligence treats semantics as a static property of latent representations, assuming that meaning can be discovered through geometric proximity in high dimensional embedding spaces. In this work, we argue that this view...
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. The compiler operates at the Python bytecode level, making it appear as an opaque box. To addres...
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
arXiv:2602.17826v1 Announce Type: new
Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontol...