Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
論文紹介: Sample Reuse via Importance Sampling in I...
Search
Masahiro Nomura
April 09, 2020
Research
0
260
論文紹介: Sample Reuse via Importance Sampling in Information Geometric Optimization / sample_reuse_igo
Masahiro Nomura
April 09, 2020
Tweet
Share
More Decks by Masahiro Nomura
See All by Masahiro Nomura
ランダム欠損データに依存しない推薦システムのバイアス除去 / towards-resolving-propensity-contradiction-in-offline-recommender-learning
nmasahiro
0
270
転移学習によるハイパーパラメータ最適化の高速化 / warm_starting_cma
nmasahiro
0
2.2k
論文紹介: IRのためのパラメータチューニング / ir-tuning
nmasahiro
0
490
機械学習における ハイパーパラメータ最適化の理論と実践 / hpo_theory_practice
nmasahiro
30
40k
論文紹介 : Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
nmasahiro
1
710
広告とAI(とハイパーパラメータ最適化) / Ad with AI
nmasahiro
1
2k
Other Decks in Research
See All in Research
不確実性下における目的と手段の統合的探索に向けた連続腕バンディットの応用 / iot70_gp_rff_mab
monochromegane
2
200
Combinatorial Search with Generators
kei18
0
1k
【輪講資料】Moshi: a speech-text foundation model for real-time dialogue
hpprc
3
770
説明可能な機械学習と数理最適化
kelicht
0
260
音声感情認識技術の進展と展望
nagase
0
300
2025/7/5 応用音響研究会招待講演@北海道大学
takuma_okamoto
1
230
MIRU2025 チュートリアル講演「ロボット基盤モデルの最前線」
haraduka
15
9.3k
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
3
380
A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects
satai
4
380
超高速データサイエンス
matsui_528
1
170
AWSで実現した大規模日本語VLM学習用データセット "MOMIJI" 構築パイプライン/buiding-momiji
studio_graph
2
810
アニメにおける宇宙猫ミームとその表現
yttrium173340
0
100
Featured
See All Featured
Designing for Performance
lara
610
69k
jQuery: Nuts, Bolts and Bling
dougneiner
65
7.9k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
9
1k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
26
3.1k
YesSQL, Process and Tooling at Scale
rocio
173
15k
Rails Girls Zürich Keynote
gr2m
95
14k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
23
1.5k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
34
2.3k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
37
2.6k
What’s in a name? Adding method to the madness
productmarketing
PRO
24
3.7k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
132
19k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
21
1.2k
Transcript
. Sample Reuse via Importance Sampling in Information Geometric Optimization
^ILW1NL Y 18 : I ) ) * ( WP LW WPK ^ 1
• . S RL ?L XL [P 8S UWY L
S RP N P 8 MUWS YPU 7LUSLYWP = YPSP YPU • . OP P OP OPW DU OLP 1 PSUYU P = OP U U =O W • . WCP[ ) (- 75 = (+ L]YL K • . w 87= ʼ • . 28: 1 5 h • . 1 5 z~ 2
• 87= • 87= • • 3
87= • i i i • . s p f
s • w ~n • 1 5 5 . ]ac nʼ s • 28: 71 . ]ac nʼ • w z~ 1 5 ? e A K YL 4
87= • 87= (. • M I M 5
87= • zrzf5EM ] F sm • y • z~
s n • z~ 5EB ] F 6
87= • zrzf5EM ] F sm • y • z~
s n • z~ 5EB ] F 7
87= • zrzf5EM ] F sm • y • z~
s n • z~ 5EB ] F 8
87= • zrzf5EM ] F sm • y • z~
s n • z~ 5EB ] F 9
87= • now f5EB ] F w ʼ • ~
• 87= ). • N I S • . • : KP[s • i rv w 10
87= • • d ]ac r • • 5EB ]
F zfc 11
87= • • d ]ac r • • 5EB ]
F zfc • d ~ sm 12
87= • 87= d n~n • PYLW YPU x ~~z
~ f t~n n • ~ ~ 13
8S UWY L S RP N • ( G /
( f r s • zʼn . ] b G N ] ∫ " # %& # '# • i . G r d • n 8 . • A IP XLKr|f G zʼ s n 14
• 28: . ]ac nʼ87= • • 1 5 .
]ac nʼ87= • z n z k 15
. 28: 16 • = L ] . ( ʼ
s ~(s • :L KP N= LX . r (s zʼ s ~(s
. 1 5 17 • 5RRP XUPK ?UXL IWU .
• 1 RL^ ? XYWPNP . • ? XYWPNP s1 RL^ s n
. 1 5 18
• 87= ʼ i i • 28: 1 5 •
8S UWY L S RP N • 28: 1 5 z~ • ? XYWPNP zr ʼsf • • 19