arg min

The 'arg min' Substack explores the intricacies of machine learning, statistical methods, and the impact of technology on decision making. It delves into the history, challenges, and philosophical questions surrounding artificial intelligence, emphasizing the importance of optimization, data analysis, and the balance between theory and application in scientific advancements.

Machine Learning Statistical Methods Artificial Intelligence Data Analysis Scientific Communication Optimization Techniques History of Technology Philosophy of Science

The hottest Substack posts of arg min

And their main takeaways

The Robot of Pure Reason

178 implied HN points • 13 Dec 23
  1. Jaynes developed a metaphorical robot as the ideal processor of information based on Bayesian logic rules
  2. Jaynes aimed to design an AI that reasons in a way recognizable to humans but without illogical aspects
  3. The concept of a rational, nonideological being of pure reason evolved from Descartes' vision of a mathematical universe

To do the mindblowing

178 implied HN points • 04 Dec 23
  1. Well-defined prediction goals are critical for machine learning tasks.
  2. Having better data sets leads to better predictions in machine learning.
  3. The train-test split method is essential for establishing the internal validity of prediction tasks.

We are more than players

277 implied HN points • 28 Aug 23
  1. The course focuses on taking actions based on data and patterns.
  2. The central question is how to find policies that maximize desirable outcomes in uncertain systems.
  3. The class explores topics like binary decision-making, optimal control, causal inference, and reinforcement learning.

Regretfully Yours

257 implied HN points • 07 Sep 23
  1. Understanding optimization convergence is essential in machine learning.
  2. The Nemirovskii and Yudin 1983 analysis is fundamental for proving convergence in algorithms like Stochastic Gradient Descent.
  3. The concept of 'regret' in machine learning measures the average gap between sequential predictions and the best predictor.

The Deep Optimization Cookbook

238 implied HN points • 22 Sep 23
  1. Neural net optimization requires various tricks and precautions to ensure convergence.
  2. Optimizing for prediction convergence is crucial in machine learning models.
  3. Overparameterized models can be beneficial and convergence to labels can be achieved with proper optimization techniques.
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The Data Winter

218 implied HN points • 09 Oct 23
  1. The importance of data for machine learning methods was not obvious in the early days of the field.
  2. In the late 1980s, data-set benchmarking and competitive testing became more prevalent in machine learning.
  3. The demand for quantitative metrics, accessible data transfer methods, and improvements in computing technology contributed to the evolution of machine learning.

Revisiting Highleyman's Data

218 implied HN points • 06 Oct 23
  1. Bill Highleyman made significant contributions to modern machine learning, including data set capture, train-test split, and convolutional pattern recognition architectures.
  2. Highleyman's work highlights the importance of sharing data for competitive testing and innovation in machine learning.
  3. Despite his significant contributions, Bill Highleyman is not as well-known as other prominent figures in machine learning history.

Integral Action

178 implied HN points • 15 Nov 23
  1. Gradient descent is a universal regulator in various fields like machine learning and decision making.
  2. PID control, with only three parameters, effectively regulates systems by adjusting for error, integral, and derivative terms.
  3. Integral control in PID and gradient descent show similar workings, indicating the universal nature of gradient descent.

Can you please everyone?

198 implied HN points • 24 Oct 23
  1. Fair decision-making requires considering protected attributes to avoid discrimination even if it's subtle.
  2. It's impossible to create universally agreed upon rules for fair decision-making due to conflicting fairness metrics.
  3. Maximizing average utility in decision-making may not ensure fairness to individuals, highlighting the limitations of solely technocratic approaches.

The Only Winning Move

158 implied HN points • 01 Dec 23
  1. Game theory teaches decision-making under uncertainty
  2. Computers improved gameplay and provided insights on various games
  3. Game theory allows for participatory decision-making in complex problems

My Mathematical Mind

218 implied HN points • 25 Sep 23
  1. Some argue that you don't need to know theory in machine learning, just large data sets and computing power.
  2. Mathematical theory can be useful in machine learning for optimization and predictive error quantification.
  3. There's a discussion on whether theory in machine learning needs to be mathematical and the importance of history and sociology in understanding competitive testing.

You got a 9 to 5, so I'll take the night shift

198 implied HN points • 10 Oct 23
  1. The effectiveness of the train-test split method in machine learning is under scrutiny.
  2. Evaluating models on test sets multiple times in machine learning may not result in overfitting.
  3. Machine learning models can have high internal validity but lower external validity.

The Department of Frictionless Reproducibilty

198 implied HN points • 05 Oct 23
  1. Frictionless Reproducibility is essential in data science.
  2. The three pillars are sharable data, re-execution, and competitive testing.
  3. Competition and collaboration are crucial for driving progress in machine learning.

I don't know what I've been trying to prove.

218 implied HN points • 13 Sep 23
  1. Generalization theory in machine learning may not always provide practical guidance.
  2. Advising to pick the 'smallest' function space that eliminates empirical error may not be beneficial in practice.
  3. Emphasizing large data sets, model selection based on test set performance, and feature generation could be more effective in machine learning practice.

Generally speaking when god plays dice

218 implied HN points • 11 Sep 23
  1. In machine learning, generalization is tied to probability assumptions like i.i.d. sampling.
  2. The i.i.d. assumption may not reflect real-world data generation scenarios.
  3. Generalization theory aims to ensure machine learning models perform well on unseen data.

Feedback on feedback

158 implied HN points • 17 Nov 23
  1. Feedback is a central concept in control theory, with both power and danger.
  2. Teaching feedback without complex prerequisites like Fourier transforms is challenging.
  3. Feedback systems can outperform open-loop strategies by adapting to unknown variables.

Architectural Theories

158 implied HN points • 14 Nov 23
  1. There is no master algorithm for decision making under uncertainty.
  2. Even simple optimization problems become challenging with uncertainty.
  3. Architectures play a key role in designing complex systems and connecting different components.

7 Minute Opts

218 implied HN points • 05 Sep 23
  1. Optimization in machine learning involves minimizing errors on data through tweaking parameters or knobs.
  2. Gradient descent is a commonly used algorithm in optimization where knobs are adjusted in the direction of the negative gradient of the empirical risk.
  3. Stochastic Gradient Descent is a variation of gradient descent that uses random data points for more efficient updates in large datasets.

Yoshimi Battles the Perceptrons

218 implied HN points • 31 Aug 23
  1. The perceptron update rule involves updating weights based on misclassified examples - it's a simple yet effective process.
  2. If a large margin classifier exists for data, the perceptron will eventually find it by making a finite number of mistakes during training.
  3. The perceptron algorithm provides insights into optimization convergence, prediction regret, and generalization bound, showcasing its robust nature.

Rise of the spreadsheets

218 implied HN points • 29 Aug 23
  1. Machine learning can be compared to building formulas in Microsoft Excel to predict values in a spreadsheet.
  2. In machine learning, the goal is to find patterns in data to make accurate predictions on unseen data.
  3. Successful machine learning involves selecting the best function that maximizes accuracy based on the available data.

Three paths to generalization

198 implied HN points • 12 Sep 23
  1. Path 1 - Uniform Convergence: Probability decreases exponentially with more samples but increases linearly with more functions, making it challenging to pick functions before seeing data.
  2. Path 2 - Sequential Prediction: Sequential prediction bounds can provide a bound on generalization, but proving these bounds can be difficult.
  3. Path 3 - Leave-One-Out Approximations: Leave-one-out error can be used to generate generalization bounds by showing robustness to dropping or resampling data points.

The national academy of spaghetti on the wall

158 implied HN points • 27 Oct 23
  1. Randomized experiments can help improve policies by testing small changes and iterating on them over time.
  2. Conceptually, iterative random testing can lead to optimized solutions in the field of policy optimization.
  3. While swarming randomized experimentation has its advantages, it can also face challenges like getting stuck at local optima and ethical considerations in human-facing sciences.

Shallow depth

178 implied HN points • 28 Sep 23
  1. Conventional wisdom about linear models can be wrong
  2. The bias-variance tradeoff is not a true tradeoff in machine learning
  3. In machine learning, there are no hard and fast rules; prediction success depends on factors like data size and minimizing loss functions

Greetings Professor Falken

138 implied HN points • 16 Nov 23
  1. Claude Shannon proposed solving complex games by planning only a few moves ahead
  2. Dynamic Programming offers a solution to optimization problems, but can be computationally expensive
  3. Model Predictive Control is a practical approach for planning actions based on short time horizons and feedback

Quantifying those unknown unknowns

138 implied HN points • 13 Nov 23
  1. Decision-making under uncertainty is challenging because we aim to select the best action with incomplete information.
  2. Different approaches like stochastic optimization and robust optimization are used to tackle decision-making under uncertainty.
  3. Some algorithms designed for stochastic optimization work well on arbitrary sequences, achieving good results without relying heavily on probabilistic models.

It's not a trick...

178 implied HN points • 20 Sep 23
  1. In machine learning, having the right number of features is crucial for good predictions.
  2. The kernel trick allows for efficient and elegant manipulation of feature representations.
  3. Kernel methods can be powerful for certain applications, but their scalability can be a limitation.

Features of the foundations of machine learning

178 implied HN points • 14 Sep 23
  1. Treat the dataset as the superpopulation for machine learning evaluations.
  2. Train and test splits are important for evaluating models in machine learning.
  3. Choosing models that capture large margin on the training set can lead to better generalization in machine learning.

Policy Phylogenesis

138 implied HN points • 07 Nov 23
  1. Optimization problems can become complex with different variables and constraints.
  2. Policy optimization involves distinctions like model-based vs. model-free and single vs. sequential decisions.
  3. The realm of policy optimization covers a wide range of cases, from binary actions to reinforcement learning.

From Intervals to Bands

79 implied HN points • 08 Feb 24
  1. Prediction bands quantify uncertainty in machine learning models.
  2. By splitting data into training and calibration sets, one can estimate prediction intervals for errors.
  3. Prediction intervals provide probabilistic guarantees for future outcomes, but are limited in their practical applications.

Fractions or the laws of nature?

138 implied HN points • 01 Nov 23
  1. Conditional probability is just a measure of relative fractions.
  2. Fractions don't add up the way we think they should in probability.
  3. Confusing relative proportions with natural laws can lead to making bad decisions.

The closed world of content recommendation

178 implied HN points • 23 Aug 23
  1. The Netflix Prize showed that simple matrix completion outperformed more complex methods in improving recommendation systems.
  2. The winning solution of the Netflix Prize used minimal movie information and focused on subscriber interactions for content recommendations.
  3. Releasing data for open competitions can raise privacy concerns and companies may prioritize corporate power over open data.

Learning with intentional randomness

178 implied HN points • 21 Aug 23
  1. Machine learning can happen without invoking natural probability.
  2. Intentional randomness is used to probe, simulate, and measure in statistical science.
  3. The course focuses on splitting between predictions and actions, exploring decision-making without probability.

A game of chance to you to him is one of real skill

138 implied HN points • 17 Oct 23
  1. Policy optimization in decision-making involves mapping observations to actions to achieve desired outcomes.
  2. In uncertain conditions where cost is related to unknown conditions, finding the best action based on measurements is essential.
  3. Decision-making can be framed as either maximizing expected reward or minimizing expected cost, with implications for stability and aggressiveness.

Basic Training

198 implied HN points • 24 Jul 23
  1. Autobiographical experiences can lead to learning valuable lessons, even in the midst of a midlife crisis.
  2. Progressive overload through full range of motion can improve performance and reduce injury risk, but results may vary.
  3. The blog author emphasizes that their experiences are not unique, and others can learn from them.

Music by machines for people

119 implied HN points • 12 Nov 23
  1. Autechre's music is cold, dark, and inaccessible, with heavy industrial influences.
  2. Autechre's sound evolved to be progressively weirder and more aggressive.
  3. Despite aiming for maximal inaccessibility, Autechre's influence crept into mainstream music.

Incremental progress helps you find what works.

178 implied HN points • 27 Jul 23
  1. Incremental progress is key to discovering effective methods.
  2. Sports science emphasizes the limitations of relying solely on randomized trials.
  3. Physical fitness training allows for quantifiable metrics of proficiency, aiding in measurement and forecasting.

The Impact of Actions

119 implied HN points • 26 Oct 23
  1. Randomized experiments are essential for measuring the impacts of actions.
  2. Randomization in experiments helps remove biases and ensures similarity between treatment and control groups.
  3. Randomized experiments are powerful tools for information gathering in various scientific fields.

Aligning the Aligners

158 implied HN points • 22 Aug 23
  1. AI Alignment research focuses on ensuring AI does not become harmful
  2. There is a contradiction in AI Alignment research between saving the world and pursuing wealth
  3. Alignment research can sometimes prioritize marketability of AI products over true safety concerns

A Strange Game

99 implied HN points • 29 Nov 23
  1. The first application of machine learning was in board games like Checkers.
  2. Gameplay provided an ideal environment to test computer capabilities.
  3. Computer programs for games laid the foundation for broader applications of machine learning.

When you are the treatment and control group

178 implied HN points • 20 Jul 23
  1. N-of-1 trials involve individuals being both the treatment and control group in an experiment
  2. N-of-1 trials are useful for chronic conditions like chronic pain or testing side effects of medications
  3. Limitations of N-of-1 trials include the need for independent trial blocks and large effect sizes