LHCb reports the first upper limits on B0 → K+π−τ+τ− and Bs0 → K+K−τ+τ− branching fractions, with recast limits of 2.8×10−4 on B0 → K*(892)0 τ+τ− at 95% CL that improve prior bounds by an order of magnitude.
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Optuna: A Next-generation Hyperparameter Optimization Framework
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abstract
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).
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representative citing papers
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Quantum reservoirs handle multivariate time series best with task-specific encodings that leverage non-classical effects.
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citing papers explorer
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Searches for $B^0\to K^+\pi^-\tau^+\tau^-$ and $B_s^0\to K^+K^-\tau^+\tau^-$ decays
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Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations
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Multivariate quantum reservoir computing with discrete and continuous variable systems
Quantum reservoirs handle multivariate time series best with task-specific encodings that leverage non-classical effects.
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From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Machine learning cloud microphysics parameterization achieves stable decade-long online coupling in ICON with performance comparable to the classical graupel scheme while eliminating two tuning parameters.
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Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
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optimize_anything: A Universal API for Optimizing any Text Parameter
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Efficient Brain Extraction of MRI Scans with Mild to Moderate Neuropathology
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TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation
TreeCoder improves LLM code generation accuracy by representing decoding as an optimizable tree search over programs with first-class constraints for syntax, style, and execution, outperforming baselines on MBPP and SQL-Spider.
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Search for the lepton-flavor-violating $\tau^{-} \rightarrow e^{\mp} \ell^{\pm} \ell^{\mp}$ decays at Belle II
Belle II sets upper limits between 1.3 and 2.5 times 10 to the minus 8 on branching fractions for four tau to e l l decay modes at 90 percent confidence level, the most stringent to date for four modes.
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A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation
A new leaf-instance dataset for soybean-cotton detection and segmentation collected across growth stages and conditions from commercial farms is presented and validated with YOLOv11.
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On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
Tabular diffusion models leak membership information via attacks even with partial attacker knowledge, and common heuristic privacy metrics like distance-to-closest-record are unreliable.
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Euclid preparation. CosmoPostProcess: A simulation calibrated framework for weak lensing selection bias in richness-selected galaxy clusters
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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
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Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction
Natural language embeddings of synthesis and testing conditions improve ML predictions of glass dissolution rates and enable generalization to out-of-distribution compositions with new elements.
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Search for the lepton-flavour violating decays $B^+ \to \pi^+ \mu^\pm e^\mp$
No signal observed for B+ → π+ μ± e∓; branching fraction upper limit set at 1.8 × 10^{-9} at 90% CL.
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Star-formation variability on the star-forming main sequence during the Epoch of Reionization
PSD modeling of SFR scatter at six timescales shows dominant variability on 10-30 Myr scales, stronger in lower-mass galaxies at z=3-8.
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Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.
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Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC
A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
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Improved Chase-Pyndiah Decoding for Product Codes with Scaled Messages
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VIGILant: an automatic classification pipeline for glitches in the Virgo detector
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An Automatic Ground Collision Avoidance System with Reinforcement Learning
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