A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
Hyperband: A novel bandit-based approach to hyperparameter optimization
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.
verdicts
UNVERDICTED 7representative citing papers
No algorithm can be optimal in both stochastic and adversarial best-arm identification; a new parameter-free algorithm matches the derived lower bound up to log factors in stochastic cases while handling adversarial rewards.
Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.
QuickScope uses modified COUP Bayesian optimization to find truly difficult questions in dynamic LLM benchmarks more sample-efficiently than baselines while cutting false positives.
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.
Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.
A perspective proposes a synthesis-first paradigm for AI-driven materials discovery, treating protocols rather than structures as the key variables to close the synthesizability gap via machine-readable recipes, generative models, and closed-loop optimization.
citing papers explorer
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Budgeted Online Influence Maximization
A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
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Best of both worlds: Stochastic & adversarial best-arm identification
No algorithm can be optimal in both stochastic and adversarial best-arm identification; a new parameter-free algorithm matches the derived lower bound up to log factors in stochastic cases while handling adversarial rewards.
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Joint Detection of Malicious Domains and Infected Clients
Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.
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QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks
QuickScope uses modified COUP Bayesian optimization to find truly difficult questions in dynamic LLM benchmarks more sample-efficiently than baselines while cutting false positives.
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When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.
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Quantifying the Carbon Emissions of Machine Learning
Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.
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Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
A perspective proposes a synthesis-first paradigm for AI-driven materials discovery, treating protocols rather than structures as the key variables to close the synthesizability gap via machine-readable recipes, generative models, and closed-loop optimization.