Pith. sign in

REVIEW 2 major objections 4 minor 1 cited by

Denoising growth complexity: Data geometry and certified schedules for diffusion sampling

T0 review · 2 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read KL error of diffusion sampling is governed by one data-geometry curve, the denoising growth complexity.

desk verdict Genuinely new DGC-based KL bound with a clean proof and useful multi-block consequences; the 'fully data-certified' claims overreach because the certified estimators require exact denoisers and are not instantiated for learned scores. read the letter →

arxiv 2607.26285 v1 pith:GEY2JBYJ submitted 2026-07-28 math.ST cs.LGcs.NAmath.NAstat.MLstat.TH

Martin J. Wainwright This is my paper · ORCID
classification math.STcs.LGcs.NAmath.NAstat.MLstat.TH MSC 60J6062B1062G05
keywords denoisinggrowthcomplexitydiffusionsamplingKLdivergencestochasticinnovationsstepsizeschedulesdatacertificationrate-distortionGaussianmixtures
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the KL error of a simple stochastic Euler diffusion sampler can be written as a sum over time steps, where each step's contribution is a relative stepsize times an increment of a single curve derived from the data's denoising error. That curve, the denoising growth complexity (DGC), is defined by a log-time weighted integral of the derivative of the mean-squared denoising error along the Gaussian heat flow. Because the curve is additive over intervals, it can be estimated from forward heat-path samples and used to select stepsize schedules with explicit, data-certified KL guarantees. A sympathetic reader should care because this gives one mechanism that both explains dimension-adaptive behavior observed in practice and supplies practical schedules with guarantees.

What carries the argument

Denoising growth complexity, H(a,b) = (1/2)∫_a^b h'(t)/t dt, where h(t) is the minimum mean-squared error of denoising the latent variable from the heat-path observation at time t. It is additive over time intervals and has an equivalent information-theoretic form involving mutual information; in precision coordinates it is controlled by the non-increasing MSE that drives the innovations SDE. Its role is to give a local, interval-wise control of the Euler discretization error and to give a data-estimable target for stepsize selection.

What would settle it

For a Gaussian prior Z ∼ N(0,1), compute exactly the one-step KL deficit between the innovations transition and its Euler approximation and compare it with the relative stepsize times the DGC increment. If the ratio ever exceeds 1, or fails to approach 1/2 as the interval shrinks, the local bound behind the main theorem is false.

Watch

Extended reading notes

Core claim

The paper's central result is that for the SI-Euler scheme—the Euler–Maruyama discretization of the stochastic-innovations SDE associated with the heat path—the KL divergence from the true smoothed law to the sampler output is bounded by a sum of relative stepsizes times DGC increments over the time grid, plus an initialization term. The proof proceeds through a one-step bound: each local KL deficit is at most the relative stepsize times the DGC increment over that interval, and this bound is sharp up to a factor of two as the interval shrinks. The same DGC function is then shown to be estimable from data via denoising increments, with a constant-factor sandwich that yields fully certified s

Load-bearing premise

The certificate step requires an i.i.d. sample of the latent variable that is independent of any data used to fit the scores and has a known p-th moment bound; reuse the same sample for both tasks and the Monte Carlo estimate of H is biased and the certified KL guarantee no longer follows.

Editorial extensions

If this is right

  • A single geometric schedule can sample to ε accuracy in KL using O(H(δ,T) log(T/δ)/ε plus initialization cost) score evaluations, with linear dimension scaling and no logarithmic overhead in the worst case.
  • K-block schedules with optimal geometric multipliers achieve D_KL ≤ 4 C_DGC(P)/N plus initialization, and the optimal K-block partition can be computed by dynamic programming.
  • With a hold-out sample of the latent variable satisfying a known p-th moment bound, DGC increments can be estimated so that the final KL guarantee holds with probability at least 1−η, up to a factor-of-two loss plus a confidence correction.
  • Analytic upper bounds on H via covariance, rate-distortion, metric entropy, and Poincaré constant recover and sharpen existing diffusion-sampling guarantees, including linear dimension scaling, dimension-free bounds for bounded models, log-K for Gaussian mixtures, and log dependence on the Poincaré constant.
  • In log heat-time, single-block cost is governed by ∫q while the fine-partition limit is governed by (∫√q)², so the spread ratio quantifies exactly when adaptive schedules help; for a two-point Gaussian mixture the separation can be from Θ(log(R²/δ)) to Θ(1).

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If DGC estimation is robust enough, certified schedules could be built directly from raw, unlabelled data by running forward heat paths, without retraining or knowing the denoiser analytically.
  • The factor-of-two local sharpness suggests the DGC bound is close to tight for Euler-type samplers, so further speed-ups would need higher-order or randomized-midpoint discretizations of the innovations SDE rather than better Euler stepsize choices.
  • The perturbed sandwich for learned denoisers gives a practical training target: reduce the weighted denoiser error below the relevant DGC increment, otherwise certification is impossible; this could be used as a stop-rule during score matching.
  • The √q-versus-q comparison predicts that multimodal or hierarchical distributions with well-separated resolution times are exactly where K-block schedules pay off most, a testable prediction on synthetic mixture benchmarks.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper introduces the denoising growth complexity (DGC), H(a,b) = (1/2)∫_a^b h'(t)/t dt, where h is the MSE of the optimal denoiser along the Gaussian heat flow. The central result, Theorem 1, bounds the KL error of a stochastic-innovations Euler scheme on an arbitrary grid by ∑ (t_j/t_{j+1}-1) H(t_{j+1},t_j) plus the initialization error. The proof is via a one-step defect bound (Lemma 5) obtained from an exact entropy/cross-entropy representation and the conditional I-MMSE identity. From this, the paper derives single-block geometric schedules (Corollary 1), a tail-robust data-dependent estimator and certified single-block procedure (Proposition 1 and Section 3.2.2), multi-block schedules (Theorem 2), certified multi-block schedules (Corollary 2), optimal block-boundary choice by dynamic programming, and a fine-partition limit governed by the log-time DGC density. It also gives information-theoretic upper bounds via covariance, rate-distortion, metric entropy, and the Poincaré constant, recovering and sharpening several existing diffusion-sampling guarantees. The main mathematical inequality is elegant, local, additive, and appears correct.

Significance. If the results hold, Theorem 1 is a significant unification: it provides an explicit, additive, parameter-free KL bound for diffusion sampling, with a short elementary proof, and it recovers or sharpens a range of prior dimension, intrinsic-dimension, mixture, and Poincaré-constant guarantees. The multi-block versus single-block comparison through the DGC spread is conceptually clean and yields concrete logarithmic-to-constant separations. The paper also has the praiseworthy feature of giving explicit constants and identifiable statistical estimators, with no fitted parameters. However, the advertised 'fully data-certified' contribution has a substantial implementability gap in the learned-score setting: the certified estimator requires oracle access to exact conditional mean denoisers. This limits the practical scope of the Q2 contribution until the gap is addressed.

major comments (2)
  1. [Section 3.2.1 / Proposition 1] Proposition 1 is advertised as a fully data-certified guarantee, but the statistic Q in Eq. (18) is built from the exact denoisers μ_{vℓ}(X_{vℓ}). For an unknown target P_Z these conditional expectations are not available from i.i.d. samples alone. The hold-out discussion in Section 3.3.1 addresses independence between the Monte Carlo sample and the score-training data, but it does not address the more basic fact that exact denoisers are unknown. Consequently, the single-block certified procedure in Section 3.2.2 is an oracle certification; it is not implementable in the primary setting of interest, namely sampling from an unknown distribution with estimated score functions.
  2. [Section 3.2.3 / Corollary 2] For estimated denoisers, the perturbed sandwich (25) states a population-level bound involving E(a,b), the sum of squared denoiser errors. No finite-sample high-probability upper bound on E(a,b) is derived, and the tail-robust Monte Carlo machinery of Proposition 1 is not re-run for the learned statistic based on eD. Thus the certified multi-block multipliers in Corollary 2, which use the Proposition 1 estimates bH_k, are not certified when scores are learned. The same gap propagates to the data-dependent dynamic program in Section 4.1.3. This is load-bearing for the paper's Q2 claim; a finite-sample control on the denoiser-error term, or a modified estimator that bypasses exact denoiser evaluation, is needed before the certified schedule is implementable with learned scores.
minor comments (4)
  1. [Eq. (17), Section 2.2.4] The same symbol H is used for the DGC function and for the dyadic approximation H(a,b)=1/2 Σ D(vℓ,vℓ+1)/vℓ, making the sandwich '1/2 H(a,b) ≤ H(a,b) ≤ H(a,b)' confusing. A distinct symbol such as H̄ or H̃ would improve readability.
  2. [Eq. (32), Proposition 4] The displayed formula has an unbalanced parenthesis/brace in the log term: 'dlog(1+8dκ/ε) + 1' should likely be d log(1+8dκ/ε) + 1 inside the curly braces. Please correct the typesetting.
  3. [References] Several references have formatting problems: [RBD+22] has garbled author initials, and [L WCC23] contains a stray space. These should be cleaned up.
  4. [Figure 3] The legend text 'g = 9.275 k SkHk = 12.3' is garbled; presumably it should read ∑ √(S_k H_k) or the equivalent. Please fix the figure caption and labels.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Theorem 1 is a proved inequality and the data-certified schemes are confidence-interval constructions; the learned-score limitation is a completeness gap, not a circular reduction.

full rationale

The paper's derivation chain is self-contained. Theorem 1 follows from Lemma 5, an analytic one-step bound Γ_Eul(s,s+h) ≤ (h/s) G(s,s+h) proved in Section 5.1 from the exact identity Γ_Eul(s,s+h)= h/2 g(s) − (1/2)∫_s^{s+h} g(r)dr and monotonicity of the precision-space MSE; no free parameter is fitted to match observed KL error. Corollary 1, Theorem 2, and Corollary 2 are algebraic consequences of Theorem 1 via additivity of H and Cauchy–Schwarz, not restatements of their inputs. The data-dependent certification (Proposition 1, Section 3.2.2, Corollary 2) estimates the fixed population quantity H(a,b) with denoising-increment Monte Carlo and uses explicit tail-robust upper confidence corrections; the final KL guarantee is conditional on those intervals, so the 'prediction' is not forced by construction. The only self-citation, [Wai26], appears as a comparison for Proposition 4 and is not load-bearing. The limitations flagged in the text—Section 3.2.3's perturbed sandwich (25) with no finite-sample upper bound on E(a,b), and Section 3.3.1's note that 'if we also incorporate score-based errors, these samples must not be used to fit the score functions'—are real implementability gaps in the advertised data-certified claims, but they do not make any claimed result equal to its input by definition. Hence no circularity.

Assumptions & free parameters 0 free parameters · 7 assumptions · 0 invented entities

The paper introduces no unexplained physical or mathematical entities. The DGC measure is a derived function of the data distribution, defined via the heat flow and MMSE derivative, and is not a postulated quantity needing independent evidence. The only substantive assumptions are the standard moment/smoothness conditions and the explicit hold-out requirement for certified estimates.

assumptions (7)
  • domain assumption The MSE function h is differentiable and its derivative h' is integrable (Section 2.1).
    The definition of H and the proof of Theorem 1 require h differentiable; this is stated as an assumption in Section 2.1.
  • domain assumption Z has finite second moments (Section 2.1, Theorem 1).
    Needed for the existence of the MMSE and the covariance-based bounds.
  • standard math The stochastic innovations SDE representation dY_λ = m_λ(Y_λ)dλ + dB_λ (Eq. 52a) from nonlinear filtering theory.
    Classical results of Fujisaki, Kallianpur, Kunita and others ensure this representation; the paper cites [KS68, FKK72].
  • domain assumption For tail-robust estimation, a known p-th moment bound (19) on the denoising function μ_t(X_t) with constant M_p (Section 3.2.1).
    Proposition 1 requires the constant M_p; if it is unknown or the bound fails, the certified confidence interval is invalid.
  • domain assumption For certified guarantees, the samples used to estimate H are independent of the score-fitting data (Section 3.3.1 and Section 3.2.2).
    The paper explicitly notes that unbiasedness of the Monte Carlo estimates requires a hold-out set not used to fit the score functions.
  • domain assumption In Proposition 4, the target satisfies the Poincaré inequality (31a) and a one-sided L-smoothness condition (31b).
    These assumptions are used to transfer the guarantee from the smoothed law P_δ to the original law P_Z, and to bound the density ratio.
  • standard math I-MMSE identity (Guo-Shamai-Verdú) and the conditional I-MMSE for Gaussian observation processes.
    Used in the proof of the information representation (8), Lemma 5, and Proposition 3.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Denoising growth complexity: Data geometry and certified schedules for diffusion sampling." pith.science (2026). https://pith.science/paper/GEY2JBYJ

@misc{pith2026260726285,
  author       = {Pith},
  title        = {Pith review of: Denoising growth complexity: Data geometry and certified schedules for diffusion sampling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GEY2JBYJ}},
  note         = {Machine review of arXiv:2607.26285}
}
abstract

Two central challenges in diffusion-based sampling are the theoretical one of understanding their remarkable effectiveness even in high-dimensional settings, and the practical one of designing algorithms with certified performance guarantees. We show that these questions are intimately connected via the \emph{denoising growth complexity} ($\mathsf{DGC}$). It is a geometric measure defined by a log-time weighted integral of the derivative of the denoising mean-squared error along the Gaussian heat flow. We show how the $\mathsf{DGC}$ increments lead to a simple and explicit bound on the KL error of an Euler scheme applied to the stochastic innovations representation. The bound is local along the path: each step is controlled by the corresponding $\mathsf{DGC}$ increment and its relative stepsize. This structure allows us to derive KL sampling guarantees for optimized stepsize schedules, both in a simpler single-block setting and in a more refined $K$-block setting. The $\mathsf{DGC}$ function has a natural martingale structure, which we exploit to develop fully data-certified versions of these algorithms. It also admits information-theoretic upper bounds in terms of covariance, rate distortion, metric entropy, and the Poincar'e constant, thereby recovering and sharpening a range of existing diffusion-sampling guarantees, as well as giving new results. In log heat-time, the fine partition limit is governed by an integral involving the square root of the $\mathsf{DGC}$ density, whereas a single-block schedule depends on its ordinary integral. This comparison precisely characterizes when adaptation to data geometry yields substantial computational gains, including logarithmic-to-constant separations for simple Gaussian mixture models.

Figures

Figures reproduced from arXiv: 2607.26285 by the authors.

Figure 1
Figure 1. Plots of the log-time DGC density (9) for three different choices of the latent variable Z. (a) A standard Gaussian N (0, 1). (b) A Gaussian mixture model with 2 components. (c) A hierarchical Gaussian mixture model with the nested structure shown in [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Structure of the hierarchical mixture model. (a) The density is separated into mixture components that are resolved at a sequence of scales. (b) Waterfall structure of the heat path, showing the resolution of mixture components of Z at four distinct heat times. Finally, panel (c) in [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Plots of approximations of R L 0 p q(r)dr based on the uniform K-block partitions UK for K ∈ {1, 2, 4}. The top row corresponds to a two-component Gaussian mixture with small variances, whereas the bottom row corresponds to a hierarchical mixture model. Let us now quantify how rapidly the block approximations based on UK approach the fine-partition limit under some simple global conditions. Suppose that f = √ q is c… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity

    cs.LG 2026-08 conditional novelty 7.0 of 10

    Unmasking growth complexity directly controls KL discretization error in masking diffusion and enables certified, data-adaptive schedules that approach oracle efficiency.

Reference graph

Works this paper leans on

300 extracted references · 4 canonical work pages · cited by 1 Pith paper

  1. [1]

    Agarwal and S

    A. Agarwal and S. Negahban and M. J. Wainwright , journal =. Fast global convergence of gradient methods for high-dimensional statistical recovery , volume =

  2. [2]

    Agarwal and P

    A. Agarwal and P. L. Bartlett and P. Ravikumar and M. J. Wainwright , journal =. Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization , topic =

  3. [3]

    Agarwal and S

    A. Agarwal and S. Negahban and M. J. Wainwright , journal =. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , volume =

  4. [4]

    Agarwal and S

    A. Agarwal and S. Negahban and M. J. Wainwright , booktitle =. Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions , topic =

  5. [5]

    El Alaoui and X

    A. El Alaoui and X. Cheng and A. Ramdas and M. J. Wainwright and M. I. Jordan , booktitle =. Asymptotic behavior of _p -based

  6. [6]

    Albergo and Nicholas M

    Michael S. Albergo and Nicholas M. Boffi and Eric Vanden-Eijnden , journal =

  7. [7]

    Altschuler, J. M. and Chewi, S. , institution =. Faster high-accuracy log-concave sampling via algorithmic warm starts , url =

  8. [8]

    A. A. Amini and M. J. Wainwright , journal = annstat, pages =. High-dimensional analysis of semdefinite relaxations for sparse principal component analysis , volume =

Show all 300 references
  1. [9]

    A. A. Amini and M. J. Wainwright , journal = annstat, number =. Sampled forms of functional

  2. [10]

    Anderson, B. D. , journal =. Reverse-time diffusion equation models , volume =

  3. [11]

    Azangulov and G

    I. Azangulov and G. Deligiannidis and J. Rousseau , journal =. Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions , year =

  4. [12]

    Balakrishnan and M

    S. Balakrishnan and M. J. Wainwright and B. Yu , journal =. Statistical guarantees for the

  5. [13]

    and Deligiannidis, G

    Benton, J. and Deligiannidis, G. and Doucet, A. , journal =. Error bounds for flow matching methods , year =

  6. [14]

    and De Bortoli, V

    Benton, J. and De Bortoli, V. and Doucet, A. and Deligiannidis, G. , booktitle =. Nearly d -Linear Convergence Bounds for Diffusion Models via Stochastic Localization , url =

  7. [15]

    Bhatia and A

    K. Bhatia and A. Pananjady and P. L. Bartlett and A. D. Dragan and M. J. Wainwright , booktitle =. Preference learning along multiple criteria: A game-theoretic perspective , year =

  8. [16]

    arXiv , arxivid =:2208.05314 , journal =

    arXiv

  9. [17]

    arXiv , arxivid =:2202.02763 , title =

    Advances in Neural Information Processing Systems , doi =. arXiv , arxivid =:2202.02763 , title =

    arXiv

  10. [18]

    De Bortoli and M

    V. De Bortoli and M. Hutchinson and P. Wirnsberger and A. Doucet , journal =. Target Score Matching , year =

  11. [19]

    Brooks and A

    S. Brooks and A. Gelman and G. L. Jones and X. L. Meng , publisher =. Handbook of

  12. [20]

    Cai and R

    J. Cai and R. Chen and M. J. Wainwright and L. Zhao , institution =. Doubly high-dimensional contextual bandits:

  13. [21]

    and Li, G

    Cai, C. and Li, G. , journal =. Minimax optimality of the probability flow

  14. [22]

    Celentano and M

    M. Celentano and M. J. Wainwright , institution =. Challenges of the inconsistency regime:

  15. [23]

    Cetin and L

    M. Cetin and L. Chen and J. W. Fisher and A. T. Ihler and R. L. Moses and M. J. Wainwright and A. S. Willsky , journal =. Distributed fusion in sensor networks , volume =

  16. [24]

    Log-Concave Sampling , url =

    Sinho Chewi , note =. Log-Concave Sampling , url =

  17. [25]

    Cheng and P

    X. Cheng and P. L. Bartlett , booktitle =. arXiv , arxivid =:1705.09048 , month =

    arXiv

  18. [26]

    Chen and M

    L. Chen and M. J. Wainwright and M. Cetin and A. Willsky , booktitle =. Multitarget-multisensor data association using the tree-reweighted \ max-product algorithm , year =

  19. [27]

    Chen and M

    J. Chen and M. Stern and M. J. Wainwright and M. I. Jordan , booktitle =. Kernel Feature Selection via Conditional Covariance Minimization , year =

  20. [28]

    Chen and R

    Y. Chen and R. Dwivedi and M. J. Wainwright and B. Yu , journal =. Fast

  21. [29]

    Chen and L

    J. Chen and L. Song and M. J. Wainwright and M. I. Jordan , booktitle =. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation , year =

  22. [30]

    Chen and M

    J. Chen and M. I. Jordan and M. J. Wainwright , booktitle =

  23. [31]

    Chen and L

    J. Chen and L. Song and M. J. Wainwright and M. I. Jordan , booktitle =. L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data , year =

  24. [32]

    Chen and R

    Y. Chen and R. Dwivedi and M. J. Wainwright and B\ . Yu , journal =. Fast mixing of

  25. [33]

    Chewi and M

    S. Chewi and M. A. Erdogdu and M. B. Li and R. Shen and M. Zhang , booktitle =. arXiv , arxivid =:2112.12662 , title =

    arXiv

  26. [34]

    Chen and H

    H. Chen and H. Lee and J. Lu , booktitle =. 2211.01916 , title =

    arXiv

  27. [35]

    Chewi and J

    S. Chewi and J. Pont and J. Li and C. Lu and S. Narayanan , month =. Query low bounds for log-concave sampling , year =

  28. [36]

    Chen and S

    S. Chen and S. Chewi and J. Li and Y. Li and A. Salim and A. R. Zhang , booktitle =. arXiv , arxivid =:2209.11215 , title =

    arXiv

  29. [37]

    Chen and K

    M. Chen and K. Huang and T. Zhao and M. Wang , eprint =. arXiv preprint arXiv:2302.07194 , title =

    arXiv

  30. [38]

    Chen and S

    S. Chen and S. Chewi and H. Lee and Y. Li and J. Lu and A. Salim , eprint =. arXiv preprint arXiv:2305.11798 , title =

    arXiv

  31. [39]

    and Mei, S

    Chen, M. and Mei, S. and Fan, J. and Wang, M. , journal =. An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization , url =

  32. [40]

    Chen and S

    F. Chen and S. Chewi and C. Daskalakis and A. Rakhlin , journal =. High-accuracy sampling for diffusion models and log-concave distributions , year =

  33. [41]

    and Gatmiry, K

    Chen, Y. and Gatmiry, K. , institution =. A Simple Proof of the Mixing of

  34. [42]

    Chen and M

    Y. Chen and M. J. Wainwright , institution =. Fast low-rank estimation by projected gradient descent:

  35. [43]

    Chichignoud and J

    M. Chichignoud and J. Lederer and M. J. Wainwright , journal =. A practical scheme and fast algorithm to tune the Lasso with optimality guarantees , volume =

  36. [44]

    Conforti and A

    G. Conforti and A. Durmus and M. G. Silveri , eprint =. arXiv preprint arXiv:2308.12240 , month =

    arXiv

  37. [45]

    Croitoru and V

    F.-A. Croitoru and V. Hondru and R. T. Ionescu and M. Shah , doi =. Diffusion Models in Vision: A Survey , volume =. IEEE Transactions on Pattern Analysis and Machine Intelligence , number =

  38. [46]

    A. S. Dalalyan , booktitle =. Further and stronger analogy between sampling and optimization:

  39. [47]

    Stochastic Processes and their Applications , number =

    User-friendly guarantees for the. Stochastic Processes and their Applications , number =. doi:10.1016/j.spa.2019.02.016 , eprint =

    2019 doi

  40. [48]

    Daskalakis and A

    C. Daskalakis and A. G. Dimakis and R. M. Karp and M. J. Wainwright , booktitle =. Probabilistic Analysis of Linear Programming Decoding , year =

  41. [49]

    Daskalakis and A

    C. Daskalakis and A. G. Dimakis and R. M. Karp and M. J. Wainwright , journal =. Probabilistic analysis of linear programming decoding , volume =

  42. [50]

    A. G. Dimakis and A. Sarwate and M. J. Wainwright , booktitle =. Geographic

  43. [51]

    A. G. Dimakis and A. Sarwate and M. J. Wainwright , journal =. Geographic gossip:

  44. [52]

    A. G. Dimakis and A. A. Gohari and M. J. Wainwright , journal =. Guessing facets:

  45. [53]

    A. G. Dimakis and P. B. Godfrey and Y. Wu and M. J. Wainwright and K. Ramchandran , journal = ieeeit, month =. Network coding for distributed storage systems , topic =

  46. [54]

    A. G. Dimakis and M. J. Wainwright , booktitle =. Guessing

  47. [55]

    Dolecek and Z

    L. Dolecek and Z. Zhang and V. Anantharam and M. J. Wainwright and B. Nikolic , booktitle =. Analysis of absorbing sets for array-based

  48. [56]

    Dolecek and Z

    L. Dolecek and Z. Zhang and M. J. Wainwright and V. Anantharam and M. J. Wainwright , booktitle =. Evaluation of the low frame error rate performance of

  49. [57]

    Dolecek and Z

    L. Dolecek and Z. Zhang and V. Anantharam and M. J. Wainwright and B. Nikolic , journal = ieeeit, month =. Analysis of Absorbing Sets and Fully Absorbing Sets for Array-Based

  50. [58]

    Dolecek and P

    L. Dolecek and P. Lee and Z. Zhang and V. Anantharam and B. Nikolic and M. J. Wainwright , journal =. Predicting error floors of structured

  51. [59]

    Doucet and W

    A. Doucet and W. Grathwohl and A. D. G. Matthews and H. Strathmann , booktitle =. Score‑Based Diffusion Meets Annealed Importance Sampling , year =

  52. [60]

    Duan and M

    Y. Duan and M. Wang and M. J. Wainwright , journal =. Optimal value estimation using kernel-based temporal difference methods , volume =

  53. [61]

    Duan and M

    Y. Duan and M. J. Wainwright , institution =. Policy evaluation from a single path: Multi-step methods, mixing and mis-specification , year =

  54. [62]

    Duan and M

    Y. Duan and M. J. Wainwright , institution =. Taming data-hungry reinforcement learning? Stability in continuous state-action spaces , url =

  55. [63]

    Duchi and A

    J. Duchi and A. Agarwal and M. J. Wainwright , booktitle =. Distributed dual averaging in networks , year =

  56. [64]

    J. C. Duchi and A. Agarwal and M. J. Wainwright , journal =. Dual Averaging for Distributed Optimization:

  57. [65]

    J. C. Duchi and A. Wibisono and M. J. Wainwright and M. I. Jordan , booktitle =. Finite sample convergence rates of zero-order stochastic optimization methods , topic =

  58. [66]

    J. C. Duchi and M. J. Wainwright and M. I. Jordan , booktitle =. Privacy-aware learning , year =

  59. [67]

    J. C. Duchi and P. L. Bartlett and M. J. Wainwright , journal =. Randomized smoothing for stochastic optimization , volume =

  60. [68]

    J. C. Duchi and M. J. Wainwright and M. I. Jordan , booktitle =. Local privacy and statistical minimax rates , year =

  61. [69]

    J. C. Duchi and M. I. Jordan and M. J. Wainwright and Y. Zhang , institution =. Optimality guarantees for distributed statistical estimation , year =

  62. [70]

    J. C. Duchi and M. J. Wainwright and M. I. Jordan , journal =. Privacy-aware learning , volume =

  63. [71]

    J. C. Duchi and M. I. Jordan and M. J. Wainwright and A. Wibisono , journal =. Optimal rates for zero-order optimization: the power of two function evaluations , volume =

  64. [72]

    J. C. Duchi and M. I. Jordan and M. J. Wainwright , journal =. Minimax optimal procedures for locally private estimation , volume =

  65. [73]

    J. C. Duchi and M. J. Wainwright , institution =. Distance-based and continuum Fano inequalities with applications to statistical estimation , year =

  66. [74]

    The Annals of Applied Probability , number =

    Nonasymptotic convergence analysis for the unadjusted. The Annals of Applied Probability , number =. doi:10.1214/16-AAP1238 , eprint =

    doi

  67. [75]

    Dwivedi and Y

    R. Dwivedi and Y. Chen and M. J. Wainwright and B. Yu , booktitle =. Log-concave sampling:

  68. [76]

    Dwivedi and N

    R. Dwivedi and N. Ho and K. Khamaru and M. J. Wainwright and M. I. Jordan , booktitle =. Theoretical guarantees for the

  69. [77]

    Dwivedi and N

    R. Dwivedi and N. Ho and K. Khamaru and M. J. Wainwright and M. I. Jordan and B. Yu , institution =. Challenges with

  70. [78]

    Dwivedi and Y

    R. Dwivedi and Y. Chen and M. J. Wainwright and B. Yu , journal =. Log-concave sampling:

  71. [79]

    Dwivedi and N

    R. Dwivedi and N. Ho and K. Khamaru and M. J. Wainwright and M. I. Jordan and B. Yu , journal =. Singularity, Misspecification, and the Convergence Rate of

  72. [80]

    Dwivedi and K

    R. Dwivedi and K. Khamaru and N. Ho and M. J. Wainwright and M. I. Jordan and B. Yu , booktitle =. Sharp analysis of expectation-maximization for weakly identifiable models , year =

  73. [81]

    Dwivedi and C

    R. Dwivedi and C. Singh and B. Yu and M. J. Wainwright , journal =. Revisiting minimum description length complexity in overparameterized models , volume =

  74. [82]

    Efron , doi =

    B. Efron , doi =. Journal of the American Statistical Association , number =

  75. [83]

    Eldan , journal =

    R. Eldan , journal =. Thin shell implies spectral gap up to polylog via a stochastic localization scheme , volume =

  76. [84]

    F. Z. Faizal and A. Ozdaglar and M. J. Wainwright , institution =. Finite-sample guarantees for best-response learning dynamics in zero-sum matrix games , year =

  77. [85]

    Feldman and D

    J. Feldman and D. R. Karger and M. J. Wainwright , booktitle =. Using linear programming to decode

  78. [86]

    Feldman and M

    J. Feldman and M. J. Wainwright and D. R. Karger , journal =. Using linear programming to decode binary linear codes , volume =

  79. [87]

    Feldman and T

    J. Feldman and T. Malkin and R. A. Servedio and C. Stein and M. J. Wainwright , journal =

  80. [88]

    Fujisaki and G

    M. Fujisaki and G. Kallianpur and H. Kunita , doi =. Stochastic Differential Equations for the Non Linear Filtering Problem , volume =. Osaka Journal of Mathematics , number =

  81. [89]

    and Zhu, L

    Gao, X. and Zhu, L. , journal =. Convergence analysis for general probability flow

  82. [90]

    arXiv preprint arXiv:2601.10708 , primaryclass =

    High-accuracy and dimension-free sampling with diffusions , year =. arXiv preprint arXiv:2601.10708 , primaryclass =

  83. [91]

    Concentration for (regularized) empirical risk minimization , volume =

    S. Concentration for (regularized) empirical risk minimization , volume =. Sankhya

  84. [92]

    Gelman and J

    A. Gelman and J. Carlin and H. S. Stern and D. B. Dunson and A. Vehtari and D. K. Salomon , journal =

  85. [93]

    Gordon, Robert D. , doi =. Values of. The Annals of Mathematical Statistics , number =

  86. [94]

    and Roberto, C

    Gozlan, N. and Roberto, C. and Samson, P.-M. , journal =. Characterization of

  87. [95]

    and L\'eonard, C

    Gozlan, N. and L\'eonard, C. , journal =. Transport Inequalities. A Survey , volume =

  88. [96]

    Mutual Information and Minimum Mean-Square Error in

    Guo, Dongning and Shamai, Shlomo and Verd. Mutual Information and Minimum Mean-Square Error in. doi:10.1109/TIT.2005.844072 , journal =

    2005

  89. [97]

    Estimation in

    Guo, Dongning and Wu, Yihong and Shamai, Shlomo and Verd. Estimation in. doi:10.1109/TIT.2011.2111010 , journal =

    2011

  90. [98]

    Gupta and L

    S. Gupta and L. Cai and S. Chen , booktitle =. Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel , url =

  91. [99]

    and Kadkhodaie, Z

    Guth, F. and Kadkhodaie, Z. and Simoncelli, E. P. , booktitle =. Learning normalized image densities via dual score matching , url =

  92. [100]

    Hastie and R

    T. Hastie and R. Tibshirani and M. J. Wainwright , publisher =. Statistical learning with sparsity:

  93. [101]

    Haussmann, U. G. and Pardoux, E. , journal =. Time reversal of diffusions , year =

  94. [102]

    Heckel and M

    R. Heckel and M. Simchowitz and K. Ramchandran and M. J. Wainwright , booktitle =. Approximate ranking from pairwise comparisons , topic =

  95. [103]

    Heckel and N

    R. Heckel and N. B. Shah and K. Ramchandran and M. J. Wainwright , journal =. Active Ranking from Pairwise Comparisons and When Parametric Assumptions Don’t Help , volume =

  96. [104]

    Higdon, D. M. , journal =. Auxiliary Variable Methods for

  97. [105]

    Ho and A

    J. Ho and A. Jain and P. Abbeel , journal =. Denoising diffusion probabilistic models , volume =

  98. [106]

    Ho and K

    N. Ho and K. Khamaru and R. Dwivedi and M. J. Wainwright and M. I. Jordan and B. Yu , journal =. Instability, Computational Efficiency and Statistical Accuracy , topic =

  99. [107]

    Huang and Y

    Z. Huang and Y. Wei and Y. Chen , doi =. Denoising Diffusion Probabilistic Models Are Optimally Adaptive to Unknown Low Dimensionality , url =. arXiv , arxivid =:2410.18784 , journal =

  100. [108]

    doi:10.52202/079017-3027 , title =

    Xunpeng Huang and Difan Zou and Hanze Dong and Yi Zhang and Yian Ma and Tong Zhang , booktitle =. doi:10.52202/079017-3027 , title =

    doi

  101. [109]

    A. Hyv. Estimation of Non-Normalized Statistical Models by Score Matching , volume =. Journal of Machine Learning Research (JMLR) , pages =

  102. [110]

    Some extensions of score matching , volume =

    Hyv. Some extensions of score matching , volume =. Computational statistics & data analysis , number =

  103. [111]

    Portilla, V

    J. Portilla, V. Strela, E. Simoncelli and M. J. Wainwright , booktitle =. Adaptive

  104. [112]

    Jin and S

    C. Jin and S. Balakrishnan and M. J. Wainwright and M. I. Jordan , booktitle =. Local Maxima in the Likelihood of

  105. [113]

    Kallianpur and C

    G. Kallianpur and C. Striebel , doi =. Estimation of Stochastic Systems: Arbitrary System Process with Additive White Noise Observation Errors , volume =. The Annals of Mathematical Statistics , number =

  106. [114]

    Kandasamy and D

    S. Kandasamy and D. Nagaraj , journal =. The

  107. [115]

    Khamaru and A

    K. Khamaru and A. Pananjady and F. Ruan and M. J. Wainwright and M. I. Jordan , journal =. Is Temporal Difference Learning Optimal?

  108. [116]

    Khamaru and Y

    K. Khamaru and Y. Deshpande and T. Lattimore and L. Mackey and M. J. Wainwright , journal =. Near-optimal inference in adaptive linear regression , topic =

  109. [117]

    Khamaru and M

    K. Khamaru and M. J. Wainwright , booktitle =. Convergence guarantees for a class of non-convex and non-smooth optimization problems , year =

  110. [118]

    Khamaru and M

    K. Khamaru and M. J. Wainwright , journal =. Convergence guarantees for a class of non-convex and non-smooth optimization problems , volume =

  111. [119]

    Kolmogorov and M

    V. Kolmogorov and M. J. Wainwright , booktitle =. On optimality properties of tree-reweighted message-passing , topic =

  112. [120]

    Lee and L

    P. Lee and L. Dolecek and Z. Zhang and V. Anantharam and B. Nikolic and M. J. Wainwright , booktitle =. Error Floors in

  113. [121]

    Lee and J

    H. Lee and J. Lu and Y. Tan , booktitle =. arXiv , arxivid =:2206.06227 , title =

    arXiv

  114. [122]

    Lee and J

    H. Lee and J. Lu and Y. Tan , booktitle =. arXiv , arxivid =:2209.12381 , pages =

    arXiv

  115. [123]

    C. J. Li and W. Mou and M. J. Wainwright and M. I. Jordan , booktitle =

  116. [124]

    Li and Y

    G. Li and Y. Wei and Y. Chen and Y. Chi , eprint =. arXiv preprint arXiv:2306.09251 , title =

    arXiv

  117. [125]

    and Wei, Y

    Li, G. and Wei, Y. and Chi, Y. and Chen, Y. , institution =. A Sharp Convergence Theory for the Probability Flow

  118. [126]

    doi:10.52202/079017-4012 , title =

    Gen Li and Yuling Yan , booktitle =. doi:10.52202/079017-4012 , title =

    doi

  119. [127]

    Lin and K

    L. Lin and K. Khamaru and M. J. Wainwright , journal =. Semi-parametric inference based on adaptively collected data , volume =

  120. [128]

    Liu, J. S. , isbn =. Monte Carlo Strategies in Scientific Computing , year =

  121. [129]

    Loh and M

    P. Loh and M. J. Wainwright , booktitle =. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity , year =

  122. [130]

    Loh and M

    P. Loh and M. J. Wainwright , journal =. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity , volume =

  123. [131]

    Loh and M

    P. Loh and M. J. Wainwright , booktitle =. No voodoo here! Learning discrete graphical models via inverse covariance estimation , topic =

  124. [132]

    Loh and M

    P. Loh and M. J. Wainwright , journal =. Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses , topic =

  125. [133]

    Loh and M

    P. Loh and M. J. Wainwright , journal =. Regularized

  126. [134]

    Loh and M

    P. Loh and M. J. Wainwright , journal =. Support recovery without incoherence: A case for nonconvex regularization , volume =

  127. [135]

    Ma and B

    C. Ma and B. Zhu and J. Jiao and M. J. Wainwright , journal =. Minimax Off-Policy Evaluation for Multi-Armed Bandits , topic =

  128. [136]

    Ma and R

    C. Ma and R. Pathak and M. J. Wainwright , journal =. Optimally tackling covariate shift in

  129. [137]

    D. J. C. MacKay , publisher =. Information Theory, Inference, and Learning Algorithms , year =

  130. [138]

    Malik and A

    D. Malik and A. Pananjady and K. Bhatia and K. Khamaru and P. L. Bartlett and M. J. Wainwright , booktitle =. Derivative-free methods for policy optimization:

  131. [139]

    Malik and A

    D. Malik and A. Pananjady and K. Bhatia and K. Khamaru and P. L. Bartlett and M. J. Wainwright , journal =. Derivative-free methods for policy optimization:

  132. [140]

    Maneva and E

    E. Maneva and E. Mossel and M. J. Wainwright , booktitle =. A New Look at Survey Propagation and its Generalizations , year =

  133. [141]

    Maneva and E

    E. Maneva and E. Mossel and M. J. Wainwright , journal =. A new look at survey propagation and its generalizations , topic =

  134. [142]

    Mania and A

    H. Mania and A. Ramdas and M. J. Wainwright and M. I. Jordan and B. Recht , journal =. On kernel methods for covariates that are rankings , volume =

  135. [143]

    Mao and A

    C. Mao and A. Pananjady and M. J. Wainwright , booktitle =. Breaking the 1/

  136. [144]

    Mao and A

    C. Mao and A. Pananjady and M. J. Wainwright , journal =. Towards Optimal Estimation of Bivariate Isotonic Marices with Unknown Permutations , volume =

  137. [145]

    Martinian and M

    E. Martinian and M. J. Wainwright , booktitle =. Analysis of

  138. [146]

    Martinian and M

    E. Martinian and M. J. Wainwright , booktitle =. Low density codes achieve the rate-distortion bound , volume =

  139. [147]

    Martinian and M

    E. Martinian and M. J. Wainwright , booktitle =. Low density codes can achieve the

  140. [148]

    Proceedings of the 22nd Conference on Learning Theory , eprint =

    Empirical. Proceedings of the 22nd Conference on Learning Theory , eprint =

  141. [149]

    arXiv preprint arXiv:2411.17667v3 , primaryclass =

    Rapid. arXiv preprint arXiv:2411.17667v3 , primaryclass =. doi:10.48550/arXiv.2411.17667 , eprint =

    doi arXiv

  142. [150]

    Estimating High Order Gradients of the Data Distribution by Denoising , year =

    Meng, Chenlin and Song, Yang and Li, Wenzhe and Ermon, Stefano , booktitle =. Estimating High Order Gradients of the Data Distribution by Denoising , year =

  143. [151]

    Metropolis and S

    N. Metropolis and S. Ulam , journal =. The Monte Carlo Method , volume =

  144. [152]

    Miyasawa , journal =

    K. Miyasawa , journal =. An empirical

  145. [153]

    arXiv , author =:2305.10690 , journal =

    Sampling, Diffusions, and Stochastic Localization , year =. arXiv , author =:2305.10690 , journal =

    arXiv

  146. [154]

    arXiv preprint arXiv:2406.19550 , primaryclass =

    Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition , year =. arXiv preprint arXiv:2406.19550 , primaryclass =. doi:10.48550/arXiv.2406.19550 , eprint =

    doi arXiv

  147. [155]

    Mou and N

    W. Mou and N. Ho and M. J. Wainwright and P. Bartlett and M. I. Jordan , institution =. Sampling for

  148. [156]

    Mou and C

    W. Mou and C. J. Li and M. J. Wainwright and P. L. Bartlett and M. I. Jordan , booktitle =. On Linear Stochastic Approximation:

  149. [157]

    Mou and Y

    W. Mou and Y. Ma and M. J. Wainwright and P. L. Bartlett and M. I. Jordan , journal =. High-Order

  150. [158]

    Mou and N

    W. Mou and N. Flammarion and M. J. Wainwright and P. L. Bartlett , journal =. Improved bounds for discretization of

  151. [159]

    Mou and N

    W. Mou and N. Flammarion and M. J. Wainwright and P. L. Bartlett , journal =. An efficient sampling algorithm for non-smooth composite potentials , topic =

  152. [160]

    Mou and M

    W. Mou and M. J. Wainwright and P. L. Bartlett , institution =. Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency , year =

  153. [161]

    Mou and A

    W. Mou and A. Pananjady and M. J. Wainwright and P. L. Bartlett , booktitle =. Optimal and instance-dependent guarantees for

  154. [162]

    Mou and K

    W. Mou and K. Khamaru and M. J. Wainwright and P. L. Bartlett and M. I. Jordan , institution =. Optimal variance-reduced stochastic approximation in

  155. [163]

    Mou and P

    W. Mou and P. Ding and P. L. Bartlett and M. J. Wainwright , institution =. Kernel-based off-policy estimation with overlap:

  156. [164]

    Mou and A

    W. Mou and A. Pananjady and M. J. Wainwright , journal =. Optimal oracle inequalities for solving projected fixed-point equations , topic =

  157. [165]

    Mou and N

    W. Mou and N. Ho and M. J. Wainwright and P. Bartlett and M. I. Jordan , journal =. A diffusion process perspective on posterior contraction rates for parameters , volume =

  158. [166]

    Mou and A

    W. Mou and A. Pananjady and M. J. Wainwright and P. L. Bartlett , journal =. Optimal and instance-dependent guarantees for

  159. [167]

    Negahban and P

    S. Negahban and P. Ravikumar and M. J. Wainwright and B. Yu , journal =. A unified framework for high-dimensional analysis of

  160. [168]

    Negahban and M

    S. Negahban and M. J. Wainwright , booktitle =. Benefits and perils of block regularization in high dimensions , year =

  161. [169]

    Negahban and M

    S. Negahban and M. J. Wainwright , booktitle =. Estimation of (near) low-rank matrices with noise and high-dimensional scaling , topic =

  162. [170]

    Negahban and M

    S. Negahban and M. J. Wainwright , journal =. Estimation of (near) low-rank matrices with noise and high-dimensional scaling , volume =

  163. [171]

    Negahban and M

    S. Negahban and M. J. Wainwright , journal =. Simultaneous support recovery in high-dimensional regression: Benefits and perils of _

  164. [172]

    Negahban and M

    S. Negahban and M. J. Wainwright , journal =. Restricted strong convexity and (weighted) matrix completion: Optimal bounds with noise , volume =

  165. [173]

    Nguyen and M

    X. Nguyen and M. J. Wainwright and M. I. Jordan , booktitle =. Divergence measures, surrogate loss functions and experimental design , year =

  166. [174]

    Nguyen and M

    X. Nguyen and M. J. Wainwright and M. I. Jordan , journal =. Nonparametric decentralized detection using kernel methods , volume =

  167. [175]

    Nguyen and M

    X. Nguyen and M. J. Wainwright and M. I. Jordan , booktitle =. On optimal quantization rules for some sequential decision problems , year =

  168. [176]

    Nguyen and M

    X. Nguyen and M. J. Wainwright and M. I. Jordan , journal =. On optimal quantization rules for some sequential decision problems , volume =

  169. [177]

    Nguyen and M

    X. Nguyen and M. J. Wainwright and M. I. Jordan , journal =. On surrogate losses and f -divergences , volume =

  170. [178]

    Noorshams and M

    N. Noorshams and M. J. Wainwright , journal =. Non-asymptotic analysis of an optimal algorithm for network-constrained averaging with noisy links , volume =

  171. [179]

    Noorshams and M

    N. Noorshams and M. J. Wainwright , journal =. Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees , url =

  172. [180]

    Noorshams and M

    N. Noorshams and M. J. Wainwright , journal =. Stochastic belief propagation: A low-complexity alternative to the sum-product algorithm , topic =

  173. [181]

    Obozinski and M

    G. Obozinski and M. J. Wainwright and M. I. Jordan , journal =. Union support recovery in high-dimensional multivariate regression , volume =

  174. [182]

    Omidiran and M

    D. Omidiran and M. J. Wainwright , journal =. High-dimensional Variable Selection with Sparse Random Projections:

  175. [183]

    and Villani, C

    Otto, F. and Villani, C. , journal =. Generalization of an Inequality by

  176. [184]

    Pananjady and M

    A. Pananjady and M. J. Wainwright and T. Courtade , booktitle =. Denoising linear models with permuted data , year =

  177. [185]

    Pananjady and M

    A. Pananjady and M. J. Wainwright and T. A. Courtade , journal =. Linear regression with shuffled data: Statistical and computational limits of permutation recovery , volume =

  178. [186]

    Pananjady and C

    A. Pananjady and C. Mao and V. Muthukumar and M. J. Wainwright and T. A. Courtade , journal =. Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons , volume =

  179. [187]

    Pananjady and M

    A. Pananjady and M. J. Wainwright , journal =. Instance-dependent _ -bounds for policy evaluation in tabular reinforcement learning , topic =

  180. [188]

    Pathak and C

    R. Pathak and C. Ma and M. J. Wainwright , booktitle =. A new similarity measure for covariate shift with applications to nonparametric regression , year =

  181. [189]

    Pathak and M

    R. Pathak and M. J. Wainwright and L. Xiao , journal =. Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints , topic =

  182. [190]

    Pathak and M

    R. Pathak and M. J. Wainwright , booktitle =. Fed

  183. [191]

    Pathak and M

    R. Pathak and M. J. Wainwright , institution =. Estimating linear functionals with elliptical constraints: Sharp results for random operators , topic =

  184. [192]

    , booktitle =

    Pidstrigach, J. , booktitle =. arXiv , arxivid =:2206.01018 , title =

    arXiv

  185. [193]

    Pilanci and M

    M. Pilanci and M. J. Wainwright and L. El Ghaoui , journal =. Sparse learning via

  186. [194]

    Pilanci and M

    M. Pilanci and M. J. Wainwright , journal =. Randomized sketches of convex programs with sharp guarantees , volume =

  187. [195]

    Pilanci and M

    M. Pilanci and M. J. Wainwright , journal =. Iterative

  188. [196]

    Pilanci and M

    M. Pilanci and M. J. Wainwright , journal =

  189. [197]

    Portilla and V

    J. Portilla and V. Strela and M. J. Wainwright and E. P. Simoncelli , journal =. Image denoising using scale mixtures of

  190. [198]

    Linear Convergence of Diffusion Models Under the Manifold Hypothesis , url =

    Peter Potaptchik and Iskander Azangulov and George Deligiannidis , booktitle =. Linear Convergence of Diffusion Models Under the Manifold Hypothesis , url =

  191. [199]

    Rabinovich and A

    M. Rabinovich and A. Ramdas and M. I. Jordan and M. J. Wainwright , journal =. Function-Specific Mixing Times and Concentration Away from Equilibrium , topic =

  192. [200]

    Rabinovich and M

    M. Rabinovich and M. I. Jordan and M. J. Wainwright , institution =. Lower bounds in multiple testing: A framework based on derandomized proxies , year =

  193. [201]

    Rabinovich and A

    M. Rabinovich and A. Ramdas and M. J. Wainwright and M. I. Jordan , journal =. Optimal Rates and Tradeoffs in Multiple Testing , volume =

  194. [202]

    Rajagopal and M

    R. Rajagopal and M. J. Wainwright and P. Varaiya , booktitle =. Universal quantile estimation with feedback in the communication-constrained setting , year =

  195. [203]

    Rajagopal and M

    R. Rajagopal and M. J. Wainwright , journal =. Network-based consensus with general noisy channels , volume =

  196. [204]

    Ramdas and F

    A. Ramdas and F. Yang and M. J. Wainwright and M. I. Jordan , booktitle =. Online control of false discovery rate with decaying memory , year =

  197. [205]

    Ramdas and J

    A. Ramdas and J. Chen and M. J. Wainwright and M. I. Jordan , booktitle =. QuTE: Decentralized multiple testing on sensor networks with false discovery rate control , year =

  198. [206]

    Ramdas and R

    A. Ramdas and R. F. Barber and M. J. Wainwright and M. I. Jordan , journal =. A Unified Treatment of Multiple Testing with Prior Knowledge using the p -filter , volume =

  199. [207]

    Ramdas and J

    A. Ramdas and J. Chen and M. J. Wainwright and M. I. Jordan , journal =

  200. [208]

    Raphan and E

    M. Raphan and E. P. Simoncelli , doi =. Least Squares Estimation Without Priors or Supervision , volume =. Neural Computation , number =

  201. [209]

    Raskutti and M

    G. Raskutti and M. J. Wainwright and B. Yu , journal =. Restricted eigenvalue conditions for correlated

  202. [210]

    Raskutti and M

    G. Raskutti and M. J. Wainwright and B. Yu , journal =. Minimax rates of estimation for high-dimensional linear regression over _q -balls , volume =

  203. [211]

    Raskutti and M

    G. Raskutti and M. J. Wainwright and B. Yu , journal =. Minimax-optimal rates for sparse additive models over kernel classes via convex programming , topic =

  204. [212]

    Raskutti and M

    G. Raskutti and M. J. Wainwright and B. Yu , journal =. Early stopping and non-parametric regression:

  205. [213]

    Ravikumar and M

    P. Ravikumar and M. J. Wainwright and J. D. Lafferty , journal =. High-dimensional

  206. [214]

    Ravikumar and A

    P. Ravikumar and A. Agarwal and M. J. Wainwright , journal =. Message-passing for graph-structured linear programs: Proximal projections, convergence and rounding schemes , topic =

  207. [215]

    Ravikumar and M

    P. Ravikumar and M. J. Wainwright and G. Raskutti and B. Yu , journal =. High-dimensional covariance estimation by minimizing _1 -penalized log-determinant divergence , volume =

  208. [216]

    H. E. Robbins , booktitle =. An Empirical

  209. [217]

    and Casella, George , title =

    Robert, Christian P. and Casella, George , title =. 2004 , doi =

    2004

  210. [218]

    G. O. Roberts and J. S. Rosenthal , journal =. Geometric Ergodicity and Hybrid

  211. [219]

    G. O. Roberts and R. L. Tweedie , journal =. Exponential Convergence of

  212. [220]

    Rombach and E

    R. Rombach and E. Blattmann and S. L. Dhariwal and A. M. D. M. L. and P. E. S. , journal =. High-Resolution Image Synthesis with Latent Diffusion Models , year =

  213. [221]

    T. G. Roosta and M. J. Wainwright and S. S. Sastry , journal =. Convergence analysis of reweighted sum-product algorithms , volume =

  214. [222]

    R. Y. Rubinstein and D. P. Kroese , edition =. Simulation and the Monte Carlo Method , year =

  215. [223]

    N. P. Santhanam and M. J. Wainwright , booktitle =. Information-theoretic limits of high-dimensional model selection , topic =

  216. [224]

    N. P. Santhanam and M. J. Wainwright , journal =. Information-theoretic limits of selecting binary graphical models in high dimensions , topic =

  217. [225]

    Schiebinger and M

    G. Schiebinger and M. J. Wainwright and B. Yu , journal =. The geometry of kernelized spectral clustering , volume =

  218. [226]

    N. B. Shah and S. Balakrishnan and J. Bradley and A. Parekh and K. Ramchandran and M. J. Wainwright , journal =. Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence , volume =

  219. [227]

    N. B. Shah and S. Balakrishnan and M. J. Wainwright , institution =. Low permutation-rank matrices:

  220. [228]

    N. B. Shah and S. Balakrishnan and A. Guntuboyina and M. J. Wainwright , journal =. Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues , volume =

  221. [229]

    N. B. Shah and S. Balakrishnan and M. J. Wainwright , journal =. Feeling the

  222. [230]

    N. B. Shah and S. Balakrishnan and M. J. Wainwright , journal =. Low permutation-rank matrices:

  223. [231]

    N. B. Shah and S. Balakrishnan and M. J. Wainwright , journal =. A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness , volume =

  224. [232]

    N. B. Shah and M. J. Wainwright , journal =. Simple, Robust and Optimal Ranking from Pairwise Comparisons , volume =

  225. [233]

    Shen and Y

    R. Shen and Y. T. Lee , journal =. The Randomized Midpoint Method for Log-Concave Sampling , year =

  226. [234]

    Shi and S

    F. Shi and S. Bates and M. J. Wainwright , institution =. Sharp Results for Hypothesis Testing with Risk-Sensitive Agents , topic =

  227. [235]

    Sohl‑Dickstein and E

    J. Sohl‑Dickstein and E. Weiss and N. Maheswaranathan and S. Ganguli , booktitle =. Deep Unsupervised Learning using Nonequilibrium Thermodynamics , url =

  228. [236]

    Song and S

    Y. Song and S. Ermon , booktitle =. Generative Modeling by Estimating Gradients of the Data Distribution , year =

  229. [237]

    Song and J

    Y. Song and J. Sohl-Dickstein and D. P. Kingma and A. Kumar and S. Ermon and B. Poole , booktitle =. Score-Based Generative Modeling through Stochastic Differential Equations , year =

  230. [238]

    Su and W

    F. Su and W. Mou and P. Ding and M. J. Wainwright , institution =. A decorrelation method for general regression adjustment in randomized experiments , year =

  231. [239]

    Su and W

    F. Su and W. Mou and P. Ding and M. J. Wainwright , institution =. When is the estimated propensity score better? High-dimensional analysis and bias correction , year =

  232. [240]

    Sudderth and M

    E. Sudderth and M. J. Wainwright and A. S. Willsky , journal =. Embedded trees: Estimation of

  233. [241]

    E. B. Sudderth and M. J. Wainwright and A. S. Willsky , booktitle =. Loop series and

  234. [242]

    , journal =

    Talagrand, M. , journal =. Transportation Cost for

  235. [243]

    S. S. Vempala and A. Wibisono , institution =. Rapid Convergence of the Unadjusted

  236. [244]

    Vincent , doi =

    P. Vincent , doi =. Connection between Score Matching and Denoising Autoencoders , volume =. Neural Networks , number =

  237. [245]

    M. J. Wainwright , month =. Stochastic processes on graphs with cycles: geometric and variational approaches , year =

  238. [246]

    M. J. Wainwright , journal =. Estimating the ``wrong'' graphical model:

  239. [247]

    M. J. Wainwright , journal =. Sparse graph codes for side information and binning , topic =

  240. [248]

    M. J. Wainwright , journal =. Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting , topic =

  241. [249]

    M. J. Wainwright , issue =. Sharp thresholds for high-dimensional and noisy sparsity recovery using _1 -constrained quadratic programming (. IEEE Trans. Info. Theory , month =

  242. [250]

    M. J. Wainwright , journal =. Discussion: Latent graphical model selection by convex optimization , topic =

  243. [251]

    M. J. Wainwright , booktitle =. Constrained forms of statistical minimax:

  244. [252]

    M. J. Wainwright , booktitle =. Structured regularizers for high-dimensional problems:

  245. [253]

    M. J. Wainwright , booktitle =. Graphical models and message-passing algorithms: some introductory lectures , topic =

  246. [254]

    , publisher =

    Wainwright, Martin J. , publisher =. High-Dimensional Statistics: A Non-Asymptotic Viewpoint , year =

  247. [255]

    M. J. Wainwright , institution =. Stochastic approximation with cone-contractive operators: Sharp _ -bounds for

  248. [256]

    M. J. Wainwright , institution =. Variance-reduced

  249. [257]

    M. J. Wainwright , institution =. Wild refitting for black box prediction , year =

  250. [258]

    M. J. Wainwright , journal =. Visual adaptation as optimal information transmission , volume =

  251. [259]

    M. J. Wainwright and E. P. Simoncelli and A. S. Willsky , journal =. Random cascades on wavelet trees and their use in modeling and analyzing natural images , volume =

  252. [260]

    M. J. Wainwright and E. B. Sudderth and A. S. Willsky , booktitle =. Tree-based modeling and estimation of

  253. [261]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , booktitle =. A new class of upper bounds on the log partition function , topic =

  254. [262]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , booktitle =. Exact

  255. [263]

    M. J. Wainwright and O. Schwartz and E. P. Simoncelli , booktitle =. Natural image statistics and divisive normalization: Modeling nonlinearities and adaptation in cortical neurons , topic =

  256. [264]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , booktitle =. Tree-based reparameterization for approximate inference on loopy graphs , topic =

  257. [265]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , journal =. Exact

  258. [266]

    M. J. Wainwright and P. Ravikumar and J. D. Lafferty , booktitle =. High-dimensional graph selection using _1 -regularized logistic regression , topic =

  259. [267]

    M. J. Wainwright and E. Maneva and E. Martinian , journal =. Lossy Source Compression using Low-Density Generator Matrix Codes:

  260. [268]

    M. J. Wainwright and M. I. Jordan , booktitle =. Variational inference in graphical models: The view from the marginal polytope , topic =

  261. [269]

    M. J. Wainwright and M. I. Jordan , institution =. Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations , topic =

  262. [270]

    M. J. Wainwright and M. I. Jordan , booktitle =. A variational principle for graphical models , topic =

  263. [271]

    M. J. Wainwright and M. I. Jordan , journal =. Log-determinant relaxation for approximate inference in discrete

  264. [272]

    M. J. Wainwright and M. I. Jordan , journal =. Graphical models, exponential families and variational inference , volume =

  265. [273]

    M. J. Wainwright and E. Maneva , booktitle =. Lossy source coding by message-passing and decimation over generalized codewords of

  266. [274]

    M. J. Wainwright and E. Martinian , journal =. Low-density codes that are optimal for binning and coding with side information , volume =

  267. [275]

    M. J. Wainwright and E. P. Simoncelli , booktitle =. Explaining adaptation in

  268. [276]

    M. J. Wainwright and E. P. Simoncelli , booktitle =. Scale mixtures of

  269. [277]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , journal =. Tree-based reparameterization framework for analysis of sum-product and related algorithms , topic =

  270. [278]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , journal =. Tree consistency and bounds on the max-product algorithm and its generalizations , topic =

  271. [279]

    M. J. Wainwright and T. S. Jaakkola and A. S. Willsky , journal =. A new class of upper bounds on the log partition function , topic =

  272. [280]

    Wang and M

    W. Wang and M. J. Wainwright and K. Ramchandran , booktitle =. Information-theoretic bounds on model selection for

  273. [281]

    Wang and M

    W. Wang and M. J. Wainwright and K. Ramchandran , journal =. Information-Theoretic Limits on Sparse Signal Recovery: Dense versus Sparse Measurement Matrices , volume =

  274. [282]

    Wan and S

    J. Wan and S. R. Sinclair and D. Shah and M. J. Wainwright , institution =. Exploiting exogenous structure for sample-efficient reinforcement learning , year =

  275. [283]

    Wei and F

    Y. Wei and F. Yang and M. J. Wainwright , journal =. Early stopping for kernel boosting algorithms:

  276. [284]

    Wei and M

    Y. Wei and M. J. Wainwright and A. Guntuboyina , journal =. The geometry of testing over convex cones:

  277. [285]

    Wei and B

    Y. Wei and B. Fang and M. J. Wainwright , journal =. From

  278. [286]

    Wei and M

    Y. Wei and M. J. Wainwright , booktitle =. Sharp minimax rates for testing monotone distributions , year =

  279. [287]

    Wei and M

    Y. Wei and M. J. Wainwright , journal =. The local geometry of testing in ellipses: Tight control via localized

  280. [288]

    Xia and K

    E. Xia and K. Khamaru and M. J. Wainwright and M. I. Jordan , journal =. Instance-dependent confidence and early stopping in reinforcement learning , year =

  281. [289]

    Xia and Y

    E. Xia and Y. Yan and M. J. Wainwright , institution =. Inference under staggered adoption:

  282. [290]

    Xia and K

    E. Xia and K. Khamaru and M. J. Wainwright and M. I. Jordan , journal =. Instance-optimality in optimal value estimation: Adaptivity via variance-reduced

  283. [291]

    Xia and W

    E. Xia and W. Newey and M. J. Wainwright , institution =. Instrumental variables:

  284. [292]

    Xia and M

    E. Xia and M. J. Wainwright , booktitle =

  285. [293]

    Xia and M

    E. Xia and M. J. Wainwright , institution =. Prediction Aided by Surrogate Training , url =

  286. [294]

    Yang and M

    Y. Yang and M. J. Wainwright and M. I. Jordan , fjournal =. On the computational complexity of high-dimensional. Annals of Statistics , number =

  287. [295]

    Yang and A

    F. Yang and A. Ramdas and K. Jamieson and M. J. Wainwright , booktitle =. A framework for multi-armed bandit testing with online

  288. [296]

    Yang and Y

    F. Yang and Y. Wei and M. J. Wainwright , booktitle =. Early stopping for kernel boosting algorithms:

  289. [297]

    Yang and M

    Y. Yang and M. Pilanci and M. J. Wainwright , journal =. Randomized sketches for kernels:

  290. [298]

    Yang and S

    F. Yang and S. Balakrishnan and M. J. Wainwright , journal =. Statistical and Computational Guarantees for the

  291. [299]

    Yang and Z

    L. Yang and Z. Zhang and Y. Song and et al. , journal =. Diffusion Models: A Comprehensive Survey of Methods and Applications , year =

  292. [300]

    Yan and M

    Y. Yan and M. J. Wainwright , institution =. Entrywise Inference for Causal Panel Data:

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.