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REVIEW 3 major objections 4 minor 144 references

Weak-to-Strong Learning in Decision Making

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Weak supervision on abundant unlabeled contexts beats label-only training for downstream decisions, provided the weak and strong feature representations share little overlap.

desk verdict The W2S upper bound is a real contribution, but the outperformance certificate leans on a calibration assumption that fails for the paper's own experiments — the framework is sound, the sufficient conditions are narrower than claimed. read the letter →

arxiv 2607.18467 v1 pith:E3ONJNHA submitted 2026-07-20 cs.LG

Jingwei Ji , Renyuan Xu This is my paper
classification cs.LG
keywords weak-to-stronglearningcontextualstochasticoptimizationdecision-awaretrainingcorrelationdimensionlabelscarcityunlabeleddataplug-inpolicyexcessdecisionrisk
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 asks whether weak supervision can improve decision making, not just prediction. The setting is contextual stochastic optimization: choose an action for each context to minimize expected cost, where outcome labels are scarce, unlabeled contexts are abundant, and two pre-trained feature representations (a weak and a strong one) are available. The paper proposes training a weak model on the labeled data, using it to produce soft outcome distributions on the unlabeled contexts, and training the strong model on those pseudo-distributions. Its main theorem gives a non-asymptotic upper bound on the induced policy's excess decision risk; comparing that bound with a lower bound for directly training the strong model on labels alone yields an explicit certificate (Corollary 1) for when the weak-to-strong policy is better. The governing quantity is the correlation dimension between the weak and strong features: the smaller the overlap, the more the abundant unlabeled data wash out teacher errors instead of transmitting them to the student.

What carries the argument

The correlation dimension d_{s∧w} — the squared Frobenius norm of the whitened cross-covariance between the weak and strong feature maps, a number between 0 and min{d_s, d_w} measuring how many aligned directions the two representations share — is the quantity that decides whether W2S beats strong-only training. The proof decomposes the W2S excess decision risk into four terms: imitation error (how well the strong class reproduces the weak teacher's logits), an N^{-1/2} statistical error from the unlabeled sample, a teacher-error term proportional to E‖Θ̂_w−Θ*_w‖²_F times (d_{s∧w}+d_s(d_w−d_{s∧w})/N), and approximation errors ρ_s+ρ_w. Only the overlap directions carry persistent teacher erro

What would settle it

In the synthetic newsvendor with controlled overlap r, the theory predicts OPR strictly decreases as r increases (at fixed n,N) and OPR>1 whenever the certified margin M_n>0 holds with N above the threshold in (57); measuring these two relations across trials and finding a violation would falsify the claim.

Watch

Extended reading notes

Core claim

W2S training — fit a weak model on scarce labels, generate soft outcome distributions on abundant unlabeled contexts, train a strong model on them — can achieve lower expected excess decision risk than training the strong model on labels alone. Corollary 1 certifies this when imitation error, approximation errors, and overlap cost C_t d_{s∧w}/n are small enough to leave a positive margin M_n and N is large enough. The mechanism: weak-teacher error propagates mainly along the d_{s∧w} shared feature directions, while the non-overlap part decays as d_s(d_w−d_{s∧w})/N, so abundant unlabeled contexts dilute, rather than transmit, teacher errors.

Load-bearing premise

The comparison relies on the assumption that for every context, the distance between the decisions made by the estimated and the true strong model is at least a fixed positive multiple (γ) of the distance between their predicted outcome distributions; the paper assumes this inequality rather than deriving or testing it, and the strong-only lower bound — the anchor for the W2S-vs-strong comparison — collapses if it fails.

Editorial extensions

If this is right

  • In label-scarce operational settings (inventory, pricing, moderation routing), a small weak model can supervise a large strong model across unlabeled contexts and deliver a decision policy with no higher expected cost than fine-tuning the strong model on labels alone.
  • The certified regime is self-limiting: gains shrink as labeled samples grow (the margin M_n shrinks) and as the weak–strong overlap dimension d_{s∧w} grows; practitioners can therefore predict when W2S stops paying off.
  • Given a positive margin, the unlabeled budget that certifies W2S scales roughly as max{d_s, n²} up to constants — an explicit data requirement that can be forecast before training.
  • Exact realizability rules out zero overlap (Appendix B.11): d_{s∧w} is bounded below by a ratio of parameter norms, so unlabeled data alone cannot make W2S arbitrarily accurate.
  • The framework is agnostic to how the weak teacher is trained: any procedure with a mean-squared parameter error bound (MLE is one example) can serve as the teacher.

Reading between the lines

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

  • The strong-only lower bound in Corollary 1 leans on a calibration inequality (47) that the paper neither derives nor tests; if that inequality fails in an application, the certificate loses its anchor while the W2S upper bound itself may still hold — practitioners should check it before relying on the comparison.
  • Because d_{s∧w} is computable from feature covariances before any training, it could serve as a model-selection diagnostic for choosing which weak/strong pair to use; the paper treats it as an analysis parameter rather than a selection tool.
  • The dilution mechanism suggests a transferable intuition for sequential decisions: if teacher errors behave like noise along non-overlapping state-feature directions, reinforcement learning may inherit teacher bias only along overlap directions — a testable extension the paper leaves open.
  • Both experiments train on smoothed surrogate decision rules but evaluate on the exact rule; an enterprising reader could check whether the OPR>1 regime survives exact non-smooth optimization, to separate the theory's effect from the surrogate's.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes a weak-to-strong (W2S) training framework for contextual stochastic optimization. A weak model is trained on scarce labeled data and used to generate soft outcome distributions on many unlabeled contexts; a strong model is then trained on these pseudo-distributions and evaluated by the downstream decision risk of the induced plug-in policy. The main technical contribution is Theorem 1, a non-asymptotic upper bound on the W2S excess risk decomposed into imitation error, unlabeled-sample statistical error, weak-teacher estimation error, and approximation error, with the teacher error governed by a newly defined correlation dimension d_{s∧w} between weak and strong feature representations. Theorem 2 gives a lower bound for the strong-only benchmark that trains directly on labeled data. Corollary 1 combines the two bounds into sufficient conditions, stated up to universal constants, under which W2S is certified to outperform strong-only training. The paper also reports a synthetic newsvendor experiment and a comment-moderation experiment on real text data, finding qualitative agreement with the predicted regimes: W2S helps most when labeled data are scarce, unlabeled data are abundant, and feature overlap is small.

Significance. If the results hold, this would be a useful first theoretical treatment of weak-to-strong transfer in decision-making rather than prediction, with an appealing interpretable mechanism: the overlap dimension d_{s∧w} determines how much teacher error persists. The paper has notable strengths: the risk decomposition is careful, supporting lemmas are provided in the appendix, and the no-free-lunch bound (Lemma 12) shows the authors explicitly guard against the misleading conclusion that d_{s∧w}=0 makes teacher error vanish entirely. The synthetic experiments control the overlap dimension directly, and the comment-moderation study tests the qualitative predictions in a realistic setting. However, the central W2S-versus-strong certificate depends critically on an unverified and, for natural cost functions, false calibration assumption in Theorem 2, and the comparison is made only up to unspecified universal constants. Thus the significance is currently conditional: the mechanism is plausible and the proof structure is credible, but the benchmark lower bound needs substantial repair or restriction before the paper's main claim is established.

major comments (3)
  1. [§4.1, Theorem 2, Eq. (47); used in Corollary 1, Eq. (56)] The calibration inequality (47) is the load-bearing lower anchor of the entire comparison, but it is not implied by Assumptions 1–2 and is false for a standard class of costs. For quadratic cost c(w,ξ)=(w−ξ)², the plug-in decision is the conditional mean w(P)=E_P[ξ]; for K≥3 there are distinct distributions on the same support with equal means, so the left side of (47) is zero while the right side is positive, forcing γ=0. The same structural failure occurs for any finite action set, including the comment-moderation experiment in §5.2, where w(P) is piecewise constant and hence has a nontrivial nullspace. Without a positive γ, Theorem 2's lower bound (50) degenerates to only the approximation term ρ_s and the 1/n statistical term that anchors Corollary 1 disappears. The paper needs either a lower-bound proof that does not require injectivity of the decision map P↦w(P), or an explicit res
  2. [§4.3, Eq. (53)–(58)] The comparison in Corollary 1 is made 'up to universal constants,' but the margin condition (56) and the subsequent statements treat the comparison as an exact inequality. Since the constants in the upper and lower bounds are not tracked, a positive residual margin M_n>0 computed from the displayed expressions does not logically imply (58) unless the hidden constants happen to satisfy the right inequality. As written, the result is a rate-level comparison, not a certificate of outperformance for fixed problem instances. The paper should either track all constants in the sufficient condition or state the conclusion as an asymptotic/rate result rather than a finite-sample guarantee.
  3. [§5.1–§5.2] The experiments are presented as validating the theoretical conditions of Corollary 1, but neither experiment satisfies the assumptions needed for the main certificate. The synthetic newsvendor uses a non-smooth, non-strongly-convex cost, which violates Assumption 2, and the comment-moderation experiment explicitly states that 'this experiment does not enforce any of the assumptions used in the theory.' In particular, the calibration inequality (47) is never checked in either experiment. The empirical results may still be useful as heuristic evidence, but they cannot be cited as confirmation of the sufficiency conditions in Corollary 1. The paper should either design an experiment that satisfies all assumptions of the theorem or clearly restrict the claim to qualitative consistency.
minor comments (4)
  1. [Corollary 1, Eq. (57) and following paragraph] The text says (57) 'scales qualitatively as N≳max{ds, n²}.' This is not generally implied by the displayed formula. If the approximation errors ρ_s, E_imit, and E_approx are strictly positive so that M_n has a positive limit as n→∞, the required N from the first term is O(1), not O(n²); the n² scaling only emerges in a special regime where the benchmark 1/n term is the only positive contribution to M_n. Please clarify the regime in which the n² statement is intended.
  2. [Definition 1, Eq. (2)] The quantity d_{s∧w} is called a 'dimension' but it is a squared Frobenius norm and is generally not an integer. This is potentially confusing; consider renaming it 'overlap' or 'effective correlation' or explicitly noting that it is a continuous measure, not a rank.
  3. [§2.2, Eq. (11)] There is a typo in 'o utperforming ratio' in the sentence defining OPR. Otherwise the OPR definition is clear.
  4. [Theorem 2, item 1] Inequality (47) uses norms of two objects of different types: a decision vector and a probability vector. Please specify explicitly that the same Euclidean norm is meant in both cases, or use separate notation if different norms are intended.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the W2S upper bound, strong-only lower bound, and comparison are derived from stated assumptions rather than fitted to the conclusion.

full rationale

The paper's derivation is self-contained in the relevant sense. Definition 1 defines the correlation dimension d_{s∧w} from population feature covariances, not from the excess-risk quantities being predicted, and this quantity enters Theorem 1 through explicit whitened-matrix calculations (Lemma 5) rather than being tuned to force the result. The W2S upper bound (Theorem 1) is obtained by decomposing the risk into imitation error, unlabeled-sample statistical error, teacher-error propagation, and approximation error; each term is bounded using the stated Assumptions 1–4. The no-free-lunch Lemma 12 explicitly prevents d_{s∧w} from being made arbitrarily small under exact realizability, so the overlap dimension is not an unconstrained knob. The strong-only benchmark lower bound (Theorem 2) is conditional on the calibration inequality (47); this is a substantive assumption whose failure would invalidate the benchmark comparison, but that is a correctness/robustness concern, not circularity, because the assumption does not presuppose W2S outperformance. Corollary 1 merely combines the upper and lower bounds into a sufficient condition; the condition is an inequality of explicit terms, not a restatement of the conclusion. The numerical experiments vary labeled/unlabeled sizes and overlap with prescribed feature constructions and then measure the resulting OPR; they do not fit the theoretical parameters to the observed OPR. The only self-citations (e.g., Javanmard et al. 2024) appear as background examples and are not load-bearing. The paper therefore does not reduce its central claim to its inputs by construction.

Assumptions & free parameters 3 free parameters · 7 assumptions · 1 invented entities

The theoretical derivation is conditional on a standard set of modeling assumptions (realizability, strong convexity/smoothness, subgaussianity, identifiability of the weak model) plus an ad hoc calibration inequality in Theorem 2. No free parameters are fitted in the theory; the listed experimental constants are hand-selected tuning choices and do not affect the central claim. The only newly introduced quantity is the correlation dimension, which is a defined statistic, not a fitted knob.

free parameters (3)
  • Surrogate sharpness β = 35 (synthetic experiment)
    Hand-chosen parameter controlling the sharpness of the smoothed newsvendor surrogate in (60); not part of the theoretical claims.
  • Smoothing parameter τ = 0.5
    Used in the softplus approximation of the newsvendor cost; hand-chosen, affects experiment only.
  • W2S regularization λ_w2s = 5e-4
    Hand-chosen regularization in the experimental W2S objective (61); not used in the theoretical derivation and not justified by theory.
assumptions (7)
  • domain assumption Realizability: ground-truth conditional P*(·|x) is softmax of a linear logit map η*(x) with η*1=0 and E||η*(x)||^4 < ∞.
    Assumption 1 in Section 3.1; defines the parametric family and is needed for all theorem proofs.
  • domain assumption Cost function c(a,z) is μ-strongly convex and L-smooth in a, with gradients bounded by G on W=conv(plug-in actions).
    Assumption 2 in Section 3.1; used in Lemmas 1, 2 and all subsequent bounds. The authors note it can be relaxed to convexity with technical effort.
  • domain assumption Decision set is A=R^d and outcome space is finite with K categories.
    Problem setup in Section 2.1; finite support is used throughout, whole-space is stated as relaxable.
  • ad hoc to paper Least-squares estimator bΘls is bounded in Frobenius norm by B almost surely.
    Assumption 3 in Section 3.1; explicitly a technical convenience to place bΘls in H_B, not central to the W2S effect.
  • domain assumption Features φ=[φ_s;φ_w] are subgaussian with MGF bound E exp(u^T φ) ≤ exp(½ c² u^T Σ_φ u).
    Assumption 4 in Section 3.1; needed for covariance concentration in Lemma 5 and moment bounds in Lemma 4.
  • domain assumption Weak model is identifiable: ρ_w=0, Θ*_w unique and interior; weak features bounded by B_φ.
    Assumptions 5–6 in Section 4.2; required for the MLE weak-teacher bound in Theorem 3. This is strong: the weak teacher must be correctly specified.
  • ad hoc to paper Calibration inequality: ||~w(P^s_{bΘ_s}(x)) − ~w(P^*(x))|| ≥ γ ||P^s_{bΘ_s}(x) − P^*(x)||; bounded logits; Hessian of empirical risk bounded by L_H.
    Assumptions in Theorem 2 (Eq. 47–49); imposed to make the strong-only lower bound go through. Not derived from previous assumptions and not verified experimentally.
invented entities (1)
  • correlation dimension d_{s∧w} independent evidence
    purpose: Quantifies the overlap/alignment between weak and strong feature representations; controls how much teacher error propagates to W2S decision risk.
    Defined in Definition 1 from feature covariances Σ_s, Σ_w; can be estimated from unlabeled data. Not a physical entity; the paper also proves a no-free-lunch lower bound (Lemma 12) under exact realizability, so it is not an unconstrained free parameter.

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Pith. "Pith review of Weak-to-Strong Learning in Decision Making." pith.science (2026). https://pith.science/paper/E3ONJNHA

@misc{pith2026260718467,
  author       = {Pith},
  title        = {Pith review of: Weak-to-Strong Learning in Decision Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E3ONJNHA}},
  note         = {Machine review of arXiv:2607.18467}
}
read the original abstract

Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distributions on unlabeled contexts. These distributions provide soft supervision for training a strong model. We establish a non-asymptotic upper bound on the excess decision risk of W2S and a complementary lower bound for a strong-only benchmark. Their comparison yields explicit sufficient conditions under which W2S improves downstream decision performance. The key quantity is the correlation dimension between the weak and strong feature representations: when it is small, abundant unlabeled data reduce the effect of teacher errors along non-overlapping directions. A synthetic newsvendor experiment and a comment moderation experiment based on real-world data provide empirical evidence consistent with the theory.

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Reference graph

Works this paper leans on

144 extracted references · 5 canonical work pages

  1. [1]

    arXiv preprint arXiv:1804.08838 , year=

    Measuring the intrinsic dimension of objective landscapes , author=. arXiv preprint arXiv:1804.08838 , year=

    arXiv

  2. [2]

    Intrinsic dimensionality explains the effectiveness of language model fine-tuning , author=. Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers) , pages=

  3. [3]

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Cross-Task Generalization via Natural Language Crowdsourcing Instructions , author=. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=. 2022 , publisher=

    2022

  4. [4]

    International Conference on Learning Representations , year=

    Multitask Prompted Training Enables Zero-Shot Task Generalization , author=. International Conference on Learning Representations , year=

  5. [5]

    Journal of Machine Learning Research , volume=

    Scaling Instruction-Finetuned Language Models , author=. Journal of Machine Learning Research , volume=

  6. [6]

    SIAM Journal on Mathematics of Data Science , volume=

    Why are big data matrices approximately low rank? , author=. SIAM Journal on Mathematics of Data Science , volume=. 2019 , publisher=

    2019

  7. [7]

    International conference on machine learning , pages=

    More than a toy: Random matrix models predict how real-world neural representations generalize , author=. International conference on machine learning , pages=. 2022 , organization=

    2022

  8. [8]

    Advances in Neural Information Processing Systems , volume=

    Sketchy moment matching: Toward fast and provable data selection for finetuning , author=. Advances in Neural Information Processing Systems , volume=

Show all 144 references
  1. [9]

    Advances in neural information processing systems , volume=

    Neural tangent kernel: Convergence and generalization in neural networks , author=. Advances in neural information processing systems , volume=

  2. [10]

    Conference on Learning Theory , pages=

    Kernel and rich regimes in overparametrized models , author=. Conference on Learning Theory , pages=. 2020 , organization=

    2020

  3. [13]

    Advances in Neural Information Processing Systems , volume=

    Training compute-optimal large language models , author=. Advances in Neural Information Processing Systems , volume=. 2022 , doi=

    2022

  4. [14]

    Advances in neural information processing systems , volume=

    Language models are few-shot learners , author=. Advances in neural information processing systems , volume=

  5. [15]

    Proceedings of the 41st International Conference on Machine Learning , pages=

    Weak-to-strong generalization: eliciting strong capabilities with weak supervision , author=. Proceedings of the 41st International Conference on Machine Learning , pages=

  6. [16]

    Proceedings of the 42nd International Conference on Machine Learning , series=

    Discrepancies are virtue: Weak-to-strong generalization through lens of intrinsic dimension , author=. Proceedings of the 42nd International Conference on Machine Learning , series=. 2025 , publisher=

    2025

  7. [17]

    The Fourteenth International Conference on Learning Representations , year=

    Does Weak-to-strong Generalization Happen under Spurious Correlations? , author=. The Fourteenth International Conference on Learning Representations , year=

  8. [18]

    Advances in neural information processing systems , volume=

    Theoretical analysis of weak-to-strong generalization , author=. Advances in neural information processing systems , volume=

  9. [19]

    International Conference on Machine Learning , pages=

    A theory of label propagation for subpopulation shift , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  10. [20]

    Patterns , volume=

    Unleashing the potential of prompt engineering for large language models , author=. Patterns , volume=. 2025 , publisher=

    2025

  11. [21]

    Advances in neural information processing systems , volume=

    Quantifying the gain in weak-to-strong generalization , author=. Advances in neural information processing systems , volume=

  12. [22]

    arXiv preprint arXiv:2402.03749 , year=

    Vision superalignment: Weak-to-strong generalization for vision foundation models , author=. arXiv preprint arXiv:2402.03749 , year=

    arXiv

  13. [23]

    arXiv preprint arXiv:2402.15505 , year=

    Co-supervised learning: Improving weak-to-strong generalization with hierarchical mixture of experts , author=. arXiv preprint arXiv:2402.15505 , year=

    arXiv

  14. [25]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Debate helps weak-to-strong generalization , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  15. [26]

    ACM Transactions on Intelligent Systems and Technology , volume=

    Survey on knowledge distillation for large language models: methods, evaluation, and application , author=. ACM Transactions on Intelligent Systems and Technology , volume=. 2025 , publisher=

    2025

  16. [27]

    Computer-Aided Civil and Infrastructure Engineering , volume=

    Weakly-supervised structural component segmentation via scribble annotations , author=. Computer-Aided Civil and Infrastructure Engineering , volume=. 2025 , publisher=

    2025

  17. [28]

    Proceedings of the Eleventh Annual Conference on Computational Learning Theory , pages=

    Combining labeled and unlabeled data with co-training , author=. Proceedings of the Eleventh Annual Conference on Computational Learning Theory , pages=

  18. [29]

    Proceedings of the 20th International Conference on Machine Learning , pages=

    Semi-supervised learning using Gaussian fields and harmonic functions , author=. Proceedings of the 20th International Conference on Machine Learning , pages=

  19. [30]

    Advances in Neural Information Processing Systems , volume=

    Semi-supervised learning by entropy minimization , author=. Advances in Neural Information Processing Systems , volume=

  20. [31]

    ICML Workshop on Challenges in Representation Learning , year=

    Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks , author=. ICML Workshop on Challenges in Representation Learning , year=

  21. [32]

    Advances in Neural Information Processing Systems , volume=

    FixMatch: Simplifying semi-supervised learning with consistency and confidence , author=. Advances in Neural Information Processing Systems , volume=

  22. [33]

    , author=

    Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. , author=. COLT , pages=

  23. [34]

    Advances in neural information processing systems , volume=

    Unlabeled data improves adversarial robustness , author=. Advances in neural information processing systems , volume=

  24. [35]

    International Conference on Learning Representations , year=

    Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data , author=. International Conference on Learning Representations , year=

  25. [36]

    IEEE transactions on knowledge and data engineering , volume=

    A survey on deep semi-supervised learning , author=. IEEE transactions on knowledge and data engineering , volume=. 2022 , publisher=

    2022

  26. [38]

    Operations Research , volume=

    Data-driven optimization: A reproducing kernel hilbert space approach , author=. Operations Research , volume=. 2022 , publisher=

    2022

  27. [39]

    International conference on neural information processing , pages=

    Assessing the performance of deep learning algorithms for newsvendor problem , author=. International conference on neural information processing , pages=. 2017 , organization=

    2017

  28. [40]

    European Journal of Operational Research , volume=

    A data-driven newsvendor problem: From data to decision , author=. European Journal of Operational Research , volume=. 2019 , publisher=

    2019

  29. [41]

    predict, then optimize

    Smart “predict, then optimize” , author=. Management Science , volume=. 2022 , publisher=

    2022

  30. [42]

    Advances in neural information processing systems , volume=

    Generalization bounds in the predict-then-optimize framework , author=. Advances in neural information processing systems , volume=

  31. [43]

    International conference on machine learning , pages=

    Decision trees for decision-making under the predict-then-optimize framework , author=. International conference on machine learning , pages=. 2020 , organization=

    2020

  32. [45]

    Tutorials in operations research: emerging and impactful topics in operations , pages=

    Integrating prediction/estimation and optimization with applications in operations management , author=. Tutorials in operations research: emerging and impactful topics in operations , pages=. 2022 , publisher=

    2022

  33. [46]

    Operations Research , volume=

    Integrated conditional estimation-optimization , author=. Operations Research , volume=. 2025 , doi=

    2025

  34. [47]

    Management Science , volume=

    From predictive to prescriptive analytics , author=. Management Science , volume=. 2020 , publisher=

    2020

  35. [48]

    Available at SSRN 3623006 , year=

    Decision-Driven Regularization: A Blended Model for Learning and Optimization , author=. Available at SSRN 3623006 , year=

  36. [49]

    Advances in neural information processing systems , volume=

    Task-based end-to-end model learning in stochastic optimization , author=. Advances in neural information processing systems , volume=

  37. [50]

    Operations Research , volume=

    The big data newsvendor: Practical insights from machine learning , author=. Operations Research , volume=. 2019 , publisher=

    2019

  38. [51]

    Computational Management Science , volume=

    Predictive stochastic programming , author=. Computational Management Science , volume=. 2022 , publisher=

    2022

  39. [52]

    Manufacturing & Service Operations Management , volume=

    On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya--Watson Approach , author=. Manufacturing & Service Operations Management , volume=. 2026 , doi=

    2026

  40. [53]

    Available at SSRN 5606310 , year=

    Feature-Rich, Data-Private: A Sparse Learning Framework for the High-Dimensional Newsvendor , author=. Available at SSRN 5606310 , year=

  41. [54]

    Manufacturing & Service Operations Management , volume=

    Analytics for an Online Retailer: Demand Forecasting and Price Optimization , author=. Manufacturing & Service Operations Management , volume=. 2016 , doi=

    2016

  42. [55]

    Mathematics of Operations Research , volume=

    Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand , author=. Mathematics of Operations Research , volume=. 2021 , doi=

    2021

  43. [56]

    Management Science , volume=

    Utility Fairness in Contextual Dynamic Pricing with Demand Learning , author=. Management Science , volume=. 2026 , doi=

    2026

  44. [57]

    Computational Management Science , volume=

    Likelihood Robust Optimization for Data-Driven Problems , author=. Computational Management Science , volume=. 2016 , doi=

    2016

  45. [58]

    Management Science , volume=

    Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information , author=. Management Science , volume=. 2022 , doi=

    2022

  46. [59]

    Innovative Technology at the Interface of Finance and Operations , pages=

    Applied Machine Learning in Operations Management , author=. Innovative Technology at the Interface of Finance and Operations , pages=. 2022 , doi=

    2022

  47. [60]

    Operations Research , volume=

    Offline Multi-Action Policy Learning: Generalization and Optimization , author=. Operations Research , volume=. 2023 , doi=

    2023

  48. [61]

    Operations Research , volume=

    Technical Note--Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand , author=. Operations Research , volume=. 2016 , doi=

    2016

  49. [62]

    Management Science , volume=

    Policy Learning with Adaptively Collected Data , author=. Management Science , volume=. 2023 , doi=

    2023

  50. [63]

    Management Science , year=

    Collaborative Learning and Decision Making on Pricing and Recommendation: A Simple Framework for Planning , author=. Management Science , year=

  51. [64]

    Management Science , volume=

    Contextual Learning with Online Convex Optimization: Theory and Application to Medical Decision-Making , author=. Management Science , volume=. 2025 , doi=

    2025

  52. [65]

    INFORMS Journal on Computing , year=

    Contextual Stochastic Vehicle Routing with Time Windows , author=. INFORMS Journal on Computing , year=

  53. [66]

    European Journal of Operational Research , volume=

    A survey of contextual optimization methods for decision-making under uncertainty , author=. European Journal of Operational Research , volume=. 2025 , publisher=

    2025

  54. [67]

    International Conference on Algorithmic Learning Theory , pages=

    A vector-contraction inequality for rademacher complexities , author=. International Conference on Algorithmic Learning Theory , pages=. 2016 , organization=

    2016

  55. [69]

    2018 , publisher=

    High-dimensional probability: An introduction with applications in data science , author=. 2018 , publisher=

    2018

  56. [71]

    2019 , publisher=

    High-Dimensional Statistics: A Non-Asymptotic Viewpoint , author=. 2019 , publisher=

    2019

  57. [72]

    Advances in Neural Information Processing Systems , volume=

    Fair and welfare-efficient constrained multi-matchings under uncertainty , author=. Advances in Neural Information Processing Systems , volume=

  58. [73]

    Management Science , volume=

    Feature-based dynamic pricing , author=. Management Science , volume=. 2020 , publisher=

    2020

  59. [75]

    The Review of Financial Studies , volume=

    Empirical asset pricing via machine learning , author=. The Review of Financial Studies , volume=. 2020 , publisher=

    2020

  60. [76]

    Transportation Research Part C: Emerging Technologies , volume=

    Data-driven robust optimization for contextual vehicle rebalancing in on-demand ride services under demand uncertainty , author=. Transportation Research Part C: Emerging Technologies , volume=. 2023 , publisher=

    2023

  61. [79]

    Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021) , pages=

    Designing Toxic Content Classification for a Diversity of Perspectives , author=. Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021) , pages=. 2021 , month=

    2021

  62. [81]

    Companion Proceedings of The 2019 World Wide Web Conference , pages=

    Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification , author=. Companion Proceedings of The 2019 World Wide Web Conference , pages=

    2019

  63. [82]

    2019 , publisher=

    Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina , booktitle=. 2019 , publisher=

    2019

  64. [83]

    2019 , eprint=

    Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas , booktitle=. 2019 , eprint=

    2019

  65. [84]

    The sign estimator: Llm alignment in the face of choice heterogeneity

    Ali Aouad, Aymane El Gadarri, and Vivek F Farias. The sign estimator: Llm alignment in the face of choice heterogeneity. arXiv preprint arXiv:2510.23965v2, 2025

    2025

  66. [85]

    The big data newsvendor: Practical insights from machine learning

    Gah-Yi Ban and Cynthia Rudin. The big data newsvendor: Practical insights from machine learning. Operations Research, 67 0 (1): 0 90--108, 2019

    2019

  67. [86]

    Zhang, and Heng Zhang

    Hamsa Bastani, Dennis J. Zhang, and Heng Zhang. Applied machine learning in operations management. In Innovative Technology at the Interface of Finance and Operations, pages 189--222. Springer, 2022. doi:10.1007/978-3-030-75729-8_7

    2022 doi

  68. [87]

    Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning

    Shai Ben-David, Tyler Lu, and D \'a vid P \'a l. Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning. In COLT, pages 33--44, 2008

    2008

  69. [88]

    Data-driven optimization: A reproducing kernel hilbert space approach

    Dimitris Bertsimas and Nihal Koduri. Data-driven optimization: A reproducing kernel hilbert space approach. Operations Research, 70 0 (1): 0 454--471, 2022

    2022

  70. [89]

    Combining labeled and unlabeled data with co-training

    Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 92--100, 1998

    1998

  71. [90]

    Nuanced metrics for measuring unintended bias with real data for text classification

    Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Nuanced metrics for measuring unintended bias with real data for text classification. In Companion Proceedings of The 2019 World Wide Web Conference, pages 491--500, 2019

    2019

  72. [91]

    Weak-to-strong generalization: eliciting strong capabilities with weak supervision

    Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al. Weak-to-strong generalization: eliciting strong capabilities with weak supervision. In Proceedings of the 41st Intern...

    2024

  73. [92]

    A theory of label propagation for subpopulation shift

    Tianle Cai, Ruiqi Gao, Jason Lee, and Qi Lei. A theory of label propagation for subpopulation shift. In International Conference on Machine Learning, pages 1170--1182. PMLR, 2021

    2021

  74. [93]

    Collaborative learning and decision making on pricing and recommendation: A simple framework for planning

    Junyu Cao. Collaborative learning and decision making on pricing and recommendation: A simple framework for planning. Management Science, 2025. doi:10.1287/mnsc.2023.00320. Articles in advance

    2025

  75. [94]

    Unlabeled data improves adversarial robustness

    Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang. Unlabeled data improves adversarial robustness. Advances in neural information processing systems, 32, 2019

    2019

  76. [95]

    Feature-rich, data-private: A sparse learning framework for the high-dimensional newsvendor

    Jinyuan Chang, Lin Yang, Yichen Zhang, and Wenxin Zhou. Feature-rich, data-private: A sparse learning framework for the high-dimensional newsvendor. Available at SSRN 5606310, 2025. doi:10.2139/ssrn.5606310

    2025 doi

  77. [96]

    Quantifying the gain in weak-to-strong generalization

    Moses Charikar, Chirag Pabbaraju, and Kirankumar Shiragur. Quantifying the gain in weak-to-strong generalization. Advances in neural information processing systems, 37: 0 126474--126499, 2024

    2024

  78. [97]

    Unleashing the potential of prompt engineering for large language models

    Banghao Chen, Zhaofeng Zhang, Nicolas Langren \'e , and Shengxin Zhu. Unleashing the potential of prompt engineering for large language models. Patterns, 6 0 (6), 2025

    2025

  79. [98]

    Nonparametric learning algorithms for joint pricing and inventory control with lost sales and censored demand

    Boxiao Chen, Xiuli Chao, and Cong Shi. Nonparametric learning algorithms for joint pricing and inventory control with lost sales and censored demand. Mathematics of Operations Research, 46 0 (2): 0 726--756, 2021. doi:10.1287/moor.2020.1084

    2021

  80. [99]

    Dynamic pricing and inventory control with fixed ordering cost and incomplete demand information

    Boxiao Chen, David Simchi-Levi, Yining Wang, and Yuan Zhou. Dynamic pricing and inventory control with fixed ordering cost and incomplete demand information. Management Science, 68 0 (8): 0 5684--5703, 2022. doi:10.1287/mnsc.2021.4171

    2022

  81. [100]

    Utility fairness in contextual dynamic pricing with demand learning

    Xi Chen, David Simchi-Levi, and Yining Wang. Utility fairness in contextual dynamic pricing with demand learning. Management Science, 72 0 (3): 0 2619--2633, 2026. doi:10.1287/mnsc.2023.03956

    2026

  82. [101]

    Feature-based dynamic pricing

    Maxime C Cohen, Ilan Lobel, and Renato Paes Leme. Feature-based dynamic pricing. Management Science, 66 0 (11): 0 4921--4943, 2020

    2020

  83. [102]

    Predictive stochastic programming

    Yunxiao Deng and Suvrajeet Sen. Predictive stochastic programming. Computational Management Science, 19 0 (1): 0 65--98, 2022

    2022

  84. [103]

    BERT : Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lang...

    2019 doi

  85. [104]

    Discrepancies are virtue: Weak-to-strong generalization through lens of intrinsic dimension

    Yijun Dong, Yicheng Li, Yunai Li, Jason D Lee, and Qi Lei. Discrepancies are virtue: Weak-to-strong generalization through lens of intrinsic dimension. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Resear...

    2025 arXiv

  86. [105]

    Task-based end-to-end model learning in stochastic optimization

    Priya Donti, Brandon Amos, and J Zico Kolter. Task-based end-to-end model learning in stochastic optimization. Advances in neural information processing systems, 30, 2017

    2017

  87. [106]

    Generalization bounds in the predict-then-optimize framework

    Othman El Balghiti, Adam N Elmachtoub, Paul Grigas, and Ambuj Tewari. Generalization bounds in the predict-then-optimize framework. Advances in neural information processing systems, 32, 2019

    2019

  88. [107]

    predict, then optimize

    Adam N Elmachtoub and Paul Grigas. Smart “predict, then optimize”. Management Science, 68 0 (1): 0 9--26, 2022

    2022

  89. [108]

    Decision trees for decision-making under the predict-then-optimize framework

    Adam N Elmachtoub, Jason Cheuk Nam Liang, and Ryan McNellis. Decision trees for decision-making under the predict-then-optimize framework. In International conference on machine learning, pages 2858--2867. PMLR, 2020

    2020

  90. [109]

    Estimate-then-optimize versus integrated-estimation-optimization versus sample average approximation: a stochastic dominance perspective

    Adam N Elmachtoub, Henry Lam, Haofeng Zhang, and Yunfan Zhao. Estimate-then-optimize versus integrated-estimation-optimization versus sample average approximation: a stochastic dominance perspective. arXiv preprint arXiv:2304.06833v4, 2023

    2023 arXiv

  91. [110]

    Analytics for an online retailer: Demand forecasting and price optimization

    Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18 0 (1): 0 69--88, 2016. doi:10.1287/msom.2015.0561

    2016

  92. [111]

    Great models think alike and this undermines ai oversight

    Shashwat Goel, Joschka Struber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping. Great models think alike and this undermines ai oversight. arXiv preprint arXiv:2502.04313v2, 2025

    2025 arXiv

  93. [112]

    Semi-supervised learning by entropy minimization

    Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In Advances in Neural Information Processing Systems, volume 17, 2004

    2004

  94. [113]

    Empirical asset pricing via machine learning

    Shihao Gu, Bryan Kelly, and Dacheng Xiu. Empirical asset pricing via machine learning. The Review of Financial Studies, 33 0 (5): 0 2223--2273, 2020

    2020

  95. [114]

    Data-driven robust optimization for contextual vehicle rebalancing in on-demand ride services under demand uncertainty

    Zhen Guo, Bin Yu, Wenxuan Shan, and Baozhen Yao. Data-driven robust optimization for contextual vehicle rebalancing in on-demand ride services under demand uncertainty. Transportation Research Part C: Emerging Technologies, 154: 0 104244, 2023

    2023

  96. [115]

    Scaling laws for transfer

    Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish. Scaling laws for transfer. arXiv preprint arXiv:2102.01293, 2021

    2021 arXiv

  97. [116]

    Training compute-optimal large language models

    Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. In Advances in Neural Information Processing System...

    2022 doi

  98. [117]

    A data-driven newsvendor problem: From data to decision

    Jakob Huber, Sebastian M \"u ller, Moritz Fleischmann, and Heiner Stuckenschmidt. A data-driven newsvendor problem: From data to decision. European Journal of Operational Research, 278 0 (3): 0 904--915, 2019

    2019

  99. [118]

    Neural tangent kernel: Convergence and generalization in neural networks

    Arthur Jacot, Franck Gabriel, and Cl \'e ment Hongler. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems, 31, 2018

    2018

  100. [119]

    Multi-task dynamic pricing in credit market with contextual information

    Adel Javanmard, Jingwei Ji, and Renyuan Xu. Multi-task dynamic pricing in credit market with contextual information. arXiv preprint arXiv:2410.14839v4, 2024

    2024

  101. [120]

    Jigsaw Unintended Bias in Toxicity Classification

    Jigsaw . Jigsaw Unintended Bias in Toxicity Classification . Kaggle competition data page, 2019. URL https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data. Accessed June 24, 2026

    2019

  102. [121]

    Scaling laws for neural language models

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020

    2001 arXiv

  103. [122]

    Van Oyen, and Pooyan Kazemian

    Esmaeil Keyvanshokooh, Mohammad Zhalechian, Cong Shi, Mark P. Van Oyen, and Pooyan Kazemian. Contextual learning with online convex optimization: Theory and application to medical decision-making. Management Science, 71 0 (12): 0 10442--10464, 2025. doi:10.1287/mnsc.2019.03211

    2025

  104. [123]

    Designing toxic content classification for a diversity of perspectives

    Deepak Kumar, Patrick Gage Kelley, Sunny Consolvo, Joshua Mason, Elie Bursztein, Zakir Durumeric, Kurt Thomas, and Michael Bailey. Designing toxic content classification for a diversity of perspectives. In Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021), page...

    2021

  105. [124]

    Debate helps weak-to-strong generalization

    Hao Lang, Fei Huang, and Yongbin Li. Debate helps weak-to-strong generalization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 27410--27418, 2025

    2025

  106. [125]

    Theoretical analysis of weak-to-strong generalization

    Hunter Lang, David Sontag, and Aravindan Vijayaraghavan. Theoretical analysis of weak-to-strong generalization. Advances in neural information processing systems, 37: 0 46837--46880, 2024

    2024

  107. [126]

    Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

    Dong-Hyun Lee. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In ICML Workshop on Challenges in Representation Learning, 2013

    2013

  108. [127]

    Design and scheduling of an ai-based queueing system

    Jiung Lee, Hongseok Namkoong, and Yibo Zeng. Design and scheduling of an ai-based queueing system. arXiv preprint arXiv:2406.06855v3, 2024

    2024

  109. [128]

    Does weak-to-strong generalization happen under spurious correlations? In The Fourteenth International Conference on Learning Representations, 2026

    Chenruo Liu, Yijun Dong, and Qi Lei. Does weak-to-strong generalization happen under spurious correlations? In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=5hfa2itwGz. arXiv:2509.24005v2

    2026

  110. [129]

    Fair and welfare-efficient constrained multi-matchings under uncertainty

    Elita Lobo, Justin Payan, Cyrus Cousins, and Yair Zick. Fair and welfare-efficient constrained multi-matchings under uncertainty. Advances in Neural Information Processing Systems, 37: 0 74579--74616, 2024

    2024

  111. [130]

    Decision-driven regularization: A blended model for learning and optimization

    Gar Goei Loke, Qinshen Tang, Yangge Xiao, and Xun Zhang. Decision-driven regularization: A blended model for learning and optimization. Available at SSRN 3623006, 2020. doi:10.2139/ssrn.3623006

    2020 doi

  112. [131]

    A vector-contraction inequality for rademacher complexities

    Andreas Maurer. A vector-contraction inequality for rademacher complexities. In International Conference on Algorithmic Learning Theory, pages 3--17. Springer, 2016

    2016

  113. [132]

    Softmax is 1/2 -lipschitz: A tight bound across all ell_ norms

    Pravin Nair. Softmax is 1/2 -lipschitz: A tight bound across all ell_ norms. arXiv preprint arXiv:2510.23012, 2025

    2025

  114. [133]

    Integrating prediction/estimation and optimization with applications in operations management

    Meng Qi and Zuo-Jun Shen. Integrating prediction/estimation and optimization with applications in operations management. In Tutorials in operations research: emerging and impactful topics in operations, pages 36--58. INFORMS, 2022

    2022

  115. [134]

    Integrated conditional estimation-optimization

    Meng Qi, Paul Grigas, and Zuo-Jun Shen. Integrated conditional estimation-optimization. Operations Research, 74 0 (3): 0 1604--1625, 2025. doi:10.1287/opre.2023.0427

    2025

  116. [135]

    A survey of contextual optimization methods for decision-making under uncertainty

    Utsav Sadana, Abhilash Chenreddy, Erick Delage, Alexandre Forel, Emma Frejinger, and Thibaut Vidal. A survey of contextual optimization methods for decision-making under uncertainty. European Journal of Operational Research, 320 0 (2): 0 271--289, 2025

    2025

  117. [136]

    DistilBERT , a distilled version of BERT : Smaller, faster, cheaper and lighter

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT , a distilled version of BERT : Smaller, faster, cheaper and lighter. In Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019, 2019. URL https://ar...

    2019 arXiv

  118. [137]

    Florio, Stefan Minner, Maximilian Schiffer, and Thibaut Vidal

    Breno Serrano, Alexandre M. Florio, Stefan Minner, Maximilian Schiffer, and Thibaut Vidal. Contextual stochastic vehicle routing with time windows. INFORMS Journal on Computing, 2026. doi:10.1287/ijoc.2025.1189. Articles in advance

    2026

  119. [138]

    Technical note--nonparametric data-driven algorithms for multiproduct inventory systems with censored demand

    Cong Shi, Weidong Chen, and Izak Duenyas. Technical note--nonparametric data-driven algorithms for multiproduct inventory systems with censored demand. Operations Research, 64 0 (2): 0 362--370, 2016. doi:10.1287/opre.2015.1474

    2016

  120. [139]

    MICo : Preventative detoxification of Large Language Models through inhibition control

    Roy Siegelmann, Ninareh Mehrabi, Palash Goyal, Prasoon Goyal, Lisa Bauer, Jwala Dhamala, Aram Galstyan, Rahul Gupta, and Reza Ghanadan. MICo : Preventative detoxification of Large Language Models through inhibition control. In Findings of the Association for Computational Ling...

    2024 doi

  121. [140]

    Fixmatch: Simplifying semi-supervised learning with consistency and confidence

    Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, volume 33...

    2020

  122. [141]

    Introduction to the non-asymptotic analysis of random matrices

    Roman Vershynin. Introduction to the non-asymptotic analysis of random matrices. arXiv preprint arXiv:1011.3027, 2010

    2010 arXiv

  123. [142]

    High-dimensional probability: An introduction with applications in data science, volume 47

    Roman Vershynin. High-dimensional probability: An introduction with applications in data science, volume 47. Cambridge university press, 2018

    2018

  124. [143]

    Wainwright

    Martin J. Wainwright. High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge University Press, 2019

    2019

  125. [144]

    On data-driven prescriptive analytics with side information: A regularized nadaraya--watson approach

    Yijie Wang, Prateek R Srivastava, Grani A Hanasusanto, and Chin Pang Ho. On data-driven prescriptive analytics with side information: A regularized nadaraya--watson approach. Manufacturing & Service Operations Management, 28 0 (3): 0 841--859, 2026. doi:10.1287/msom.2024.0997

    2026

  126. [145]

    Glynn, and Yinyu Ye

    Zizhuo Wang, Peter W. Glynn, and Yinyu Ye. Likelihood robust optimization for data-driven problems. Computational Management Science, 13 0 (2): 0 241--261, 2016. doi:10.1007/s10287-015-0240-3

    2016 doi

  127. [146]

    On the sample complexity of semi-supervised multi-objective learning

    Tobias Wegel, Geelon So, Junhyung Park, and Fanny Yang. On the sample complexity of semi-supervised multi-objective learning. arXiv preprint arXiv:2508.17152, 2025

    2025 arXiv

  128. [147]

    Theoretical analysis of self-training with deep networks on unlabeled data

    Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma. Theoretical analysis of self-training with deep networks on unlabeled data. In International Conference on Learning Representations, 2021

    2021

  129. [148]

    Kernel and rich regimes in overparametrized models

    Blake Woodworth, Suriya Gunasekar, Jason D Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro. Kernel and rich regimes in overparametrized models. In Conference on Learning Theory, pages 3635--3673. PMLR, 2020

    2020

  130. [149]

    Survey on knowledge distillation for large language models: methods, evaluation, and application

    Chuanpeng Yang, Yao Zhu, Wang Lu, Yidong Wang, Qian Chen, Chenlong Gao, Bingjie Yan, and Yiqiang Chen. Survey on knowledge distillation for large language models: methods, evaluation, and application. ACM Transactions on Intelligent Systems and Technology, 16 0 (6): 0 1--27, 2025

    2025

  131. [150]

    A survey on deep semi-supervised learning

    Xiangli Yang, Zixing Song, Irwin King, and Zenglin Xu. A survey on deep semi-supervised learning. IEEE transactions on knowledge and data engineering, 35 0 (9): 0 8934--8954, 2022

    2022

  132. [151]

    Policy learning with adaptively collected data

    Ruohan Zhan, Zhimei Ren, Susan Athey, and Zhengyuan Zhou. Policy learning with adaptively collected data. Management Science, 70 0 (8): 0 5270--5297, 2023. doi:10.1287/mnsc.2023.4921

    2023

  133. [152]

    Weakly-supervised structural component segmentation via scribble annotations

    Chenyu Zhang, Ke Li, Zhaozheng Yin, and Ruwen Qin. Weakly-supervised structural component segmentation via scribble annotations. Computer-Aided Civil and Infrastructure Engineering, 40 0 (5): 0 561--578, 2025

    2025

  134. [153]

    Assessing the performance of deep learning algorithms for newsvendor problem

    Yanfei Zhang and Junbin Gao. Assessing the performance of deep learning algorithms for newsvendor problem. In International conference on neural information processing, pages 912--921. Springer, 2017

    2017

  135. [154]

    Offline multi-action policy learning: Generalization and optimization

    Zhengyuan Zhou, Susan Athey, and Stefan Wager. Offline multi-action policy learning: Generalization and optimization. Operations Research, 71 0 (1): 0 148--183, 2023. doi:10.1287/opre.2022.2271

    2023

  136. [155]

    Semi-supervised learning using gaussian fields and harmonic functions

    Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, pages 912--919, 2003

    2003

Pith tools

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