How the artists in this year's survey do or, more often, don't acknowledge those who paved the way for them. Machado makes pieces that one might call documents of reverence, excavated burial grounds. If nothing else, the 2026 Whitney Biennial, curated by Marcela Guerrero and Drew Sawyer (at the Whitney Museum through August 23rd), introduces viewers to what I call ChatGPT art--facsimiles of facsimiles by makers who have little if any relationship to what they're putting out there, aside from its being a product in service of a career. Indeed, it's difficult to think of the people who grew up with and apparently condone the use of A.I. sources in the creation of "art" as artists themselves, especially if you define art as a creative expression of thoughts or feelings that have changed, and contributed to the vision of, the artists who made it. It's true that, nearly from the beginning, postmodern art challenged the notion of originality, or, more specifically, the weight of originality--often with great joy and wit and not a little fear.

In this paper, we study the support recovery guarantees of underdetermined sparse regression using the `1-norm as a regularizer and a non-smooth loss function for data fidelity. More precisely, we focus in detail on the cases of `1 and ` losses, and contrast them with the usual `2 loss. While these losses are routinely used to account for either sparse (`1 loss) or uniform (` loss) noise models, a theoretical analysis of their performance is still lacking. In this article, we extend the existing theory from the smooth `2 case to these non-smooth cases. We derive a sharp condition which ensures that the support of the vector to recover is stable to small additive noise in the observations, as long as the loss constraint size is tuned proportionally to the noise level. A distinctive feature of our theory is that it also explains what happens when the support is unstable. While the support is not stable anymore, we identify an "extended support" and show that this extended support is stable to small additive noise. To exemplify the usefulness of our theory, we give a detailed numerical analysis of the support stability/instability of compressed sensing recovery with these different losses. This highlights different parameter regimes, ranging from total support stability to progressively increasing support instability.

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

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

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

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

This easy-to-use, Wi-Fi-enabled bean-to-cup brewer is good, but not quite great. App is simple and works well. "Smart" features only work with Amazon Alexa and Google Assistant. Integrating with HomeKit via third-party apps is not worth the effort. Pricey for what's essentially an auto-drip machine that works with an app, which is no longer novel or futuristic.

Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks.

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

We propose an online convex optimization algorithm (RESCALEDEXP) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RESCALEDEXP matches this lower bound asymptotically in the number of iterations. RESCALEDEXP is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that require hyperparameter optimization.