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非凸確率的最適化と再生核ヒルベルト空間の最適化

Taiji Suzuki
June 18, 2023
53

 非凸確率的最適化と再生核ヒルベルト空間の最適化

「機械学習における最適化理論と学習理論的側面@組合せ最適化セミナー2020」第二部

Taiji Suzuki

June 18, 2023
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  1. ඇತؔ਺Ͱͷ SGD ໨తؔ਺ɿL(x) = Ez [ℓ(z, x)]ɽ (Լʹ༗ք, L∗ =

    infx L(x) ͱ͢Δ) SGD zt ∼ P(Z) Λ؍ଌɽℓt (x) := ℓ(zt, x) ͱ͢Δ. gt ∈ ∂x ℓt (xt−1 ). xt = xt−1 − ηt gt. Ծఆ (A1) L ͸ γ-ฏ׈ (A2) E[∥gt − E[gt ]∥2] = σ2 (֬཰తޯ഑ͷ෼ࢄ͸ σ2). ηt = min { ˜ D σ √ T , 1 γ } ͱ͢Δͱ (Ghadimi and Lan (2013)) min 1≤t≤T E[∥∇L(xt )∥2] ≤ γσ √ T ( D2 f ˜ D + ˜ D ) + γ2D2 f T , ͨͩ͠ɼDf = √ 2(L(x1)−L∗) γ ɽ ʢඍ෼͕ 0 ΁ऩଋͯ͠Ώ͘͜ͱΛอূʣ ࠨลͷ min1≤t≤T ͷ୅ΘΓʹɼˆ t ∈ {1, . . . , T} ΛҰ༷෼෍ʹैͬͯબΜͰ E[∥∇L(xˆ t )∥2] ͱͯ͠΋ྑ͍ɽ 5 / 42
  2. ඇತ SVRG min x∈Rp L(x) = min x∈Rp 1 n

    n ∑ i=1 ℓi (x) SVRG Λͦͷ··ඇತؔ਺࠷దԽʹద༻ͯ͠Α͍ɽ ʢͨͩ͠εςοϓαΠζͱϛχ όον਺͸ద੾ʹௐ੔ʣ E[∥∇L(ˆ x)∥2] ≤ ϵ ʹͳΔ·Ͱͷߋ৽ճ਺ T (Allen-Zhu and Hazan, 2016, Reddi et al., 2016) ℓi ͕γ-ฏ׈ͷ࣌ɿ T ≥ Ω ( n + n2/3 ϵ ) . ʢී௨ͷඇ֬཰తޯ഑๏ͳΒ Ω(n/ϵ)ʣ ℓi ͕ γ-ฏ׈͔ͭ L(x) − L(x∗) ≤ τ∥∇L(x)∥2 (∀x) (x∗ ͸େҬత࠷దղ) ͷ࣌ (Polyak- Lojasiewicz, PL ৚݅)ɿ T ≥ Ω ( (n + τn2/3)log(1/ϵ) ) . ʢී௨ͷඇ֬཰తޯ഑๏ͳΒ Ω(τn log(1/ϵ))ʣ 6 / 42
  3. SARAH ͱͦͷվྑ๏ SSRGD (Li, 2019): SARAH + ϊΠζ෇ՃʹΑΔҌ఺୤ग़ Simple Stochastic

    Recursive Gradient Descent (SSRGD) Iterate the following for t = 1, 2, . . . , T: 1 Ҍ఺୤ग़Ϟʔυʹೖ͓ͬͯΒͣɼ∥∇L(xt )∥ ≤ gthresh ͳΒɼ xt ← xt + ξ (ξ ∼ Unif(Br (Rd ))) ͱͯ͠ɼҌ఺୤ग़ϞʔυʹೖΔɽ 2 y0 = xt , v0 = ∇f (xt ) 3 For k = 1, . . . , m, 1 yk = yk−1 − ηvk−1 2 vk = 1 b ∑ i∈Ik (∇fi (yk ) − ∇fi (yk−1 )) + vk−1 (SARAH: variance reduction) 3 ͋Δఀࢭ৚݅Λຬ͍ͨͯͨ͠ΒҌ఺୤ग़ϞʔυΛࢭΊΔɽ 4 xt+1 = ym Output: xT SARAH: StochAstic Recursive grAdient algoritHm (Nguyen et al., 2017, Pham et al., 2020) ΦϯϥΠϯܕͷ৔߹͸ ∇L ͷܭࢉ͸αϯϓϧฏۉʹ͢Δ 1 B ∑ i∈It ∇fi (xt ). ೋ࣍࠷దੑ΋ߴ͍֬཰Ͱอূ 8 / 42
  4. SARAH ʹ͍ͭͯ SARAH: vk = 1 b ∑ i∈Ik (∇fi

    (yk ) − ∇fi (yk−1 )) + vk−1 SVRG: vk = 1 b ∑ i∈Ik (∇fi (yk ) − ∇fi (˜ x)) + ˜ v SVRG ͸಺෦ϧʔϓͷߋ৽ΛਐΊΔͱ෼ࢄ͕େ͖͘ͳΔɽ SARAH ͸಺෦ϧʔϓͷߋ৽ΛਐΊͯ΋෼ࢄ͕େ͖͘ͳΒͳ͍ or 0 ʹऩଋ͢Δ (ڧತͷ৔߹) ˠ ޯ഑͕๫ΕͣɼҰ࣌࠷దੑ৚݅Λຬͨ͢ղΛಘ΍͍͢ɽ (ತ࠷దԽͰ໨తؔ਺஋Λݟ͍ͯΔݶΓ͸͜ͷҧ͍͕ݟʹ͍͘) 9 / 42
  5. ܭࢉྔͷൺֱ ϵ-Ұ࣍࠷దੑ৚݅: E[∥∇L(x)∥2] ≤ ϵ δ-ೋ࣍࠷దੑ৚݅: λmin (∇2L(x)) ≥ −δ

    (with high probability) ΦϯϥΠϯܕ ख๏ ֬཰తޯ഑ͷܭࢉ਺ ࠷దੑ৚݅ GD O(n ϵ ) 1 ࣍ SVRG (Allen-Zhu and Hazan, 2016) O(n + n2/3 ϵ ) 1 ࣍ SARAH (Pham et al., 2020) O(n + √ n ϵ ) 1 ࣍ SSRGD (Li, 2019) O(n + √ n ϵ ) 1 ࣍ PGD (Jin et al., 2017b) O(n ϵ + n δ4 ) 2 ࣍ SSRGD (Li, 2019) O( √ n ϵ + √ n δ4 + n δ3 ) 2 ࣍ ༗ݶ࿨ܕ ख๏ ֬཰తޯ഑ͷܭࢉ਺ ࠷దੑ৚݅ SGD (Ghadimi and Lan, 2013) O(1/ϵ2) 1 ࣍ SVRG+ (Li and Li, 2018) O(1/ϵ7/4) 1 ࣍ SARAH (Pham et al., 2020) O(1/ϵ3/2) 1 ࣍ SSRGD (Li, 2019) O(1/ϵ3/2) 1 ࣍ SSRGD (Li, 2019) O( 1 ϵ3/2 + 1 ϵδ3 + 1 ϵ1/2δ4 ) 2 ࣍ 10 / 42
  6. ʢࢀߟʣStrict saddle ਂ૚ֶशͳͲ͸ఀཹ఺͕ଟ͍ɽ ໨తؔ਺͕ strict saddle property ͱ͍͏ੑ࣭Λຬ͍ͨͯ͠Ε͹ɼαυϧϙΠ ϯτΛճආ͢Δ͜ͱ͕Ͱ͖Δɽ 

     ৴པྖҬ๏ (Conn et al., 2000) ΍ࡶԻΛՃ͑ͨ֬཰తޯ഑๏ (Ge et al., 2015) ͸ strict saddle ͳ໨తؔ਺ͷہॴత࠷దղʹ౸ୡ͢Δ (Sun et al., 2015).   ˞ ղʹࡶԻΛՃ͑Δ͜ͱͰαυϧϙΠϯτ͔Βൈ͚ग़ͤΔɽ Strict saddle ೋճඍ෼Մೳͳؔ਺ f ͕ strict saddle Ͱ͋Δͱ͸ɼ∀x Ͱ࣍ͷͲΕ͔͕ຬͨ͞Ε ͍ͯΔ: ∥∇f (x)∥ ≥ ϵ. λmin (∇2f (x)) ≤ −γ. ͋Δ x∗ ͕ଘࡏͯ͠ ∥x − x∗∥ ≤ δ ͔ͭ f (x) ͕ x∗ ͷۙ๣ {x′ | ∥x∗ − x′∥ ≤ 2δ} Ͱڧತؔ਺. E.g., ςϯιϧ෼ղ maxu∈Rp ⟨ ∑ d r=1 a⊗4 r , u ⊗ u ⊗ u ⊗ u ⟩ ͸ a⊤ r ar′ = δr,r′ ͳΒ strict saddleɽ 11 / 42
  7. ઢܗ੍໿͋Γͷֶश໰୊ min x 1 n n ∑ i=1 fi (z⊤

    i x) + ψ(B⊤x) ⇔ min x,y 1 n n ∑ i=1 fi (z⊤ i x) + ψ(y) s.t. y = B⊤x.   ֦ுϥάϥϯδΞϯ L(x, y, λ) = 1 n ∑ i fi (z⊤ i x) + ψ(y) + λ⊤(y − B⊤x) + ρ 2 ∥y − B⊤x∥2   inf x,y sup λ L(x, y, λ) Ͱ࠷దղ͕ٻ·Δɽ ৐਺๏: Hestenes (1969), Powell (1969), Rockafellar (1976). ަޓํ޲৐਺๏ (ADMM): Gabay and Mercier (1976), Mota et al. (2011), He and Yuan (2012), Deng and Yin (2012), Hong and Luo (2012a) ֬཰తަޓํ޲৐਺๏: SGD-ADMM (Suzuki, 2013, Ouyang et al., 2013), RDA-ADMM (Suzuki, 2013), SDCA-ADMM (Suzuki, 2014), SVRG-ADMM (Zheng and Kwok, 2016), ASVRG-ADMM (Liu et al., 2017). 12 / 42
  8. ߏ଄తਖ਼ଇԽͷྫ Overlapped group lasso ˜ ψ(w) = C ∑ g∈G

    ∥wg ∥ It is difficult to compute the proximal mapping. Solution: Prepare ψ for which proximal mapping is easily computable. Let ψ(B⊤w) = ˜ ψ(w), and utilize the proximal mapping w.r.t. ψ. BTw Decompose into independent groups: B⊤w = ψ(y) = C ∑ g′∈G′ ∥yg′ ∥ prox(q|ψ) = ( qg′ max { 1 − C ∥qg′ ∥ , 0 }) g′∈G′ 13 / 42
  9. ͦͷଞͷྫ Graph guided regularization ˜ ψ(w) = C ∑ (i,j)∈E

    |wi − wj |.      x1 x2 x3 x4 x5 ψ(y) = C ∑ e∈E |ye|, y = B⊤w = (wi − wj )(i,j)∈E ⇒    ψ(B⊤w) = ˜ ψ(w), prox(q|ψ) = ( qe max { 1 − C |qe | , 0 }) e∈E . Soft-Thresholding function. 14 / 42
  10. ߏ଄తਖ਼ଇԽʹର͢Δަޓํ޲৐਺๏ min x {f (x) + ψ(B⊤w)} ⇔ min x,y

    {f (x) + ψ(y) s.t. y = B⊤x} L(x, y, λ) = f (x) + ψ(y) + λ⊤(y − B⊤x) + ρ 2 ∥y − B⊤x∥2 ͨͩ͠ f (x) = 1 n ∑ fi (z⊤ i x) ADMM ʹΑΔߏ଄తਖ਼ଇԽֶश x(t) = arg min x {f (x) + λ(t−1)⊤ (−B⊤x) + ρ 2 ∥y(t−1) − B⊤x∥2} y(t) = arg min y {ψ(y) + λ(t)⊤ y + ρ 2 ∥y − B⊤x(t)∥2} (= prox(B⊤x(t) − λ(t)/ρ|ψ/ρ)) λ(t) = λ(t−1) − ρ(B⊤x(t) − y(t)) y ͷߋ৽͸୯७ͳ ψ ʹΑΔۙ઀ࣸ૾. → ղੳղ. Ұൠతʹ͸ O(1/k) (He and Yuan, 2012), ڧತͳΒ͹ઢܗऩଋ (Deng and Yin, 2012, Hong and Luo, 2012b)ɽ 15 / 42
  11. SGD-ADMM minx EZ [ℓ(x, Z)] + ψ(B⊤x) ⇒ ֦ுϥάϥϯδΞϯ: EZ

    [ℓ(x, Z)] + ψ(y) + λ⊤(y − B⊤x) + ρ 2 ∥y − B⊤x∥2. ௨ৗͷ SGD: xt+1 = arg minx { ⟨gt , x⟩ + ˜ ψ(x) + 1 2ηt ∥x − xt ∥2 } (gt ∈ ∂x ℓ(xt , zt )). SGD-ADMM xt+1 =argmin x∈X { g⊤ t x − λt ⊤(B⊤x − yt ) + ρ 2 ∥B⊤x − yt∥2 + 1 2ηt ∥x − xt∥2 Gt } , yt+1 = argmin y∈Y { ψ(y) − λ⊤ t (B⊤xt+1 − y) + ρ 2 ∥B⊤xt+1 − y∥2 } λt+1 =λt − ρ(B⊤xt+1 − yt+1 ). yt+1 ͱ λt+1 ͷߋ৽͸௨ৗͷ ADMM ͱಉ͡ɽ Gt ͸೚ҙͷਖ਼ఆ஋ରশߦྻɽ 16 / 42
  12. SGD-ADMM minx EZ [ℓ(x, Z)] + ψ(B⊤x) ⇒ ֦ுϥάϥϯδΞϯ: EZ

    [ℓ(x, Z)] + ψ(y) + λ⊤(y − B⊤x) + ρ 2 ∥y − B⊤x∥2. ௨ৗͷ SGD: xt+1 = arg minx { ⟨gt , x⟩ + ˜ ψ(x) + 1 2ηt ∥x − xt ∥2 } (gt ∈ ∂x ℓ(xt , zt )). SGD-ADMM xt+1 =argmin x∈X { g⊤ t x − λt ⊤(B⊤x − yt ) + ρ 2 ∥B⊤x − yt∥2 + 1 2ηt ∥x − xt∥2 Gt } , yt+1 = argmin y∈Y { ψ(y) − λ⊤ t (B⊤xt+1 − y) + ρ 2 ∥B⊤xt+1 − y∥2 } =prox(B⊤xt+1 − λt/ρ|ψ), λt+1 =λt − ρ(B⊤xt+1 − yt+1 ). yt+1 ͱ λt+1 ͷߋ৽͸௨ৗͷ ADMM ͱಉ͡ɽ Gt ͸೚ҙͷਖ਼ఆ஋ରশߦྻɽ 16 / 42
  13. RDA-ADMM ௨ৗͷ RDA: wt+1 = arg minw { ⟨¯ gt

    , w⟩ + ˜ ψ(w) + 1 2ηt ∥w∥2 } (¯ gt = 1 t (g1 + · · · + gt )) RDA-ADMM Let ¯ xt = 1 t ∑ t τ=1 xτ , ¯ λt = 1 t ∑ t τ=1 λτ , ¯ yt = 1 t ∑ t τ=1 yτ , ¯ gt = 1 t ∑ t τ=1 gτ . xt+1 =argmin x∈X { ¯ g⊤ t x − (B¯ λt )⊤x + ρ 2t ∥B⊤x∥2 +ρ(B⊤¯ xt − ¯ yt )⊤B⊤x + 1 2ηt ∥x∥2 Gt } , yt+1 =prox(B⊤xt+1 − λt/ρ|ψ), λt+1 =λt − ρ(B⊤xt+1 − yt+1 ). yt+1 ͱ λt+1 ͷߋ৽͸௨ৗͷ ADMM ͱಉ͡ɽ 17 / 42
  14. Convergence analysis We bound the expected risk: Expected risk P(x)

    = EZ [ℓ(Z, x)] + ˜ ψ(x). Assumptions: (A1) ∃G s.t. ∀g ∈ ∂x ℓ(z, x) satisfies ∥g∥ ≤ G for all z, x. (A2) ∃L s.t. ∀g ∈ ∂ψ(y) satisfies ∥g∥ ≤ L for all y. (A3) ∃R s.t. ∀x ∈ X satisfies ∥x∥ ≤ R. 18 / 42
  15. Convergence rate: bounded gradient (A1) ∃G s.t. ∀g ∈ ∂x

    ℓ(z, x) satisfies ∥g∥ ≤ G for all z, x. (A2) ∃L s.t. ∀g ∈ ∂ψ(y) satisfies ∥g∥ ≤ L for all y. (A3) ∃R s.t. ∀x ∈ X satisfies ∥x∥ ≤ R. Theorem (Convergence rate of RDA-ADMM) Under (A1), (A2), (A3), we have Ez1:T−1 [P(¯ xT ) − P(x∗)] ≤ 1 T T ∑ t=2 ηt−1 2(t − 1) G2 + γ ηT ∥x∗∥2 + K T . Theorem (Convergence rate of SGD-ADMM) Under (A1), (A2), (A3), we have Ez1:T−1 [P(¯ xT ) − P(x∗)] ≤ 1 2T ∑ T t=2 max { γ ηt − γ ηt−1 , 0 } R2 + 1 T ∑ T t=1 ηt 2 G2 + K T . Both methods have convergence rate O ( 1 √ T ) by letting ηt = η0 √ t for RDA-ADMM and ηt = η0/ √ t for SGD-ADMM. 19 / 42
  16. ༗ݶ࿨ͷ໰୊ ਖ਼ଇԽ͋Γ܇࿅ޡࠩͷ૒ର໰୊ A = [a1, a2, . . . ,

    an ] ∈ Rp×n. min w {1 n n ∑ i=1 fi (a⊤ i w) + ψ(B⊤w) } (P: ओ) = − min x∈Rn,y∈Rd {1 n n ∑ i=1 f ∗ i (xi ) + ψ∗ ( y n ) Ax + By = 0 } (D: ૒ର) ࠷దੑ৚݅: a⊤ i w∗ ∈ ∇f ∗ i (x∗ i ), 1 n y∗ ∈ ∇ψ(u)|u=B⊤w∗ , Ax∗ + By∗ = 0. ⋆ ֤࠲ඪ xi ͸֤؍ଌ஋ ai ʹରԠ. 20 / 42
  17. SDCA-ADMM ֦ுϥάϥϯδΞϯ: L(x, y, w) := ∑ n i=1 f

    ∗ i (xi ) + nψ∗(y/n) − ⟨w, Ax + By⟩ + ρ 2 ∥Ax + By∥2. SDCA-ADMM For each t = 1, 2, . . . Choose i ∈ {1, . . . , n} uniformly at random, and update y(t) ← arg min y { L(x(t−1), y, w(t−1)) + 1 2 ∥y − y(t−1)∥2 Q } x(t) i ← arg min xi ∈R { L([xi ; x(t−1) \i ], y(t), w(t−1)) + 1 2 ∥xi − x(t−1) i ∥2 Gi,i } w(t) ← w(t−1) − ξρ{n(Ax(t) + By(t))−(n − 1)(Ax(t−1) + By(t−1))}. Q, Gi,i ͸͋Δ৚݅Λຬͨ͢ਖ਼ఆ஋ରশߦྻɽ ֤ߋ৽Ͱ i-൪໨ͷ࠲ඪ xi ͷΈߋ৽ɽ w ͷߋ৽͸ؾΛ෇͚Δඞཁ͕͋Δɽ 21 / 42
  18. ઢܗճؼ σβΠϯߦྻ X = (Xij ) ∈ Rn×p. Y =

    [y1, . . . , yn ]⊤ ∈ Rn. ਅͷϕΫτϧ β∗ ∈ Rp: Ϟσϧ : Y = Xβ∗ + ξ. ϦοδճؼʢTsykonov ਖ਼ଇԽʣ ˆ β ← arg min β∈Rp 1 n ∥Xβ − Y ∥2 2 +λn∥β∥2 2 . 24 / 42
  19. ઢܗճؼ σβΠϯߦྻ X = (Xij ) ∈ Rn×p. Y =

    [y1, . . . , yn ]⊤ ∈ Rn. ਅͷϕΫτϧ β∗ ∈ Rp: Ϟσϧ : Y = Xβ∗ + ξ. ϦοδճؼʢTsykonov ਖ਼ଇԽʣ ˆ β ← arg min β∈Rp 1 n ∥Xβ − Y ∥2 2 +λn∥β∥2 2 . ม਺ม׵: ɹ ਖ਼ଇԽ߲ͷͨΊɼˆ β ∈ Ker(X)⊥ɽͭ·Γɼˆ β ∈ Im(X⊤)ɽ ͋Δ ˆ α ∈ Rn ͕ଘࡏͯ͠ɼˆ β = X⊤ ˆ α ͱॻ͚Δɽ (౳Ձͳ໰୊) ˆ α ← arg min α∈Rn 1 n ∥XX⊤α − Y ∥2 2 + λnα⊤(XX⊤)α. ˞ (XX⊤)ij = x⊤ i xj ΑΓɼ؍ଌ஋ xi ͱ xj ͷ಺ੵ͑͞ܭࢉͰ͖Ε͹Α͍ɽ 24 / 42
  20. ϦοδճؼͷΧʔωϧԽ Ϧοδճؼʢม਺ม׵൛ʣ ˆ α ← arg min α∈Rn 1 n

    ∥(XX⊤)α − Y ∥2 2 + λnα⊤(XX⊤)α. ˞ (XX⊤)ij = x⊤ i xj ͸αϯϓϧ xi ͱ xj ͷ಺ੵɽ 25 / 42
  21. ϦοδճؼͷΧʔωϧԽ Ϧοδճؼʢม਺ม׵൛ʣ ˆ α ← arg min α∈Rn 1 n

    ∥(XX⊤)α − Y ∥2 2 + λnα⊤(XX⊤)α. ˞ (XX⊤)ij = x⊤ i xj ͸αϯϓϧ xi ͱ xj ͷ಺ੵɽ • Χʔωϧ๏ͷΞΠσΟΞ x ͷؒͷ಺ੵΛଞͷඇઢܗͳؔ਺Ͱஔ͖׵͑Δ: x⊤ i xj → k(xi , xj ). ͜ͷ k : Rp × Rp → R ΛΧʔωϧؔ਺ͱݺͿ.   Χʔωϧؔ਺ͷຬͨ͢΂͖৚݅ ରশੑ: k(x, x′) = k(x′, x). ਖ਼஋ੑ: ∑ m i=1 ∑ m j=1 αi αj k(xi , xj ) ≥ 0, (∀{xi }m i=1 , {αi }m i=1 , m).   ٯʹ͜ͷੑ࣭Λຬͨؔ͢਺ͳΒԿͰ΋Χʔωϧ๏Ͱ༻͍ͯྑ͍ɽ 25 / 42
  22. ΧʔωϧϦοδճؼ ΧʔωϧϦοδճؼ: K = (k(xi , xj ))n i,j=1 ͱͯ͠ɼ

    ˆ α ← arg min β∈Rn 1 n ∥Kα − Y ∥2 2 + λnα⊤Kα. ৽͍͠ೖྗ x ʹରͯ͠͸ɼ y = n ∑ i=1 k(x, xi )ˆ αi Ͱ༧ଌɽ 26 / 42
  23. ΧʔωϧϦοδճؼ ΧʔωϧϦοδճؼ: K = (k(xi , xj ))n i,j=1 ͱͯ͠ɼ

    ˆ α ← arg min β∈Rn 1 n ∥Kα − Y ∥2 2 + λnα⊤Kα. ৽͍͠ೖྗ x ʹରͯ͠͸ɼ y = n ∑ i=1 k(x, xi )ˆ αi Ͱ༧ଌɽ Χʔωϧؔ਺ ⇔ ࠶ੜ֩ώϧϕϧτۭؒ (RKHS) k(x, x′) Hk ͋Δ ϕ(x) : Rp → Hk ͕ଘࡏͯ͠ɼ k(x, x′) = ⟨ϕ(x), ϕ(x′)⟩Hk ɽ ΧʔωϧτϦοΫ: ⟨ ∑ n i=1 αi ϕ(xi ), ϕ(x)⟩Hk = ∑ n i=1 αi k(xi , x). ˠΧʔωϧؔ਺ͷ஋͑͞ܭࢉͰ͖Ε͹ྑ͍ɽ 26 / 42
  24. ࠶ੜ֩ώϧϕϧτۭؒ (Reproducing Kernel Hilbert Space, RKHS) ೖྗσʔλͷ෼෍ɿPX ɼରԠ͢Δ L2 ۭؒɿL2

    (PX ) = {f | EX∼PX [f (X)2] < ∞}. Χʔωϧؔ਺͸ҎԼͷΑ͏ʹ෼ղͰ͖Δ (Steinwart and Scovel, 2012): k(x, x′) = ∞ ∑ j=1 µj ej (x)ej (x′). (ej )∞ j=1 ͸ L2 (PX ) ಺ͷਖ਼ن௚ަجఈ: ∥ej ∥L2(PX ) = 1, ⟨ej , ej′ ⟩L2(PX ) = 0 (j ̸= j′). µj ≥ 0. Definition (࠶ੜ֩ώϧϕϧτۭؒ (Hk )) ⟨f , g⟩Hk := ∑ ∞ j=1 1 µj αj βj for f = ∑ ∞ j=1 αj ej , g = ∑ ∞ j=1 βj ej ∈ L2 (PX ). ∥f ∥Hk := √ ⟨f , f ⟩Hk . Hk := {f ∈ L2 (PX ) | ∥f ∥Hk < ∞} equipped with ⟨·, ·⟩Hk . ࠶ੜੑ: f ∈ Hk ʹରͯ͠ f (x) ͸಺ੵͷܗͰʮ࠶ੜʯ͞ΕΔ: f (x) = ⟨f , k(x, ·)⟩Hk . 27 / 42
  25. ࠶ੜ֩ώϧϕϧτۭؒͷੑ࣭ ϕk (x) = k(x, ·) ∈ Hk ͱॻ͚͹ɼk(x, x′)

    = ⟨ϕk (x), ϕk (x′)⟩Hk ͱॻ͚Δɽ͜ͷ ϕk Λಛ௃ࣸ૾ͱ΋ݴ͏ɽ Χʔωϧؔ਺ʹରԠ͢Δੵ෼࡞༻ૉ Tk : L2 (PX ) → L2 (PX ): Tk f := ∫ f (x)k(x, ·)dPX (x). ઌͷΧʔωϧؔ਺ͷ෼ղ͸ Tk ͷεϖΫτϧ෼ղʹରԠɽ ࠶ੜ֩ώϧϕϧτۭؒ Hk ͸ҎԼͷΑ͏ʹ΋ॻ͚Δ: Hk = T1/2 k L2 (PX ). ∥f ∥Hk = inf{∥h∥L2(PX ) | f = T1/2 k h, h ∈ L2 (PX )}. f ∈ Hk ͸ f (x) = ∑ ∞ j=1 aj √ µj ej (x) ͱॻ͚ͯɼ∥f ∥Hk = √∑ ∞ j=1 a2 j ɽ (ej )j ͸ L2 ಺ͷਖ਼ن௚ަجఈɼ( √ µj ej )j ͸ RKHS ಺ͷ׬શਖ਼ن௚ަجఈɽ ಛ௃ࣸ૾ ϕk (x) = k(x, ·) ∈ Hk Λ׬શਖ਼ن௚ަجఈʹؔ͢Δ܎਺Ͱදݱ͢Δͱ ϕk (x) = ( √ µ1 e1 (x), √ µ2 e2 (x), . . . )⊤ 28 / 42
  26. ΧʔωϧϦοδճؼͷ࠶ఆࣜԽ ࠶ੜੑ: f ∈ Hk ʹର͠ f (x) = ⟨f

    , ϕ(x)⟩Hk . ΧʔωϧϦοδճؼͷ࠶ఆࣜԽ ˆ f ← min f ∈Hk 1 n n ∑ i=1 (yi − f (xi ))2 + C∥f ∥2 Hk දݱఆཧ ∃αi ∈ R s.t. ˆ f (x) = n ∑ i=1 αi k(xi , x), ⇒ ∥ˆ f ∥Hk = √∑ n i,j=1 αi αj k(xi , xj ) = √ α⊤Kα. ͖͞΄ͲͷΧʔωϧϦοδճؼͷఆࣜԽͱҰகɽ 30 / 42
  27. Χʔωϧͷྫ Ψ΢γΞϯΧʔωϧ k(x, x′) = exp ( − ∥x −

    x′∥2 2σ2 ) ଟ߲ࣜΧʔωϧ k(x, x′) = ( 1 + x⊤x′ )p χ2-Χʔωϧ k(x, x′) = exp ( − γ2 ∑ d j=1 (xj −x′ j )2 (xj +x′ j ) ) Mat´ ern-kernel k(x, x′) = ∫ Rd eiλ⊤(x−x′) 1 (1 + ∥λ∥2)α+d/2 dλ άϥϑΧʔωϧɼ࣌ܥྻΧʔωϧɼ... 31 / 42
  28. ࠶ੜ֩ώϧϕϧτۭؒ಺ͷ֬཰త࠷దԽ ໰୊ઃఆ: yi = f o(xi ) + ξi .

    (xi , yi )n i=1 ͔Β f o Λਪఆ͍ͨ͠ɽ(f o ͸ Hk ʹ΄΅ೖ͍ͬͯΔ) ظ଴ଛࣦͷมܗ: E[(f (X) − Y )2] = E[(f (X) − f o(X) − ξ)2] = E[(f (X) − f o(X))2] + σ2 ˠ minf ∈Hk E[(f (X) − Y )2] Λղ͚͹ f o ͕ٻ·Δɽ Kx = k(x, ·) ∈ Hk ͱ͢Δͱɼf (x) = ⟨f , Kx ⟩Hk ΑΓ L(f ) = E[(f (X) − Y )2] ͷ RKHS ಺Ͱͷ Frechet ඍ෼͸ҎԼͷ௨Γ: ∇L(f ) = 2E[KX (⟨KX , f ⟩Hk − Y )] = 2(E[KX K∗ X ] =:Σ f − E[KX Y ]) = 2(Σf − E[KX Y ]). ظ଴ଛࣦͷޯ഑๏: f ∗ t = f ∗ t−1 − η2(Σf ∗ t−1 − E[KX Y ]). ܦݧଛࣦͷޯ഑๏ (E[·] ͸ඪຊฏۉ): ˆ ft = ˆ ft−1 − η2(Σˆ ft−1 − E[KX Y ]). ֬཰తޯ഑ʹΑΔߋ৽: gt = gt−1 − η2(Kxit K∗ xit gt−1 − Kxit yit ). ˞ (xit , yit )∞ t=1 ͸ (xi , yi )n i=1 ͔Β i.i.d. Ұ༷ʹऔಘɽ 32 / 42
  29. ޯ഑ͷεϜʔδϯάͱͯ͠ͷݟํ ؔ਺஋ͷߋ৽ࣜ: f ∗ t (x) = f ∗ t−1

    (x) − 2η ∫ k(x, X) (f ∗ t−1 (X) − Y ) →f ∗ t−1 (X)−f o(X) dP(X, Y ) = f ∗ t−1 (x) − 2ηTk (f ∗ t−1 − f o)(x). ੵ෼࡞༻ૉ Tk ͸ߴप೾੒෼Λ཈੍͢Δ࡞༻͕͋Δɽ RKHS ಺ͷޯ഑͸ L2 ಺ͷؔ਺ޯ഑ΛTk ʹΑͬͯฏ׈Խͨ͠΋ͷʹͳ͍ͬͯ Δɽ(࣮ࡍ͸ Tk ͷαϯϓϧ͔Βͷਪఆ஋Λ࢖͏) ߴप೾੒෼͕ग़ͯ͘ΔલʹࢭΊΕ͹աֶशΛ๷͛Δɽ ˠ Early stopping ӌᮣʹ Newton ๏ͳͲΛ࢖͏ͱةݥɽ 33 / 42
  30. Early stopping ʹΑΔਖ਼ଇԽ Early stopping ʹΑΔਖ਼ଇԽ ॳظ஋ ܇࿅ޡࠩ࠷খԽݩ ʢաֶशʣ &BSMZTUPQQJOH

    όΠΞε-όϦΞϯε෼ղ ∥f o − ˆ f ∥L2(PX ) Estimation error ≤ ∥f o − ˇ f ∥L2(PX ) Approximation error (bias) + ∥ˇ f − ˆ f ∥L2(PX ) Sample deviation (variance) ܇࿅ޡࠩ࠷খԽݩʹୡ͢ΔલʹࢭΊΔ (early stopping) ͜ͱͰਖ਼ଇԽ͕ಇ͘ɽ ແݶ࣍ݩϞσϧ (RKHS) ͸աֶश͠΍͍͢ͷͰؾΛ෇͚Δඞཁ͕͋Δɽ 34 / 42
  31. ղੳʹ༻͍Δ৚݅ ௨ৗɼҎԼͷ৚݅Λߟ͑Δɽ ʢ౷ܭཧ࿦Ͱ΋ಉ༷ͷԾఆΛ՝͢ఆ൪ͷԾఆʣ (Caponnetto and de Vito, 2007, Dieuleveut et

    al., 2016, Pillaud-Vivien et al., 2018) µi = O(i−α) for α > 1. α ͸ RKHSHk ͷෳࡶ͞Λಛ௃͚ͮΔɽ(খ͍͞ α: ෳࡶɼେ͖͍ α: ୯७) f o ∈ Tr (L2 (PX )) for r > 0. f o ͕ RKHS ͔ΒͲΕ͚ͩ “͸Έग़͍ͯΔ͔” Λಛ௃͚ͮɽ r = 1/2 ͸ f o ∈ Hk ʹରԠɽ(r < 1/2: ͸Έग़ͯΔ, r ≥ 1/2: ؚ·ΕΔ) ∥f ∥L∞(PX ) ≲ ∥f ∥1−µ L2(PX ) ∥f ∥µ Hk (∀f ∈ Hk ) for µ ∈ (0, 1]. Hk ʹؚ·Ε͍ͯΔؔ਺ͷ׈Β͔͞Λಛ௃͚ͮɽ ʢখ͍͞ µ: ׈Β͔ʣ ˞ ࠷ޙͷ৚݅ʹ͍ͭͯ: f ∈ W m([0, 1]d ) (Sobolev ۭؒ) ͔ͭ PX ͷ୆͕ [0, 1]d Ͱີ౓ؔ਺Λ࣋ͪɼͦͷີ౓͕Լ͔Β͋Δఆ਺ c > 0 Ͱ཈͑ΒΕ͍ͯΕ͹ɼ µ = d/(2m) ͰͳΓͨͭɽ 35 / 42
  32. ऩଋϨʔτ όΠΞε-όϦΞϯεͷ෼ղ: ∥f o − gt∥2 L2(PX ) ≲ ∥f

    o − f ∗ t ∥2 L2(PX ) (a): Bias + ∥f ∗ t − ˆ ft∥2 L2(PX ) (b): Variance + ∥ˆ ft − gt∥2 L2(PX ) (c): SGD deviation (a) (ηt)−2r , (b) (ηt)1/α+(ηt)µ−2r n , (c) η(ηt)1/α−1 (a) ޯ഑๏ͷղͷσʔλʹؔ͢Δظ଴஋ͱਅͷؔ਺ͱͷζϨ (Bias)ɽ (b) ޯ഑๏ͷղͷ෼ࢄ (Variance)ɽ (c) ֬཰తޯ഑Λ༻͍Δ͜ͱʹΑΔมಈ. ߋ৽਺ t Λେ͖͘͢Δͱ Bias ͸ݮΔ͕ Variance ͕૿͑Δɽ͜ΕΒΛόϥϯε͢ Δඞཁ͕͋Δ (Early stopping)ɽ Theorem (Multi-pass SGD ͷऩଋϨʔτ (Pillaud-Vivien et al., 2018)) η = 1/(4 supx k(x, x)2) ͱ͢Δɽ µα < 2rα + 1 < α ͷ࣌ɼt = Θ(nα/(2rα+1)) ͱ͢Ε͹ɼ E[L(gt )] − L(f o) = O(n−2rα/(2rα+1)). µα ≥ 2rα + 1 ͷ࣌ɼt = Θ(n 1 µ (log n) 1 µ ) ͱ͢Ε͹ɼE[L(gt )] − L(f o) = O(n−2r/µ). 36 / 42
  33. Natural gradient ͷऩଋ Natural gradient (ࣗવޯ഑๏): ˆ ft = ˆ

    ft−1 − η(Σ + λI)−1(Σˆ ft−1 − E[KX Y ]). (unlabeled data ͕୔ࢁ͋Γ Σ ͸ྑ͘ਪఆͰ͖Δઃఆ; GD ͷղੳ (Murata and Suzuki, 2020)) Theorem (Natural gradient ͷऩଋ (Amari et al., 2020)) E[∥ˆ ft − f o∥2 L2(PX ) ] ≲ B(t) + V (t), ͨͩ͠ɼB(t) = exp(−ηt) ∨ (λ/(ηt))2r , V (t) = (1 + ηt) λ−1B(t) + λ− 1 α n + (1 + tη)4 (1 ∨ λ2r−µ)λ− 1 α n . ಛʹɼλ = n− α 2rα+1 , t = Θ(log(n)) Ͱ E[∥ˆ ft − f o∥2 L2(PX ) ] = O(n− 2rα 2rα+1 log(n)4). ˞ όΠΞε͸ٸ଎ʹऩଋ͢Δ͕ɼόϦΞϯε΋଎͘૿େ͢Δɽ ˠ Preconditioning ͷͨΊߴप೾੒෼͕ૣΊʹग़ݱ͢ΔɽΑΓૣΊʹࢭΊ ͳ͍ͱաֶश͢Δɽ 37 / 42
  34. ࡞༻ૉ Bernstein ͷෆ౳ࣜ Σ = Ex [Kx K∗ x ]:

    Σf = ∫ k(·, x)f (x)dPx (x) Σ = 1 n ∑ n i=1 Kxi K∗ xi : Σf = 1 n ∑ n i=1 k(·, xi )f (xi ) Σλ := Σ + λIɼF∞ (λ) := supx K∗ x Σ−1 λ Kx ͱ͢ΔɽҎԼͷΑ͏ͳධՁ͕ඞཁ: ∥Σ−1 λ (Σ − Σ)Σ−1 λ ∥ ≲ √ F∞ (λ)β n + (1 + F∞ (λ))β n with prob. 1 − δɽͨͩ͠ɼβ = log(4Tr[ΣΣ−1 λ ] δ )ɽ ˠ ܦݧ෼෍ͱਅͷ෼෍ͷͣΕΛό΢ϯυɽ Theorem (ࣗݾڞ໾࡞༻ૉͷ Bernstein ͷෆ౳ࣜ (Minsker, 2017)) (Xi )n i=1 ͸ಠཱͳࣗݾڞ໾࡞༻ૉͷ֬཰ม਺Ͱ E[Xi ] = 0 ͔ͭɼ σ2 ≥ ∥ ∑ n i=1 E[X2 i ]∥, U ≥ ∥Xi ∥ ͱ͢Δɽr(A) = Tr[A]/∥A∥ ͱͯ͠ɼ P ( n ∑ i=1 Xi ≥ t ) ≤ 14r( ∑ n i=1 E[X2 i ]) exp ( − t2 2(σ2 + tU/3) ) . Xi = Σ−1 λ Kxi K∗ xi Σ−1 λ ͱ͢Δɽ (Tropp (2012) ΋ࢀর) 39 / 42
  35. ਖ਼ଇԽ͋Γͷ֬཰త࠷దԽ ೋ৐ଛࣦΛ֦ுͯ͠ɼҰൠͷ׈Β͔ͳತଛࣦؔ਺ ℓ Λߟ͑Δɽ ʢ൑ผ໰୊ͳͲʣ ਖ਼ଇԽ͋Γͷظ଴ଛࣦ࠷খԽ: min f ∈Hk E[ℓ(Y

    , f (X))] + λ∥f ∥2 Hk =: Lλ (f ). ͜ΕΛ SGD Ͱղ͘ɽ໨తؔ਺͕ λ-ڧತͰ͋Δ͜ͱΛར༻ɽ gt+1 = gt − ηt (ℓ′(yt, gt (xt )) + λgt ). ¯ gT+1 = T+1 ∑ t=1 2(c0+t−1) (2c0+T)(T+1) gt (ଟ߲ࣜฏۉ). Ծఆɿ(i) ℓ ͸ γ-ฏ׈ɼ∥ℓ′∥∞ ≤ M, (ii) k(x, x) ≤ 1. gλ = argming∈Hk Lλ (g). Theorem (Nitanda and Suzuki (2019)) ద੾ͳ c0 > 0 ʹରͯ͠ ηt = 2/(λ(c0 + t)) ͱ͢Ε͹ɼ E[Lλ (¯ gT+1 ) − Lλ (gλ )] ≲ M2 λ(c0 + T) + γ + λ T + 1 ∥g1 − gλ ∥2 Hk . ͞ΒʹϚϧνϯήʔϧ֬཰ूதෆ౳ࣜΑΓ High probability bound ΋ಘΒΕΔɽ ൑ผ໰୊ͳΒ strong low noise condition ͷ΋ͱ൑ผޡࠩͷࢦ਺ऩଋ΋ࣔͤΔɽ40 / 42
  36. Ϛϧνϯήʔϧ Hoeffding ͷෆ౳ࣜ Theorem (Ϛϧνϯήʔϧ Hoeffding ܕूதෆ౳ࣜ (Pinelis, 1994)) ֬཰ม਺ྻ:

    D1, . . . , DT ∈ Hk ɽE[Dt ] = 0ɼ∥Dt∥Hk ≤ Rt (a.s.) ͱ͢Δɽ ∀ϵ > 0 ʹର͠ P [ max 1≤t≤T ∥ t ∑ s=1 Ds∥Hk ≥ ϵ ] ≤ 2 exp ( − ϵ2 2 ∑ T t=1 R2 t ) . Dt = E[¯ gT+1|Z1, . . . , Zt ] − E[¯ gT+1|Z1, . . . , Zt−1 ], ͨͩ͠ Zt = (xt, yt ) ͱ͢Ε͹ɼ ∑ T t=1 Dt = ¯ gT+1 − E[¯ gT+1 ] ͱͳΓɼظ଴஋ͱ࣮ ݱ஋ͷͣΕΛ཈͑ΒΕΔɽ 41 / 42
  37. Ϛϧνϯήʔϧ Hoeffding ͷෆ౳ࣜ Theorem (Ϛϧνϯήʔϧ Hoeffding ܕूதෆ౳ࣜ (Pinelis, 1994)) ֬཰ม਺ྻ:

    D1, . . . , DT ∈ Hk ɽE[Dt ] = 0ɼ∥Dt∥Hk ≤ Rt (a.s.) ͱ͢Δɽ ∀ϵ > 0 ʹର͠ P [ max 1≤t≤T ∥ t ∑ s=1 Ds∥Hk ≥ ϵ ] ≤ 2 exp ( − ϵ2 2 ∑ T t=1 R2 t ) . Dt = E[¯ gT+1|Z1, . . . , Zt ] − E[¯ gT+1|Z1, . . . , Zt−1 ], ͨͩ͠ Zt = (xt, yt ) ͱ͢Ε͹ɼ ∑ T t=1 Dt = ¯ gT+1 − E[¯ gT+1 ] ͱͳΓɼظ଴஋ͱ࣮ ݱ஋ͷͣΕΛ཈͑ΒΕΔɽ (ิ଍) Lλ ͸ RKHS ϊϧϜʹؔͯ͠ λ-ڧತͰ͋Δ͜ͱΑΓɼ ∥¯ gT+1 − gλ ∥Hk ≤ O( 1 λ2T ) ͕ߴ͍֬཰Ͱ੒Γཱͭɽ࣮͸ ∥ · ∥∞ ≤ ∥ · ∥Hk Ͱ΋͋ΔͷͰɼ |P(Y = 1|X) − P(Y = −1|X)| ≥ δ ͳΔϚʔδϯ৚݅ (strong low noise condition) ͷ΋ͱɼ׬શͳ൑ผ͕ߴ͍֬཰ͰͰ͖ΔΑ͏ʹͳΔɽ 41 / 42
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