When n is independent ofδ our approach yields an algorithm whose sample complexityconvergesto 2n2 log 1δ asngrows.

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.

However,duetogrowing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using anydata ormetadata. Weachievethisbytraining anadversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student.

This module is trained in an adversarial learning manner [5]. The Selectionmodule selects high possibility candidates from proposals and combines to produce the final prediction, according to the criteria of better position matching.

We study the problem of how to repeatedly sell to a buyer running a no-regret,mean-based algorithm. Previous work [Braverman et al., 2018] shows that it ispossible to design effective mechanisms in such a setting that extract almost allof the economic surplus, but these mechanisms require the buyer's values each

But spectral clustering remains a frequently used baseline, and a serious contender to state-of-the-art graphembeddingmethods,e.g.[20,11,28,22].

Toeasethelearning, wedemonstrate thatitisbeneficial toadoptacurriculum learning strategy [23], where harder negatives are introduced after an initial stage of learning on easiernegatives.

Therefore, many existing SLP vectorization schemes are driven by hand-crafted heuristics (Liu et al., 2012;

Several papers consider approaches that limit the number of bits to represent floating point numbers [13, 24, 31]. Recent work proposes adaptive tuning of the compression ratio [7].