Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Here are the best apps for finding a casual, low-stress connection this summer. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and . Editors and writers independently select products unless marked Sponsored or Promoted. Sponsored content is a paid ad, while content marked Promoted is chosen by Ziff Davis leadership. We may earn an affiliate commission if you buy through our links. Promoted cards do not include input from individual authors. These are the tech, tools, and products -- from laptops to e-readers, from earbuds to robovacs, and more -- that Mashable ranks best in class. In the age of on-demand everything, it's no surprise that we can get dates (and I use that term loosely) with the tap of a button. Thanks to classic hookup apps like Tinder and newcomers like Pure, finding a sexy new friend can be as straightforward as ordering a pizza. That's about the same as your pizza -- hit or miss, depending on the time of day, where you're located, and how you are. SEE ALSO: Is AdultFriendFinder the best dating site for Gen Z? Fortunately, you've got me, your resident dating app expert (though I prefer modern-day Carrie Bradshaw), to guide you to the promised land, aka the best hookup apps. I've spent the past three years (yes, years) researching, downloading, and testing dozens of the top dating apps to find the best ones for all kinds of daters, including the one-night-stand crowd. I also consulted a helpful panel of sex and relationship experts to make sure my picks are solid. My number one rule is to be brutally honest about what you want. Are you here for a good time?

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series We tested the safest, most effective apps for finding a real partner. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and . Editors and writers independently select products unless marked Sponsored or Promoted. Sponsored content is a paid ad, while content marked Promoted is chosen by Ziff Davis leadership. We may earn an affiliate commission if you buy through our links. Promoted cards do not include input from individual authors. These are the tech, tools, and products -- from laptops to e-readers, from earbuds to robovacs, and more -- that Mashable ranks best in class. Being a woman on the internet has never been easy, but trying to find a genuine connection on a dating app in 2026? Between the emboldened creeps, the swipe fatigue, and surprisingly convincing AI profiles, it's enough to make you wonder if applying for is the more sane alternative. The reality is, dating right now comes with a set of challenges --especially when it comes to safety. A 2026 survey from SSRS found that 55 percent of women believe meeting someone in person from a dating app is unsafe, compared to just 30 percent of men. We're dealing with a constant barrage of low-effort hey messages, unsolicited dick pics, men who are married, and the underlying safety concerns men just don't have to think about .

Gift Ideas For Everyone On Your List Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Mashable Selects Say More Versus Creator Hub Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Five authors, including Rufi Thorpe, wrote and narrated Hinge couple stories. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Bluesky .

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Find love for the summer -- or forever. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Bluesky . Bethany Allard is a Los Angeles-based shopping reporter at Mashable covering beauty tech, dating, sex and relationships, and headphones. That basically means she puts her hair through a lot, scrolls through a lot of dating apps, and rotates through a lot of different headphones. In addition to testing out and rounding up the best products, she also covers deals for Mashable, paying an especially obsessive amount of attention to Apple deals and prices. That knowledge comes in handy when she's covering shopping holidays like Prime Day and Black Friday, which she's now done for three years at Mashable. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School.

Recent years have witnessed the emergence of a spectrum of foundation models, covering a broad range of capabilities and costs. Often, we effectively use foundation models as feature generators and train classifiers that use the outputs of these models to make decisions. In this paper, we consider an increasingly relevant setting where we have two classifier stages. The first stage has access to features x and has the option to make a classification decision or defer, while incurring a cost, to a second classifier that has access to features x and z. This is similar to the "learning to defer" setting, with the important difference that we train both classifiers jointly, and the second classifier has access to more information. The natural loss for this setting is an ℓ01c loss, where a penalty is paid for incorrect classification, as in ℓ01, but an additional penalty cis paid for consulting the second classifier. The ℓ01c loss is unwieldy for training. Our primary contribution in this paper is the derivation of a hinge-based surrogate loss ℓchinge that is much more amenable to training but also satisfies the property that ℓchinge-consistency implies ℓ01c-consistency.

Recent years have witnessed the emergence of a spectrum of foundation models, covering a broad range of capabilities and costs. Often, we effectively use foundation models as feature generators and train classifiers that use the outputs of these models to make decisions. In this paper, we consider an increasingly relevant setting where we have two classifier stages. The first stage has access to features $x$ and has the option to make a classification decision or defer, while incurring a cost, to a second classifier that has access to features $x$ and $z$. This is similar to the ``learning to defer'' setting, with the important difference that we train both classifiers jointly, and the second classifier has access to more information. The natural loss for this setting is an $\ell_{01c}$ loss, where a penalty is paid for incorrect classification, as in $\ell_{01}$, but an additional penalty $c$ is paid for consulting the second classifier. The $\ell_{01c}$ loss is unwieldy for training. Our primary contribution in this paper is the derivation of a hinge-based surrogate loss $\ell^c_{hinge}$ that is much more amenable to training but also satisfies the property that $\ell^c_{hinge}$-consistency implies $\ell_{01c}$-consistency.

Bayes-consistency only holds for the full family of measurable functions, which of course is distinct from the more restricted hypothesis set used by a learning algorithm. Therefore, a hypothesis setdependent notion of H-consistency has been proposed by Long and Servedio (2013) in the realizable setting, used by Zhang and Agarwal (2020) for linear models, and generalized by Kuznetsov et al. (2014) to the structured prediction case. Long and Servedio (2013) showed that there exists a case where a Bayes-consistent loss is not H-consistent while inconsistent losses can be H-consistent. Zhang and Agarwal (2020) further investigated the phenomenon in (Long and Servedio, 2013) and showed that the situation of losses that are not H-consistent with linear models can be remedied by carefully choosing a larger piecewise linear hypothesis set. Kuznetsov et al. (2014) proved positive results for the H-consistency of several multi-class ensemble algorithms, as an extension of H-consistency results in (Long and Servedio, 2013). Recently, the notions of H-calibration and H-consistency have been used by Bao et al. (2020); Awasthi et al. (2021a) in the study of adversarial binary classification losses, as defined in (Goodfellow et al., 2014; Madry et al., 2017; Tsipras et al., 2018; Carlini and Wagner, 2017; Awasthi et al., 2023).

Human beings are able to transfer the policies swiftly to a structurally similar task.

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