When you walk into Erven, Nick Erven's newish restaurant in Santa Monica, you may not immediately place it as vegan. There's a kind of deli counter up front selling salads and sandwiches to go, and the guy at the host station may hand you a shot glass of sangria as a gesture of goodwill. The smell may be typical of vegan restaurants, the funk of many simmering brassicas instead of the scent of charring meat -- but the sharp angles and textile blocks of the double-height dining room seem more welcoming than they did when this space was Real Food Daily. The snacks that everybody seems to be popping into their mouths -- jet-black squares of chickpea fritter scented with yuzu; crisply fried sunchokes with what tastes like a cross between romesco sauce and ketchup; crunchy nuggets of savory deep-fried dates -- are pretty much what you would expect to taste in a sleek tasting-menu restaurant. You bite down into what looks like a doughnut hole, and although the sour, dark purée of sauerkraut and smoked apples squirts halfway across the table cloth, it is hard to see how Nick Erven has anything but pure animal pleasure on his mind.
How it got to be December is anyone's guess, but here we are, in the lull between one holiday and the next. Which means it's a great time to take a break from baking cookies and figuring out what to get Luke Walton for Christmas, and get back to exploring this town's complex and glorious restaurant scene. If you need a break from heavy holiday food, maybe try Erven, the subject of Jonathan Gold's latest review. It's vegan, it has things called "slurpables," and it has sauerkraut-stuffed doughnut holes. Yeah, yeah, I know, but Jonathan really liked them.
We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algorithm (and also a version of the polynomially weighted average algorithm) with multiple learning rates. These bounds are in terms of excess losses, the differences between the instantaneous losses suffered by the algorithm and the ones of a given expert. We then demonstrate the interest of these bounds in the context of experts that report their confidences as a number in the interval [0,1] using a generic reduction to the standard setting. We conclude by two other applications in the standard setting, which improve the known bounds in case of small excess losses and show a bounded regret against i.i.d. sequences of losses.