mailitics

Tag: best

  • Best technique for training models on a sample of data?

    Best technique for training models on a sample of data? Due to memory limits on my work computer I’m unable to train machine learning models on our entire analysis dataset. Given my data is highly imbalanced I’m under-sampling from the majority class of the binary outcome. What is the proper method to train ML models…

  • The Best Data Scientists Are Always Learning

    The Best Data Scientists Are Always Learning Part 2: Avoiding burnout, learning strategies and the superpower of solitude The post The Best Data Scientists Are Always Learning appeared first on Towards Data Science. Jarom Hulet Go to original source

  • ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI

    ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI For the last couple of years, a lot of the conversation around AI has revolved around a single, deceptively simple question: Which model is the best? But the next question was always, the best for what?  The best for reasoning? Writing? Coding? Or…

  • NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating

    NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating This one little trick can bring about enhanced training stability, the use of larger learning rates and improved scaling properties The post NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating appeared first on Towards Data Science. Sean Moran Go to original…

  • The Best Data Scientists are Always Learning

    The Best Data Scientists are Always Learning Why continuous learning matters & how to come up with topics to study The post The Best Data Scientists are Always Learning appeared first on Towards Data Science. Jarom Hulet Go to original source

  • Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations

    Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations arXiv:2511.21890v1 Announce Type: new Abstract: We study Sparse Multiple Kernel Learning (SMKL), which is the problem of selecting a sparse convex combination of prespecified kernels for support vector binary classification. Unlike prevailing l1 regularized approaches that approximate a sparsifying penalty, we formulate the problem by…

  • Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs

    Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs A small-scale exploration using Tiny Transformers The post Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs appeared first on Towards Data Science. Shuyang Go to original source

  • Identifying All {epsilon}-Best Arms in (Misspecified) Linear Bandits

    Identifying All {epsilon}-Best Arms in (Misspecified) Linear Bandits arXiv:2510.00073v1 Announce Type: new Abstract: Motivated by the need to efficiently identify multiple candidates in high trial-and-error cost tasks such as drug discovery, we propose a near-optimal algorithm to identify all {epsilon}-best arms (i.e., those at most {epsilon} worse than the optimum). Specifically, we introduce LinFACT, an…

  • Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits

    Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits arXiv:2508.18768v1 Announce Type: new Abstract: We introduce the first best-of-both-worlds algorithm for contextual combinatorial semi-bandits that simultaneously guarantees $widetilde{mathcal{O}}(sqrt{T})$ regret in the adversarial regime and $widetilde{mathcal{O}}(ln T)$ regret in the corrupted stochastic regime. Our approach builds on the Follow-the-Regularized-Leader (FTRL) framework equipped with a Shannon entropy regularizer, yielding…

  • Balancing Performance and Costs in Best Arm Identification

    Balancing Performance and Costs in Best Arm Identification arXiv:2505.20583v1 Announce Type: new Abstract: We consider the problem of identifying the best arm in a multi-armed bandit model. Despite a wealth of literature in the traditional fixed budget and fixed confidence regimes of the best arm identification problem, it still remains a mystery to most practitioners…

  • Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment

    Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment arXiv:2503.21878v1 Announce Type: cross Abstract: Inference-time computation provides an important axis for scaling language model performance, but naively scaling compute through techniques like Best-of-$N$ sampling can cause performance to degrade due to reward hacking. Toward a theoretical understanding of how to best…

  • The Best Way to Prepare for Data Science and Machine Learning Interviews

    The Best Way to Prepare for Data Science and Machine Learning Interviews Never get stumped again Continue reading on Towards Data Science » Marina Wyss – Gratitude Driven Go to original source

  • recommend me the best statistics textbook for data science

    recommend me the best statistics textbook for data science I am intermediate level student who already studied stats , But i want to revisit it from DS and ML perspective submitted by /u/Emotional-Rhubarb725 [link] [comments] /u/Emotional-Rhubarb725 Go to original source