How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design | Paper Citation Record

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design SCIP: solving constraint integer programs

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning to prune: Speeding up repeated computations

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Densit´ e et dimension

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Data-driven algorithm design

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Mechanism design via machine learning

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Sample complexity of auto- mated mechanism design

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning- theoretic foundations of algorithm conﬁguration for combinatorial partitioning problems

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning to branch

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Dispersion for data-driven algorithm design, online learning, and private optimization

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A general theory of sample complexity for multi-item proﬁt maximization

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning to link

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Semi-bandit optimization in the dispersed setting

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning to optimize com- putational resources: Frugal training with generalization guarantees

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Generalization in portfolio- based algorithm selection

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Some unexpected expected behavior results for bin packing

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning complexity of simulated annealing

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Partition of space

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning multi-item auctions with (or without) samples

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Pandora’s box with correlations: Learning and approximation

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning to Schedule Heuristics in Branch-and-Bound

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Unresolved cited work

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The sample complexity of revenue maximization

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Parameter Advising for Multiple Sequence Alignment

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The sample complexity of auctions with side information

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Secretaries with Advice

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning-based support estimation in sublinear time

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Quality measures for protein alignment benchmarks

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Designing and learning optimal ﬁnite support auctions

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Rein- forcement learning for variable selection in a branch and bound algorithm

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning augmented energy minimization via speed scaling

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The primal-dual method for learn- ing augmented algorithms

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The RPR 2 rounding technique for semideﬁnite programs

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Progressive sequence alignment as a prerequisite to correct phylogenetic trees

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design MIPaaL: Mixed integer program as a layer

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Parametric multiple sequence alignment and phylogeny construction

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Identiﬁcation of alternative topological domains in chromatin

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A machine learning- based branch and price algorithm for a sampled vehicle routing problem

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Supervising unsupervised learning

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Information-theoretic approaches to branching in search

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Improved approximation algorithms for max- imum cut and satisﬁability problems using semideﬁnite programming

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Eigentaste: A constant time collaborative ﬁltering algorithm

Reference 42

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Eﬃcient empirical revenue maximization in single- parameter auction environments

Reference 43

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The sample complexity of up-to- ε multi- dimensional revenue maximization

Reference 44

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design An improved algorithm for matching biological sequences

Reference 45

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Incentives in teams

Reference 46

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Settling the sample complexity of single- parameter revenue maximization

Reference 47

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A PAC approach to application-speciﬁc algorithm se- lection

Reference 48

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Parametric and inverse-parametric sequence alignment with xparal

Reference 49

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Parametric optimization of se- quence alignment

Reference 50

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Clustal: a package for performing multiple sequence alignment on a microcomputer

Reference 51

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Holley, Jean Apgar, George A

Reference 52

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This paper cites A Bayesian approach to tackling hard computational problems.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A Bayesian approach to tackling hard computational problems

Reference 53

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning-based frequency estima- tion algorithms

Reference 54

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This paper cites ParamILS: An auto- matic algorithm conﬁguration framework.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design ParamILS: An auto- matic algorithm conﬁguration framework

Reference 55

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This paper cites Online Page Migration with ML Advice.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Online Page Migration with ML Advice

Reference 56

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This paper cites An experimental study of polylogarithmic, fully dynamic, connectivity algorithms.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design An experimental study of polylogarithmic, fully dynamic, connectivity algorithms

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.334365Z

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Observation 609a3c49-529f-4793-ae1e-b1bcc3ddaf35 · outbound

This paper cites ISAC-instance-speciﬁc algorithm conﬁguration.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design ISAC-instance-speciﬁc algorithm conﬁguration

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.318892Z

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Observation dfeca0e1-984c-4cde-843a-dd7bbddad1f7 · outbound

This paper cites Aligning alignments exactly.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Aligning alignments exactly

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.303730Z

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Observation b2b7b739-0ef9-4c20-a63b-965589b676d8 · outbound

This paper cites Inverse sequence alignment from partial examples.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Inverse sequence alignment from partial examples

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.288365Z

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Observation f70a5d3f-42f6-4ca5-9cfd-e985deea2a51 · outbound

This paper cites Eﬃciency through procrastina- tion: Approximately optimal algorithm conﬁguration with runtime guarantees.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Eﬃciency through procrastina- tion: Approximately optimal algorithm conﬁguration with runtime guarantees

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.272892Z

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Observation 051f292e-0e30-43e2-90e9-3ea4f441bbe5 · outbound

This paper cites Procrastinat- ing with conﬁdence: Near-optimal, anytime, adaptive algorithm conﬁguration.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Procrastinat- ing with conﬁdence: Near-optimal, anytime, adaptive algorithm conﬁguration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.257684Z

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Observation 762a20f5-4a1d-4dce-87c9-e650264fb1a9 · outbound

This paper cites End-to-End Constrained Optimization Learning: A Survey.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design End-to-End Constrained Optimization Learning: A Survey

Reference 63

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Observation 67e28948-fcea-4132-a50e-4016a8f41be3 · outbound

This paper cites An automatic method of solving discrete programming problems.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design An automatic method of solving discrete programming problems

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.242313Z

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Observation 3e029a16-758b-43aa-814e-00c03e22058c · outbound

This paper cites Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:42:15.386862Z

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Observation dba54c12-cc73-49cc-8762-1636b52a0637 · outbound

This paper cites Empirical hardness models: Methodology and a case study on combinatorial auctions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Empirical hardness models: Methodology and a case study on combinatorial auctions

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.226259Z

Source-reported events for the cited work

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Observation 3e4e252d-f3c2-4f94-832d-cf92a5fcc7ce · outbound

This paper cites van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy, Agnes Telling, Ido Amit, Bryan R.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy, Agnes Telling, Ido Amit, Bryan R

Reference 67

Resolution: verified exact; doi, observed 2026-08-14T14:42:15.346187Z

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Observation 21676874-eb48-433b-95b6-b03275674a02 · outbound

This paper cites Methods for boosting revenue in combinatorial auctions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Methods for boosting revenue in combinatorial auctions

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.210592Z

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Observation 2f37a9f3-c1e4-4cf6-958d-badd64044339 · outbound

This paper cites Approximating revenue-maximizing combinato- rial auctions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Approximating revenue-maximizing combinato- rial auctions

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.195157Z

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Observation a569b518-21b4-4210-a60b-98020fb3b927 · outbound

This paper cites A computational study of search strategies for mixed integer programming.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A computational study of search strategies for mixed integer programming

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.179450Z

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Observation 66170b18-d5a3-4ba4-bd41-531d6c12094d · outbound

This paper cites Breaking TADs: how alterations of chromatin domains result in disease.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Breaking TADs: how alterations of chromatin domains result in disease

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.163557Z

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Observation 450f4bf7-30c8-4b8a-86ed-79f3f2a3cf04 · outbound

This paper cites Competitive caching with machine learned advice.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Competitive caching with machine learned advice

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.147323Z

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Observation 1d442fe9-481c-427f-b7d8-cbcd348dfad4 · outbound

This paper cites A guide to experimental algorithmics.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A guide to experimental algorithmics

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.131539Z

Source-reported events for the cited work

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Observation 9bb80893-ee7b-412b-913b-1fd6fe978cd3 · outbound

This paper cites Roberts’ theorem with neutrality: A social welfare ordering approach.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Roberts’ theorem with neutrality: A social welfare ordering approach

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.116531Z

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Observation 61a08065-3471-4897-be03-67a8876b1fdc · outbound

This paper cites A model for learned bloom ﬁlters and optimizing by sandwiching.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A model for learned bloom ﬁlters and optimizing by sandwiching

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.100933Z

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Observation 56a52cd8-3adb-4635-87f8-fa9aee501776 · outbound

This paper cites Learning theory and algorithms for revenue optimization in second price auctions with reserve.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning theory and algorithms for revenue optimization in second price auctions with reserve

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.085185Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d100c98a-ae14-47b6-a151-7dc1b1b66547 · outbound

This paper cites Learning simple auctions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Learning simple auctions

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.069045Z

Source-reported events for the cited work

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Observation c1219b90-d6a1-4bd7-a1cd-24e4d60aac5f · outbound

This paper cites Eﬃciency and budget balance in general quasi-linear domains.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Eﬃciency and budget balance in general quasi-linear domains

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.053289Z

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Observation 468bdf21-5e08-4d89-924d-616281ed3b54 · outbound

This paper cites Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.036883Z

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Observation 1039cbd0-ba4d-4325-b0ca-a133fe2d4844 · outbound

This paper cites Fast algorithm for predicting the secondary structure of single-stranded RNA.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Fast algorithm for predicting the secondary structure of single-stranded RNA

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:16.018824Z

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Observation 11eada1f-8eb4-49a0-bb6e-726192c32034 · outbound

This paper cites Parametric inference for biological sequence analysis.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Parametric inference for biological sequence analysis

Reference 81

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Observation 2020dbeb-20eb-459a-b4d9-b6f9f27837f4 · outbound

This paper cites Tropical geometry of statistical models.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Tropical geometry of statistical models

Reference 82

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Observation 2a9bf340-7e13-4aab-bb92-11781e9cea7b · outbound

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Convergence of Stochastic Processes

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.986144Z

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Observation 9b4d3234-18fb-450a-9e4f-296079db6d46 · outbound

This paper cites Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-14T14:42:15.142088Z

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Improving online algorithms via ML pre- dictions

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.968297Z

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Observation e0c82d1b-4380-441f-b3a8-45f25c4ce40c · outbound

This paper cites The characterization of implementable social choice rules.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design The characterization of implementable social choice rules

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.952586Z

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Observation 50dd0fe6-6970-4c3d-bba1-ff125fa7fc7c · outbound

This paper cites Very-large-scale generalized combinatorial multi-attribute auctions: Lessons from conducting $60 billion of sourcing.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Very-large-scale generalized combinatorial multi-attribute auctions: Lessons from conducting $60 billion of sourcing

Reference 87

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Observation cf3f5db0-b159-41d7-8f03-f03eb7d5a12c · outbound

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How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Automated design of revenue-maximizing com- binatorial auctions

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.926802Z

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Observation 5fb484bf-4db3-4f11-9d67-3d7f751a799a · outbound

This paper cites Michael Sauder, Jonathan W.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Michael Sauder, Jonathan W

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.910606Z

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This paper cites On the density of families of sets.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design On the density of families of sets

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.893953Z

Source-reported events for the cited work

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Observation 8a1cb5cc-9b23-4825-b1a5-50e22f792084 · outbound

This paper cites Guiding high-performance SAT solvers with unsat-core predictions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Guiding high-performance SAT solvers with unsat-core predictions

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.878382Z

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Observation 6c528ba4-da44-4f0e-ac7a-bc425d28f2e6 · outbound

This paper cites Understanding machine learning: From theory to algorithms.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Understanding machine learning: From theory to algorithms

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-14T14:42:15.180598Z

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Observation 6765b5f1-a310-45c6-8895-831572c9266e · outbound

This paper cites Lorm: Learning to optimize for resource management in wireless networks with few training samples.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Lorm: Learning to optimize for resource management in wireless networks with few training samples

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.852172Z

Source-reported events for the cited work

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Observation 738f5e62-bcb9-4cd3-8f30-89d4f086e284 · outbound

This paper cites A general large neighborhood search framework for solving integer programs.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design A general large neighborhood search framework for solving integer programs

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.836086Z

Source-reported events for the cited work

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Observation 2a71cb88-aafa-463b-b701-03a77d581602 · outbound

This paper cites Reinforcement learning for integer program- ming: Learning to cut.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Reinforcement learning for integer program- ming: Learning to cut

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.820262Z

Source-reported events for the cited work

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Observation 375f6840-7ac7-4b55-b2da-363ff7cbf326 · outbound

This paper cites On the zeros of ﬁnite sums of exponential functions.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design On the zeros of ﬁnite sums of exponential functions

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.803692Z

Source-reported events for the cited work

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Observation 0fae430e-3bdc-46be-b5ef-074022db4b96 · outbound

This paper cites On the uniform convergence of relative frequencies of events to their probabilities.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design On the uniform convergence of relative frequencies of events to their probabilities

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.788175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9a08fefe-81b4-4331-95ed-b4de5d4fcdac · outbound

This paper cites Counterspeculation, auctions, and competitive sealed tenders.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Counterspeculation, auctions, and competitive sealed tenders

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.771793Z

Source-reported events for the cited work

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Observation b26a789c-4088-4728-bfff-7bf99a5b551a · outbound

This paper cites On the complexity of multiple sequence alignment.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design On the complexity of multiple sequence alignment

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.756405Z

Source-reported events for the cited work

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Observation e5c33913-c3f0-4c5c-ac9e-f9073e70bc17 · outbound

This paper cites Some biological sequence metrics.

How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design Some biological sequence metrics

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:42:15.740913Z

Source-reported events for the cited work

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