Sports Analytics Weekly by kubeia.io - 45/2023

The Upshot’s N.F.L. Playoff Simulator is churning out options for the 10th straight year.

Passed every frame of a football video to gpt-4-vision-preview, and with some simple prompting asked to generate a narration

YOLO-NAS Pose models is the latest contribution to the field of Pose Estimation. Earlier this year, Deci garnered widespread recognition for its groundbreaking object detection foundation model, YOLO-NAS. Building upon the success of YOLO-NAS, the company has now unveiled YOLO-NAS Pose as its Pose Estimation counterpart. This Pose model offers an excellent balance between latency and accuracy.Pose Estimation plays a crucial role in computer vision, encompassing a wide range of important applications. These applications include monitoring patient movements in healthcare, analyzing the performance of athletes in sports, creating seamless human-computer interfaces, and improving robotic systems.

The video discusses the evolution of trading over the years, focusing on how changing market regimes have impacted simple trading strategies. In the 1970s and 1980s, traders used fast-moving averages to achieve absolute alpha. However, by the mid-1980s, these strategies stopped working, leading to higher drawdowns and the disappearance of alpha. Long-only strategies have underperformed due to quantitative easing, and market regimes have become more mean-reverting. Today, the only possible alpha comes from leverage.

Developing good predictive models hinges upon accurate performance evaluation and comparisons. However, when evaluating machine learning models, we typically have to work around many constraints, including limited data, independence violations, and sampling biases. Confidence intervals are no silver bullet, but at the very least, they can offer an additional glimpse into the uncertainty of the reported accuracy and performance of a model.

To get a crude answer to this question, we took 5000 questions from Manifold markets that were resolved after GPT-4’s current knowledge cutoff of Jan 1, 2022.

Abstract:Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if non-negligible dependencies exist between targets. To overcome this limitation, we present an extension of XGBoostLSS that models multiple targets and their dependencies in a probabilistic regression setting. Empirical results show that our approach outperforms existing GBMs with respect to runtime and compares well in terms of accuracy.

In this work, we leverage pre-trained Large Language Models (LLMs) to enhance time-series forecasting. Mirroring the growing interest in unifying models for Natural Language Processing and Computer Vision, we envision creating an analogous model for long-term time-series forecasting. Due to limited large-scale time-series data for building robust foundation models, our approach LLM4TS focuses on leveraging the strengths of pre-trained LLMs. By combining time-series patching with temporal encoding, we have enhanced the capability of LLMs to handle time-series data effectively. Inspired by the supervised fine-tuning in chatbot domains, we prioritize a two-stage fine-tuning process: first conducting supervised fine-tuning to orient the LLM towards time-series data, followed by task-specific downstream fine-tuning. Furthermore, to unlock the flexibility of pre-trained LLMs without extensive parameter adjustments, we adopt several Parameter-Efficient Fine-Tuning (PEFT) techniques. Drawing on these innovations, LLM4TS has yielded state-of-the-art results in long-term forecasting. Our model has also shown exceptional capabilities as both a robust representation learner and an effective few-shot learner, thanks to the knowledge transferred from the pre-trained LLM.

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains have demonstrated that with the availability of massive amounts of time series, ML and DL techniques are competitive in time series forecasting. Nevertheless, the different forms of non-stationarities associated with time series challenge the capabilities of data-driven ML models. Furthermore, due to the domain of forecasting being fostered mainly by statisticians and econometricians over the years, the concepts related to forecast evaluation are not the mainstream knowledge among ML researchers. We demonstrate in our work that as a consequence, ML researchers oftentimes adopt flawed evaluation practices which results in spurious conclusions suggesting methods that are not competitive in reality to be seemingly competitive. Therefore, in this work we provide a tutorial-like compilation of the details associated with forecast evaluation. This way, we intend to impart the information associated with forecast evaluation to fit the context of ML, as means of bridging the knowledge gap between traditional methods of forecasting and adopting current state-of-the-art ML techniques.We elaborate the details of the different problematic characteristics of time series such as non-normality and non-stationarities and how they are associated with common pitfalls in forecast evaluation. Best practices in forecast evaluation are outlined with respect to the different steps such as data partitioning, error calculation, statistical testing, and others. Further guidelines are also provided along selecting valid and suitable error measures depending on the specific characteristics of the dataset at hand.

Whether you're doing machine learning, scientific computing, or working with huge datasets, CuPy is an absolute game-changer.

As we pen this article, the NVIDIA H100 80GB PCIe is $32K at online retailers like CDW and is back-ordered for roughly six months. Understandably, the price of NVIDIA’s top-end do (almost) everything GPU is extremely high, as is the demand. NVIDIA came out with an alternative for many AI users and those running mixed workloads in the enterprise that is flying under the radar, but that is very good. The NVIDIA L40S is a variant of the graphics-oriented L40 that is quickly becoming the best-kept secret in AI. Let us dive in and understand why.

Abstract:Standard Markov chain Monte Carlo (MCMC) admits three fundamental control parameters: the number of chains, the length of the warmup phase, and the length of the sampling phase. These control parameters play a large role in determining the amount of computation we deploy. In practice, we need to walk a line between achieving sufficient precision and not wasting precious computational resources and time. We review general strategies to check the length of the warmup and sampling phases, and examine the three control parameters of MCMC in the contexts of CPU- and GPU-based hardware. Our discussion centers around three tasks: (1) inference about a latent variable, (2) computation of expectation values and quantiles, and (3) diagnostics to check the reliability of the estimators. This chapter begins with general recommendations on the control parameters of MCMC, which have been battle-tested over the years and often motivate defaults in Bayesian statistical software. Usually we do not know ahead of time how a sampler will interact with a target distribution, and so the choice of MCMC algorithm and its control parameters, tend to be based on experience, re-evaluated after simulations have been obtained and analyzed. The second part of this chapter provides a theoretical motivation for our recommended approach, with pointers to some concerns and open problems. We also examine recent developments on the algorithmic and hardware fronts, which motivate new computational approaches to MCMC.