Healthcare operations data is rarely “a dataset.” It is a living system. Forms change, codes evolve, staff enter data differently across sites, and upstream systems get patched without warning. If you train a model on top of that without guardrails, you do not have an ML pipeline. You have a one-time experiment. This post is a concise, real-world pipeline for turning messy healthcare ops data into ML-ready features you can trust, rerun, and explain. Treat data quality as […]

Ver mais

arXiv:2603.06704v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) that directly process RGB inputs for tasks like 3D localization and navigation have shown remarkable potential. However, we argue that these RGB-only approaches are fundamentally flawed in their ability to generalize across cameras. By ignoring camera parameters, they entangle an object’s physical properties with the camera’s perspective, creating an irresolvable ambiguity. We show this leads MLLMs to overfit to the training camera distribution, rather than learning true and […]

Ver mais

arXiv:2603.25864v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop). While prior research has primarily focused on automating user actions through clicks and keystrokes, this paradigm overlooks human intention, where users value the ability to explore, iterate, and refine their ideas while maintaining agency. To move beyond automation and toward collaboration, GUI agents must understand what users are doing and why. We introduce GUIDE […]

Ver mais

arXiv:2603.05483v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE-Bench, the first comprehensive benchmark for HTE estimation with censored […]

Ver mais

Key Highlights What’s Really New in One R3? Opera has just released One R3 on Thursday, with new AI features & redesign to its flagship browser. The update focuses on “agentic” AI capabilities and a modular interface designed to streamline complex browsing sessions. The release marks the third annual “rebirth” of the Opera One platform. With this update, Opera focuses to shift towards an AI centric browser built on top of the Opera Neon browser. The company claims […]

Ver mais

Machine learning models have demonstrated remarkable success in sports prediction in the past years, often treating sports prediction as a classification task within the field. This paper introduces new perspectives for analyzing sports data to predict outcomes more accurately. We leverage rankings to generate team rankings for the 2024 dataset using Combinatorial Fusion Analysis (CFA), a new paradigm for combining multiple scoring systems through the rank-score characteristic (RSC) function and cognitive diversity (CD). Our result based on rank […]

Ver mais

arXiv:2602.09207v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in a wide range of sequential decision-making problems. Recent diffusion-based policies further improve RL by modeling complex, high-dimensional action distributions. However, existing diffusion policies primarily rely on statistical associations and fail to explicitly account for causal relationships among states, actions, and rewards, limiting their ability to identify which action components truly cause high returns. In this paper, we propose Causality-guided Diffusion Policy (CausalGDP), a unified framework […]

Ver mais

arXiv:2601.09865v1 Announce Type: new Abstract: Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models requires addressing three key challenges: acquiring task-specific data, fine-tuning for performance, and compressing models to accelerate inference while reducing resource demands. We propose an integrated framework combining GPTQ-based quantization, low-rank adaptation (LoRA), and a specialized data distillation process to significantly reduce model size and complexity […]

Ver mais