arXiv:2602.05798v1 Announce Type: cross Abstract: Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable guarantees are often overly conservative, creating a persistent gap between the realized false discovery proportion (FDP) and the target FDR level. We introduce a learning-augmented enhancement of the T-Rex Selector framework that narrows this gap. Our approach replaces the analytical FDP estimator with a neural network trained solely on diverse synthetic […]

Ver mais

Missed a post last week due to the Thanksgiving long weekend :-). We had gone to San Francisco to see the city and try out a couple of hikes). Just FYI – strolling around SF is also as much a hike as any of the real trails at Mt Sutro – with all the uphill & downhill roads! As for Robotics, I am currently on Week 3 of the Mobility course, which is more of physics than ‘computer […]

Ver mais

Climate change poses significant challenges to agricultural practices worldwide, affecting crop yields, food insecurity, and rural livelihoods. Maize crop farming is particularly vulnerable due to extreme weather conditions such as high rainfall, high temperature, soil acidity, humidity and unproper irrigation affecting crop yield and consider to be a source of hunger and food security concern. The aim of the study was to propose a reliable and accurate machine learning techniques to be used in the prediction of maize […]

Ver mais

arXiv:2601.09836v1 Announce Type: new Abstract: Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability (SWC-120) that has led to real-world exploits. Current detection tools identify only simple patterns and fail to verify whether protective modifiers actually guard vulnerable code. A major obstacle to improving these tools is the lack of large, accurately […]

Ver mais

arXiv:2601.21049v1 Announce Type: new Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy recall process of a latent target item. To address this, we propose QUARK, a simple yet effective training-free framework for robust retrieval under non-faithful queries. QUARK explicitly models query uncertainty through recovery hypotheses, i.e., multiple […]

Ver mais

arXiv:2601.20868v1 Announce Type: new Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on manual design, this LLM-Driven Heuristic Design (LHD) process leverages LLMs to iteratively generate and refine solvers to achieve high performance. However, existing LHD frameworks face two critical limitations: (1) Endpoint-only evaluation, which ranks solvers solely by final quality, ignoring the convergence process and runtime efficiency; (2) High adaptation costs, where distribution shifts necessitate re-adaptation […]

Ver mais

arXiv:2510.07473v2 Announce Type: replace-cross Abstract: Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference gives an estimate of uncertainty but is analytically intractable and requires costly approximation using Markov Chain Monte Carlo (MCMC) methods. Neural posterior estimation shifts the bulk of computation from inference time to pre-training time, amortizing over simulated datasets with known ground truth targets. We propose metabeta, a neural network […]

Ver mais

arXiv:2601.18897v1 Announce Type: new Abstract: Wastewater treatment plants consume 1-3% of global electricity, making accurate energy forecasting critical for operational optimization and sustainability. While machine learning models provide point predictions, they lack explainable uncertainty quantification essential for risk-aware decision-making in safety-critical infrastructure. This study develops an Interval Type-2 Adaptive Neuro-Fuzzy Inference System (IT2-ANFIS) that generates interpretable prediction intervals through fuzzy rule structures. Unlike black-box probabilistic methods, the proposed framework decomposes uncertainty across three levels: feature-level, footprint of uncertainty […]

Ver mais

I’ve repeatedly run into the same issue when working with ML / NLP systems (and more recently LLM-based ones): there often isn’t a single correct answer – only better or worse behavior – and small changes can have non-local effects across the system. Traditional testing approaches (assertions, snapshot tests, benchmarks) tend to break down here: failures don’t explain what changed evaluation is expensive tests become brittle or get ignored We ended up building a review-driven regression testing approach […]

Ver mais