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UID:6c805fcd06f52f674e20595f9d0ee08b
CATEGORIES:Special Seminar
CREATED:20250428T091423
SUMMARY:Rates of convergence in Pool-Based Batch Active Learning
LOCATION:Hill 423
DESCRIPTION:<div data-olk-copy-source="MessageBody" style="font-style: normal; font-wei
 ght: 400; letter-spacing: normal; text-align: start; text-indent: 0px; text
 -transform: none; white-space: normal; word-spacing: 0px; text-decoration: 
 none; border: 0px; font-size: 15px; line-height: inherit; font-family: 'Seg
 oe UI', 'Segoe UI Web (West European)', -apple-system, BlinkMacSystemFont, 
 Roboto, 'Helvetica Neue', sans-serif; margin: 0px; padding: 0px; vertical-a
 lign: baseline; color: #242424;">We consider a supervised machine learning 
 scenario called batch active learning, where a learning agent adaptively is
 sues batches of points to a labeling oracle. Sampling labels in batches is 
 highly desirable in practice due to the smaller number of interactive round
 s with the labeling oracle (often human beings or highly expensive pre-trai
 ned models). Yet, batch active learning typically pays the price of reduced
  adaptivity, leading to suboptimal convergence results. We describe a solut
 ion which requires a careful trade off between the informativeness of the q
 ueried points and their diversity. We theoretically investigate batch activ
 e learning in the practically relevant scenario where the unlabeled pool of
  data is available beforehand (pool-based batch active learning). We analyz
 e a stage-wise greedy algorithm and show that, as a function of the label c
 omplexity, the excess risk of this algorithm matches the known minimax rate
 s in a standard statistical learning setting with linear function spaces. O
 ur results also exhibit a mild dependence on the batch size. These results 
 are then extended to hold for general function spaces with similar algorith
 mics, but more involved analytical tools.&nbsp;</div><div style="font-style
 : normal; font-weight: 400; letter-spacing: normal; text-align: start; text
 -indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;
  text-decoration: none; border: 0px; font-size: 15px; line-height: inherit;
  font-family: 'Segoe UI', 'Segoe UI Web (West European)', -apple-system, Bl
 inkMacSystemFont, Roboto, 'Helvetica Neue', sans-serif; margin: 0px; paddin
 g: 0px; vertical-align: baseline; color: #242424;">Joint with: Z. Wang and 
 T. Zhang.</div>
CONTACT:Claudio Gentile (Google Research)
DTSTAMP:20260829T005056
DTSTART;TZID=America/New_York:20250501T153000
DTEND;TZID=America/New_York:20250501T163000
SEQUENCE:0
TRANSP:OPAQUE
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