(cid:NN). The PDF this text was extracted from encodes 1 character in a way that names no letter, so the extractor could not say what they were. The quotes keep them because a quote is reproduced exactly or it proves nothing — the marks are the source’s, not the tagging’s.phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | domain adaptation - training where the labels are and deploying where they are not, with a theory whose usable half is the half that can be computed without labels— “For example, we might have a spam filter trained from a large email collection received by a group of current users (the source domain) and wish to adapt it for a new user (the target domain). Intuitively this should improve filtering performance for the new user, under the assumption that users generally agree on what is spam and what is… | |
| claim-type | mechanism— “The H-divergence resolves both problems associated with the L1 divergence. First, for hypothesis classes H of finite VC dimension, the H-divergence can be estimated from finite samples (see Lemma 1 below). Second, the H-divergence for any class H is never larger than the L1 divergence, and is in general smaller when H has finite VC… | |
| method — evidence | — “Our basic plan of attack will be as follows: Label each unlabeled source instance with 0 and unlabeled target instance as 1. Then train a classifier to discriminate between source and target instances. The H-divergence is immediately computable from the error.” | |
| object of study — evidence | — “We give a classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains. Under the assumption that there exists some hypothesis that performs well in both domains, we show that this quantity together with the empirical source error characterize the target error of a source-trained… | |
| research question — evidence | — “We give a classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains. Under the assumption that there exists some hypothesis that performs well in both domains, we show that this quantity together with the empirical source error characterize the target error of a source-trained… | |
| contribution — evidence | — “The H-divergence resolves both problems associated with the L1 divergence. First, for hypothesis classes H of finite VC dimension, the H-divergence can be estimated from finite samples (see Lemma 1 below). Second, the H-divergence for any class H is never larger than the L1 divergence, and is in general smaller when H has finite VC… | |
| transfer | from database change detection: a divergence built for change detection in data streams, taken into statistical learning theory as the domain-distance term of a generalisation bound— “The H-divergence resolves both problems associated with the L1 divergence. First, for hypothesis classes H of finite VC dimension, the H-divergence can be estimated from finite samples (see Lemma 1 below). Second, the H-divergence for any class H is never larger than the L1 divergence, and is in general smaller when H has finite VC… | transfers[0] |
| transfer | from statistical hypothesis testing: binary classification itself used as a measuring instrument - a discriminator trained to separate two unlabeled samples, with its error read as a distance— “Our basic plan of attack will be as follows: Label each unlabeled source instance with 0 and unlabeled target instance as 1. Then train a classifier to discriminate between source and target instances. The H-divergence is immediately computable from the error.” | transfers[1] |
| mechanism | a distance defined by the classifier you were going to use the L1 divergence takes a supremum over all measurable subsets and is rejected as unestimable and over-strict; the H-divergence takes it over the hypothesis class. So the distance between two FIXED distributions is a function of H - a richer class sees the domains as further apart, and the bound loosens because the model got more expressive rather than because the data moved— “Bounding the error in terms of the L1 divergence between distributions has two disadvantages. First, it cannot be accurately estimated from finite samples of arbitrary distributions (Batu et al. 2000; Kifer et al. 2004) and therefore has limited usefulness in practice. Second, for our purposes the L1 divergence is an overly strict measure… | mechanisms[0] |
| mechanism | measure the difference by trying to detect it label every unlabeled source instance 0 and every unlabeled target instance 1, train a classifier to tell them apart, and read the divergence off its error. If the domains are indistinguishable to H the divergence is zero. The measurement IS the adversary, which is why the whole domain-adversarial literature follows from this paragraph— “Our basic plan of attack will be as follows: Label each unlabeled source instance with 0 and unlabeled target instance as 1. Then train a classifier to discriminate between source and target instances. The H-divergence is immediately computable from the error.” | mechanisms[1] |
| mechanism | the third term is the assumption target error is bounded by source error plus divergence plus lambda. The first two come from unlabeled data. Lambda is the error of the best joint hypothesis and needs labelled target data to evaluate - the thing the setting is defined by not having. THE BOUND IS USABLE EXACTLY WHEN ITS UNCHECKABLE TERM IS SMALL— “When the combined error of the ideal joint hypothesis is large, then there is no classifier that performs well on both the source and target domains, so we cannot hope to find a good target hypothesis by training only on the source domain.” | mechanisms[2] |
| mechanism | the half of the difference that is assumed away two domains can differ in their labeling functions as well as in their input distributions. One clause - `since we expect the labeling function difference to be small in practice` - removes the first from the theory. Everything downstream measures distributional shift and inherits the assumption without restating it— “Since we expect the labeling function difference to be small in practice, we focus here on measuring distribution divergence, and especially on how to estimate it with finite samples of unlabeled data from DS and DT .” | mechanisms[3] |
| mechanism | an intractable quantity approximated by a surrogate computing the H-divergence needs the minimum-error hypothesis for the source-versus-target problem, which is intractable for most classes. What is used instead is the error of a hypothesis minimising a convex upper bound - so the reported divergence is an approximation of an approximation, and the paper says so— “Of course, minimizing error for most reasonable hypothesis classes is a computationally intractable problem. Nonetheless, as we shall see in Sect. 7, the error of hypotheses trained to minimize convex upper bounds on error are useful in approximating the H-divergence.” | mechanisms[4] |
| critiques | the use of L1 and Kullback-Leibler divergence in domain adaptation, on the grounds that neither can be accurately estimated from finite samples in a distribution-free setting— “In a distribution-free setting, we cannot obtain accurate estimates of common measures of divergence such as L1 or Kullback-Leibler from finite samples. Instead, we show that when learning a hypothesis from a class of finite complexity, it is sufficient to use a classifier-induced divergence” | claim_relations[0] |
| extends | Kifer et al. 2004's divergence for change detection, carried into a learning bound— “The H-divergence resolves both problems associated with the L1 divergence. First, for hypothesis classes H of finite VC dimension, the H-divergence can be estimated from finite samples (see Lemma 1 below). Second, the H-divergence for any class H is never larger than the L1 divergence, and is in general smaller when H has finite VC… | claim_relations[1] |
| distinguishes-from | Crammer et al. 2008, whose bound is less general and does not handle positive H-delta-H divergence or non-uniform weighting— “However, the bound of Crammer et al. (2008) is less general.” | claim_relations[2] |
| qualifies | its own bound, by stating that it is informative only when the ideal joint hypothesis has small error - a quantity the setting cannot supply— “When the combined error of the ideal joint hypothesis is large, then there is no classifier that performs well on both the source and target domains, so we cannot hope to find a good target hypothesis by training only on the source domain.” | claim_relations[3] |
| sense | divergence between domains - AS L1: a supremum over all measurable subsets, which cannot be accurately estimated from finite samples and unnecessarily inflates the bound— “Bounding the error in terms of the L1 divergence between distributions has two disadvantages. First, it cannot be accurately estimated from finite samples of arbitrary distributions (Batu et al. 2000; Kifer et al. 2004) and therefore has limited usefulness in practice. Second, for our purposes the L1 divergence is an overly strict measure… | senses[0] |
| sense | divergence between domains - AS H-DIVERGENCE: a supremum over the hypothesis class only, estimable from finite unlabeled samples and never larger than L1— “The H-divergence resolves both problems associated with the L1 divergence. First, for hypothesis classes H of finite VC dimension, the H-divergence can be estimated from finite samples (see Lemma 1 below). Second, the H-divergence for any class H is never larger than the L1 divergence, and is in general smaller when H has finite VC… | senses[1] |
| sense | the domain difference - THE PART MEASURED: the divergence between the two marginal distributions— “Since we expect the labeling function difference to be small in practice, we focus here on measuring distribution divergence, and especially on how to estimate it with finite samples of unlabeled data from DS and DT .” | senses[2] |
| sense | the domain difference - THE PART ASSUMED AWAY: the difference between the two labeling functions, expected to be small in practice and not measured— “Since we expect the labeling function difference to be small in practice, we focus here on measuring distribution divergence, and especially on how to estimate it with finite samples of unlabeled data from DS and DT .” | senses[3] |
| sense | lambda - AS A TERM IN THE BOUND: the combined error of the hypothesis that minimises source plus target error— “The ideal joint hypothesis explicitly embodies our notion of adaptability. When this hypothesis… | senses[4] |
| sense | lambda - AS AN ASSUMPTION: adaptability itself. If it is large there is no classifier good on both domains and no hope from source training; if small, the H-divergence carries the bound - and which of the two holds cannot be checked without labelled target data Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | senses[5] |
| control | the H-divergence checked against the L1 divergence on both counts that matter - estimability from finite samples and tightness of the resulting bound — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out a new measure adopted for convenience - it is shown never to be larger than the one it replaces, so the bound cannot have been loosened to make it work Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[0] |
| control | the bound's behaviour examined at both extremes of lambda rather than only where it is convenient — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out a bound that is quietly vacuous when adaptation is impossible - the paper states plainly that a large lambda means no classifier serves both domains Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[1] |
| control | the intractability of computing the H-divergence stated, with the convex surrogate named as an approximation rather than presented as the quantity — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out a reported divergence being read as the defined one - the gap between them is declared at the point the substitution is made Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[2] |
| control | source domains at varying distance compared against target-only training as labelled target data increases — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out the claim that source data always helps - with enough target labels it never does, whatever its volume Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[3] |
| control | the relation to Crammer et al. (2008) worked out, with the conditions under which the two measures coincide — absent-stated-about-other — rules out that the two divergence measures differ in the cases that matter - the conditions under which they coincide are what would settle it Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[4] |
| weight | the use of L1 and Kullback-Leibler divergence in domain adaptation, on the grounds that neither can be accurately estimated from finite samples in a distribution-free setting — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[0] |
| weight | Kifer et al. 2004's divergence for change detection, carried into a learning bound — 0.9 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[1] |
| weight | Crammer et al. 2008, whose bound is less general and does not handle positive H-delta-H divergence or non-uniform weighting — 0.9 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[2] |
| weight | its own bound, by stating that it is informative only when the ideal joint hypothesis has small error - a quantity the setting cannot supply — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[3] |
| differs in (stated by the tagger, no quotation) | H-divergence —distinct-from→ L1 divergence — what set the supremum runs over, and therefore whether the distance can be estimated | concept_edges[0] |
| relation | H-divergence —distinct-from→ L1 divergence — the H-divergence is estimable from finite samples for classes of finite VC dimension and is never larger than the L1 divergence, because its supremum runs over the hypothesis class rather than over all measurable subsets Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[0] |
| weight | H-divergence —distinct-from→ L1 divergence — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[0] |
| audit | labeling function —distinct-from→ distribution divergence — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span sets the labeling-function difference aside to focus on distribution divergence; distinct-from is loose. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[2] |
| differs in (stated by the tagger, no quotation) | labeling function —distinct-from→ distribution divergence — which of the two ways a domain can shift the theory actually measures | concept_edges[2] |
| relation | labeling function —distinct-from→ distribution divergence — two domains can differ in their labeling functions or in their distributions; the labeling difference is expected to be small in practice and the paper measures the other one Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[2] |
| weight | labeling function —distinct-from→ distribution divergence — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[2] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[0] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[1] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[2] |
| outcome | control — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[3] |
| outcome (stated by the tagger, no quotation) | control — absent-stated-about-other Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[4] |
| limitation | the bound is only useful when the ideal joint hypothesis has small error, and that error cannot be estimated without labelled target data Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[0] |
| limitation | the difference between the two domains' labeling functions is assumed small rather than measured Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[1] |
| limitation | minimising error to compute the H-divergence is computationally intractable for most hypothesis classes, so a convex surrogate is used Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[2] |
| limitation | common divergences such as L1 and Kullback-Leibler cannot be accurately estimated from finite samples in a distribution-free setting Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[3] |
| limitation | with enough labelled target data the source data is never preferred, no matter how much of it there is - the method's value is bounded by target-label scarcity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[4] |
| limitation | the practical recommendation holds only given a reasonable measure of complexity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[5] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[0] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[1] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[2] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[3] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[4] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[5] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[6] |
| variable | distribution divergence Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[0] |
| variable | labeling function Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[1] |
| definition | H-divergence Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[0] |
| definition | adaptability Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| concept | H-divergence — not defined in this paper's own words; the distance between two distributions as seen by a hypothesis class - a supremum over H rather than over all measurable sets, so the distance between two fixed domains changes when H does Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[0] |
| concept | L1 divergence — not defined in this paper's own words; the classical distance, rejected here because it cannot be estimated from finite samples of arbitrary distributions and inflates the bound Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[1] |
| concept | ideal joint hypothesis — not defined in this paper's own words; the hypothesis minimising combined source and target error; its error is the bound's third term and the one term that cannot be estimated without labelled target data Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[2] |
| concept | adaptability — not defined in this paper's own words; what the ideal joint hypothesis explicitly embodies - whether ANY single classifier could serve both domains, which is prior to any question about a particular one Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[3] |
| concept | labeling function — not defined in this paper's own words; the second way two domains can differ; its difference is expected to be small in practice and is not measured, so the theory covers the distributional half Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[4] |
| concept | distribution divergence — not defined in this paper's own words; the half of the domain difference the paper measures, chosen because the other half is assumed small Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | VC dimension — not defined in this paper's own words; the finiteness that makes the H-divergence estimable at all - the whole construction rests on the hypothesis class being simple enough to converge Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | source domain — not defined in this paper's own words; where the labels are - in the paper's own example, the mail of a group of current users Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | target domain — not defined in this paper's own words; where the classifier must work and where labels are scarce or absent - one new user, receiving a unique distribution of email Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| concept | hypothesis classes — not defined in this paper's own words; the set the learner searches; minimising error over most of them is computationally intractable, so the divergence is approximated by convex surrogates Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[9] |
| VC dimension | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[0] |
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