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

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

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

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself.

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

The design of revenue-maximizing combinatorial auctions, i.e. multi item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, automated mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable automated mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class.

Agentic AI is poised to take on a much more central role in the IT industry, according to a new report from Cisco. The report, titled "The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience," surveyed close to 8,000 business leaders across 30 countries, all of whom routinely work closely with customer service professionals from B2B technology services. In broad strokes, it paints a picture of a business landscape eager to embrace the rising wave of AI agents, particularly when it comes to customer service. Also: Can you build a billion-dollar business with only AI agents (yet)? As soon as next year, according to the report, over half (68%) of all customer service and support interactions with tech vendors could become automated, thanks to agentic AI.

We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with M \geq 3 components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians. We prove that the log-likelihood value of these bad local maxima can be arbitrarily worse than that of any global optimum, thereby resolving an open question of Srebro (2007). Our second main result shows that the EM algorithm (or a first-order variant of it) with random initialization will converge to bad critical points with probability at least 1-e {-\Omega(M)} . We further establish that a first-order variant of EM will not converge to strict saddle points almost surely, indicating that the poor performance of the first-order method can be attributed to the existence of bad local maxima rather than bad saddle points.

Age: Ted started in 1984. And has Ted been talking ever since? I know, and they do the inspirational online talks. Correct, under the slogan "Ideas change everything". She was talking at the Hay festival, in Wales.

Breakthroughs, discoveries, and DIY tips sent every weekday. Weather forecasts are notoriously unreliable. Most people can relate to booking a trip or making plans expecting a sunny day, only to have it disappointingly rained out. While seven-day weather forecasts are accurate about 80 percent of the time, that figure drops to around 50 percent when extended to 10 days or more. Recent staffing cuts at the National Weather Service have already led to reduced weather balloon data collection, which experts warn could further degrade forecast accuracy.