Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort.

Data valuation techniques quantify each training example's contribution to model performance, providing a principled basis for data cleaning, acquisition, and selection. Existing valuation methods remain inadequate: model-based techniques depend on a single fitted model and inherit its biases, while algorithm-based approaches like Data Shapley scale poorly due to their need to train multiple models. Recent work has proposed model-agnostic alternatives based on Wasserstein distance between the training set and a clean reference set, but exact computation is expensive and approximations often misrank examples. We introduce KAIROS, a model-agnostic framework that values examples by their contribution to the Maximum Mean Discrepancy (MMD) between the training set and a clean reference distribution. Unlike Wasserstein methods, MMD admits a closed-form solution that requires no approximations and is scalable to large datasets. Additionally, KAIROS enables efficient online valuation: adding a new batch of m examples requires only O(mN)computation to update all scores, compared to O(N2)in prior work where N is the training set size. Empirical evaluations on noise, mislabeling, and poisoning benchmarks show that KAIROS consistently outperforms state-of-the-art baselines in both accuracy and runtime. On ImageNet, KAIROS achieves up to 15 speedup over the fastest baseline while maintaining superior data valuation quality. Our results demonstrate that model-agnostic methods can match or exceed model-based approaches in performance while scaling to large datasets.

Data Shapley values provide a principled approach for quantifying the contribution of individual training examples to machine learning models. However, computing these values often requires computational complexity that is exponential in the data size, and this has led researchers to pursue efficient algorithms tailored to specific machine learning models. Building on the prior success of the Shapley valuation for K-nearest neighbor (KNN) models, in this paper, we introduce a localized data Shapley framework that significantly accelerates the valuation of data points.

We study the online multi-class selection problem with group fairness guarantees, where limited resources must be allocated to sequentially arriving agents.

Differentiable economics--the use of deep learning for auction design--has driven progress in multi-item auction design with additive and unit-demand valuations. However, there has been little progress for combinatorial auctions (CAs), even in the simplest and yet important single bidder case, due to exponential growth of the bundle space with the number of items. We address this challenge by introducing a deep network architecture for a menu-based CA, which supports the first dominantstrategy incentive compatible (DSIC), revenue-optimizing single-bidder CA. Our idea is to generate a bundle distribution through an ordinary differential equation (ODE) applied to a tractable initial distribution. Our method, BUNDLEFLOW, learns suitable ODE-based transforms, one for each menu element, to optimize expected revenue. BUNDLEFLOW achieves up to 2.23 higher revenue than baselines on standard CA testbeds and scales up to 500 items.

SpaceX to list on US stock market at $1.77tn valuation in largest ever debut IPO for Elon Musk's company comes in what is predicted to be a banner year for public offerings of AI companies SpaceX will become publicly traded on Friday after nearly two and a half decades as a private company. Executives are slated to ring the bell on Wall Street with the rocket ship maker's historic stock market debut. If all goes to plan, the company's initial public offering (IPO) will mint a valuation of $1.77tn - earning it the designation of the world's largest ever IPO. Elon Musk, the founder and CEO of SpaceX, has a large stake in the company as majority shareholder, so if investors' enthusiasm validates the eye-popping valuation, he would take the title of the world's first-ever trillionaire. Musk is also the CEO of Tesla, which is valued at $1.2tn.

Data Shapley values provide a principled approach for quantifying the contribution of individual training examples to machine learning models. However, computing these values often requires computational complexity that is exponential in the data size, and this has led researchers to pursue efficient algorithms tailored to specific machine learning models. Building on the prior success of the Shapley valuation for $K$-nearest neighbor (KNN) models, in this paper, we introduce a localized data Shapley framework that significantly accelerates the valuation of data points.

SpaceX's stock market blast-off could be Musk's biggest gamble yet It's 07:25 am, 13 October 2024, at Starbase, near Boca Chica on the Texas side of the US/Mexico border, and on the launch pad stands the biggest rocket ever made. Its engines fire and it climbs into the skies over the Gulf of Mexico to cheers and screams in the SpaceX control room. But the launch is not the main event. What goes up must come down - and how it comes down will become a milestone in space exploration. Seven minutes later, the massive rocket booster that blasted the craft towards space starts falling back to Earth - until its engines reignite as planned.

Anthropic has usurped OpenAI as the world's most valuable artificial intelligence startup, soaring to a $965bn valuation ahead of expected public listings by the rival firms. Anthropic, the maker of the Claude family of chatbots, said on Thursday that it had raised $65bn from private investors after a fundraising round led by Altimeter Capital, Greenoaks, Dragoneer and Sequoia Capital. "This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens," Anthropic's Chief Financial Officer Krishna Rao said in a statement. Altimeter Capital CEO Brad Gerstner hailed the adoption of Claude among the "world's most demanding organisations" as evidence of Anthropic's command in the field. "This momentum positions Anthropic to lead the next phase of AI innovation and capture the enormous opportunity ahead," Gerstner said.

Musk v. Altman week 3: Elon Musk and Sam Altman traded blows over each other's credibility. Now the jury will pick a side. The trial spilled plenty of dirt--and raised more questions than answers about how the AI giant should be governed. In the final week of the trial, lawyers traded blows over Elon Musk's and OpenAI CEO Sam Altman's credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)--powerful AI that can compete with humans on most cognitive tasks.