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Jack J. Mayo

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Conference and Workshop Papers

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2024
[c3]
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  - electronic edition @ mlr.press (open access)
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  dblp key:
  - conf/colt/FokkemaHLM24
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Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo:
Online Newton Method for Bandit Convex Optimisation Extended Abstract. COLT 2024: 1713-1714
2023
[c2]
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  dblp key:
  - conf/nips/OlkhovskayaMENW23
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  - https://dblp.org/rec/conf/nips/OlkhovskayaMENW23
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS 2023
2022
[c1]
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  dblp key:
  - conf/colt/MayoHE22
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Jack J. Mayo, Hédi Hadiji, Tim van Erven:
Scale-free Unconstrained Online Learning for Curved Losses. COLT 2022: 4464-4497

Informal and Other Publications

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2024
[i3]
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  - journals/corr/abs-2406-06506
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Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo:
Online Newton Method for Bandit Convex Optimisation. CoRR abs/2406.06506 (2024)
2023
[i2]
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  dblp key:
  - journals/corr/abs-2305-00832
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  - https://dblp.org/rec/journals/corr/abs-2305-00832
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR abs/2305.00832 (2023)
2022
[i1]
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  - electronic edition @ arxiv.org (open access)
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  dblp key:
  - journals/corr/abs-2202-05630
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  - https://dblp.org/rec/journals/corr/abs-2202-05630
Jack J. Mayo, Hédi Hadiji, Tim van Erven:
Scale-free Unconstrained Online Learning for Curved Losses. CoRR abs/2202.05630 (2022)

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