Jean-Paul Thorbjornsen is a leader of THORChain, a blockchain that is not supposed to have any leaders--and is reeling from a series of expensive controversies. We can do whatever we want," Jean-Paul Thorbjornsen tells me from the pilot's seat of his Aston Martin helicopter. As we fly over suburbs outside Melbourne, Australia, it's becoming clear that doing whatever he wants is Thorbjornsen's MO. Upper-middle-class homes give way to vineyards, and Thorbjornsen points out our landing spot outside a winery. "They're going to ask for a shot now," he says, used to the attention drawn by his luxury helicopter, emblazoned with the tail letters "BTC" for bitcoin (the price tag of $5 million in Australian dollars--$3.5 million in US dollars today--was perhaps reasonable for someone who claims a previous crypto project made more than AU$400 million, although he also says those funds were tied up in the company). Thorbjornsen is a founder of THORChain, a blockchain through which users can swap ...

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information.

Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels. Current state of the art approaches that do not use any labeled training data, however, require two separate modeling steps: Learning a probabilistic latent variable model based on the WS sources - making assumptions that rarely hold in practice - followed by downstream model training. Importantly, the first step of modeling does not consider the performance of the downstream model. To address these caveats we propose an end-to-end approach for directly learning the downstream model by maximizing its agreement with probabilistic labels generated by reparameterizing prior probabilistic posteriors with a neural network. Our results show improved performance over prior work in terms of end model performance on downstream test sets, as well as in terms of improved robustness to dependencies among weak supervision sources.

An Iranian drone was shot down as it aggressively approached an American aircraft carrier in the Arabian Sea on Tuesday, a US military spokesman has said. An F-35C stealth fighter jet which took off from the USS Abraham Lincoln warship shot down the drone in self-defence to protect the aircraft carrier and its personnel, US Central Command spokesman Capt Tim Hawkins said. The ship was approximately 500 miles from the Iranian coast when the drone approached it with unclear intent. No US service members were harmed and no equipment was damaged. It comes as the US continues to build up a military presence in the region, with tensions high between Washington and Tehran.

Large-scale AI evaluation increasingly relies on aggregating binary judgments from $K$ annotators, including LLMs used as judges. Most classical methods, e.g., Dawid-Skene or (weighted) majority voting, assume annotators are conditionally independent given the true label $Y\in\{0,1\}$, an assumption often violated by LLM judges due to shared data, architectures, prompts, and failure modes. Ignoring such dependencies can yield miscalibrated posteriors and even confidently incorrect predictions. We study label aggregation through a hierarchy of dependence-aware models based on Ising graphical models and latent factors. For class-dependent Ising models, the Bayes log-odds is generally quadratic in votes; for class-independent couplings, it reduces to a linear weighted vote with correlation-adjusted parameters. We present finite-$K$ examples showing that methods based on conditional independence can flip the Bayes label despite matching per-annotator marginals. We prove separation results demonstrating that these methods remain strictly suboptimal as the number of judges grows, incurring nonvanishing excess risk under latent factors. Finally, we evaluate the proposed method on three real-world datasets, demonstrating improved performance over the classical baselines.