Generalization Bounds

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Most implemented papers archive 2025-07-28

30 shown of 161 papers with code (686 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Bridging Theory and Algorithm for Domain Adaptation
  11 Apr 2019 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We introduce Margin Disparity Discrepancy, a novel measurement with rigorous generalization bounds, tailored to the distribution comparison with the asymmetric margin loss, and to the minimax optimization for easier…
• SWAD: Domain Generalization by Seeking Flat Minima
  17 Feb 2021 4 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
• Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
  9 Nov 2020 4 repositories listed
  We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not…
• Estimating individual treatment effect: generalization bounds and algorithms
  13 Jun 2016 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the…
• Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
  6 Jun 2022 3 repositories listed Syntology ran 7 of 11 samples · 4 unverified
  We study the generalization properties of fine-tuning to understand the problem of overfitting, which has often been observed (e.
• Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
  6 Jul 2021 3 repositories listed
  Meta learning has attracted much attention recently in machine learning community.
• PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
  13 Feb 2020 3 repositories listed
  Meta-learning can successfully acquire useful inductive biases from data.
• A Surprising Linear Relationship Predicts Test Performance in Deep Networks
  25 Jul 2018 3 repositories listed
  Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors?
• Optimal Auctions through Deep Learning: Advances in Differentiable Economics
  12 Jun 2017 3 repositories listed
  Designing an incentive compatible auction that maximizes expected revenue is an intricate task.
• Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
  31 Mar 2017 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 2 pointer-only (licence)
  One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data.
• Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization
  3 Oct 2024 2 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  Fine-tuning only the 𝐖_q and 𝐖ᵥ matrices is computationally efficient while delivering results comparable to, or even better than fine-tuning all three matrices (𝐖_q, 𝐖ₖ, and 𝐖ᵥ).
• Learning to Warm-Start Fixed-Point Optimization Algorithms
  14 Sep 2023 2 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  We introduce a machine-learning framework to warm-start fixed-point optimization algorithms.
• Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
  9 Feb 2023 2 repositories listed Syntology ran 6 of 12 samples · 6 unverified
  Graph neural networks are widely used tools for graph prediction tasks.
• Transformers as Algorithms: Generalization and Stability in In-context Learning
  17 Jan 2023 2 repositories listed
  We first explore the statistical aspects of this abstraction through the lens of multitask learning: We obtain generalization bounds for ICL when the input prompt is (1) a sequence of i.
• NICO++: Towards Better Benchmarking for Domain Generalization
  17 Apr 2022 2 repositories listed
  Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
• Fast Interpretable Greedy-Tree Sums
  28 Jan 2022 2 repositories listed
  In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
• Personalized Federated Learning through Local Memorization
  17 Nov 2021 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Federated learning allows clients to collaboratively learn statistical models while keeping their data local.
• Robust Generalization despite Distribution Shift via Minimum Discriminating Information
  8 Jun 2021 2 repositories listed
  Training models that perform well under distribution shifts is a central challenge in machine learning.
• Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
  8 Jun 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Despite the empirical successes, theoretical foundations are limited -- prior analyses assume conditional independence of the positive pairs given the same class label, but recent empirical applications use heavily…
• Generalization Bounds for Sparse Random Feature Expansions
  4 Mar 2021 2 repositories listed
  In particular, we provide generalization bounds for functions in a certain class (that is dense in a reproducing kernel Hilbert space) depending on the number of samples and the distribution of features.
• Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
  22 Dec 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the…
• Minimax Classification with 0-1 Loss and Performance Guarantees
  15 Oct 2020 2 repositories listed
  We also present MRCs' finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.
• Generalization Guarantees for Imitation Learning
  5 Aug 2020 2 repositories listed
  Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.
• Learning Robust State Abstractions for Hidden-Parameter Block MDPs
  14 Jul 2020 2 repositories listed
  Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of…
• PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
  31 Dec 2019 2 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.
• Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
  3 Jul 2019 2 repositories listed
  In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regression and softmax regression.
• Deep multi-Wasserstein unsupervised domain adaptation
  30 Apr 2019 2 repositories listed
  In unsupervised domain adaptation (DA), 1 aims at learning from labeled source data and fully unlabeled target examples a model with a low error on the target domain.
• Deep Learning and the Information Bottleneck Principle
  9 Mar 2015 2 repositories listed
  Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle.
• Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
  27 May 2025 1 repository listed
  Out-of-distribution (OOD) generalization remains a fundamental challenge in machine learning.
• Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
  23 May 2025 1 repository listed
  We consider the problem of learning the evolution operator for the time-dependent Schr\"{o}dinger equation, where the Hamiltonian may vary with time.

Syntology lines on 12 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.