We study the problem of learning general (i.e., not necessarily homogeneous) halfspaces under the Gaussian distribution on $\mathbb{R}^d$ in the presence of some form of query access. In the classical pool-based active learning model, where the algorithm isallowed to make adaptive label queries to previously sampled points, we establish a strong information-theoretic lower bound ruling out non-trivialimprovements over the passive setting. Specifically, we show thatany active learner requires label complexity of $\tilde{\Omega}(d/(\log(m)\epsilon))$, where $m$ is the number of unlabeled examples. Specifically, to beat the passive label complexity of $\tilde{O}(d/\epsilon)$, an active learner requires a pool of $2^{\mathrm{poly}(d)}$ unlabeled samples.On the positive side, we show that this lower bound can be circumvented with membership query access, even in the agnostic model.

Wizards of the Coast are seeking'a say in layoffs, accountability and a living wage.' The CWA says it has secured a supermajority among workers in favor of unionization for the chapter, called United Wizards of the Coast (UWOTC-CWA). The CWA has filed for a formal election with the National Labor Relations Board (NLRB), but that will be withdrawn if Hasbro voluntarily recognizes the union by May 1st. At Wizards, we're organizing for a say in layoffs, accountability that runs up and down the chain, and a living wage that actually lets people build a life, said UWOTC-CWA member and senior software engineer Damien Wilson. I'm hopeful about what we can build here and being clear-eyed about why it's necessary. Workers have outlined several areas of concern including protections over layoffs and remote work, generative AI guardrails and mandatory crunch time, along with increased transparency and equity in the workplace.

The bitter feud between Elon Musk and OpenAI boss Sam Altman has raged for years, but has mostly played out online in the form of accusations, counter-accusations and jibes. But starting on Tuesday, the beef between the two tech billionaires will shift to a much higher-profile forum: a federal courtroom in California, where their row will be the focus of a month-long trial. Being considered is Musk's claim that Altman - with whom he founded OpenAI - has swindled him out of millions of dollars and reneged on the ChatGPT-maker's original non-profit mission. Musk and Altman themselves will be among those to testify in a case in which the future of AI could be at stake. And while one will presumably emerge the winner, it's plausible that neither will emerge from the saga unscathed.

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Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving minimization problems. In this paper, we provide a comprehensive generalization analysis of MC-SGMs for both minimization and minimax problems through the lens of algorithmic stability in the framework of statistical learning theory. For empirical risk minimization (ERM) problems, we establish the optimal excess population risk bounds for both smooth and non-smooth cases by introducing on-average argument stability. For minimax problems, we develop a quantitative connection between on-average argument stability and generalization error which extends the existing results for uniform stability [38]. We further develop the first nearly optimal convergence rates for convex-concave problems both in expectation and with high probability, which, combined with our stability results, show that the optimal generalization bounds can be attained for both smooth and non-smooth cases. To the best of our knowledge, this is the first generalization analysis of SGMs when the gradients are sampled from a Markov process.