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

All paths point downhill

218 implied HN points • 31 Oct 24
🕹 Technology Algorithms Optimization Data science Machine Learning Mathematics
  1. In optimization, there are three main approaches: local search, global optimization, and a method that combines both. They all aim to find the best solution to minimize a function.
  2. Gradient descent is a popular method in optimization that works like local search, by following the path of steepest descent to improve the solution. It can also be viewed as a way to solve equations or approximate values.
  3. Newton's method, another optimization technique, is efficient because it converges quickly but requires more computation. Like gradient descent, it can be interpreted in various ways, emphasizing the interconnectedness of optimization strategies.

The Higgs Discovery Did Not Take Place

1071 implied HN points • 22 Oct 24
🔬 Science Physics Statistics Quantum Mechanics Scientific Method
  1. The Higgs boson was theoretically discovered, but many people argue that this claim isn't solid due to complex statistical methods used in the research. It's not just about finding a particle; it's heavily based on probabilities.
  2. A lot of the processes in particle physics rely on trust within scientific communities and committees. They decide what counts as 'discovery' often through agreed conventions rather than direct proof.
  3. Questions about the Higgs boson reflect broader concerns in science regarding accountability. It shows that scientific findings often come down to people, their processes, and their decisions rather than just raw data.

Basic Linear Algebra Subprogramming

178 implied HN points • 29 Oct 24
🕹 Technology Computing Mathematics Optimization Data science Algorithms
  1. Understanding how optimization solvers work can save time and improve efficiency. Knowing a bit about the tools helps you avoid mistakes and make smarter choices.
  2. Nonlinear equations are harder to solve than linear ones, and methods like Newton's help us get approximate solutions. Iteratively solving these systems is key to finding optimal results in optimization problems.
  3. The speed and efficiency of solving linear systems can greatly affect computational performance. Organizing your model in a smart way can lead to significant time savings during optimization.

Toward a Transformative Hermeneutics of Standard Model Physics

456 implied HN points • 25 Oct 24
🔬 Science Physics Statistics Philosophy Research Collaboration
  1. The Higgs discovery shows how science relies on consensus rather than just statistics. It's all about how many scientists agree on something, and that's what really gives it weight.
  2. Complex governance structures are necessary in big science projects. These systems help teams work together and make important decisions about groundbreaking discoveries.
  3. Sometimes, playful writing can lead to misunderstandings. It's important to find the right balance between being engaging and being precise when discussing complex topics.

A use-theory of testing

436 implied HN points • 24 Oct 24
🔬 Science Statistics Research Mathematics Data Analysis Theory
  1. Statistical tests are designed to help separate real signals from random noise. It's not just about understanding what they mean, but what they can do in practical situations.
  2. Many people misuse statistical tests, which can lead to misunderstandings about their purpose. Communities should establish clear guidelines on how to use these tests correctly.
  3. The main function of statistical tests is to regulate opinions and decisions in various fields like tech and medicine. They help ensure that important standards are met, rather than just preventing errors.
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What Is Statistics' Purpose?

734 implied HN points • 14 Oct 24
🔬 Science Statistics Research Medicine Philosophy Data Analysis
  1. Statistics should help us test claims by measuring how surprising the results are. However, there's doubt about whether our current statistical tests actually do this well.
  2. Randomized trials are important because they help us learn about treatments that may not always work. They focus on safety as much as they do on finding effective solutions.
  3. The field of statistics needs to be clear about its purpose. We should distinguish between using statistics for proving theories and for practical decision-making like quality control.

The Shape of Stats to Come

634 implied HN points • 10 Oct 24
🚌 Education Statistics Optimization Philosophy Data Analysis Mathematics
  1. Statistics often involves optimizing methods to get the best results. Many statistical techniques can actually be viewed as optimization problems.
  2. Choosing a statistical method isn't just about the math—it's also based on beliefs about reality. This philosophical side is important but often overlooked.
  3. There's a danger in relying too much on tools and models we can solve. Sometimes, we force the data to fit our preferred methods instead of being open to the actual complexities.

Designed Interactions

257 implied HN points • 15 Oct 24
🚌 Education Statistics Optimization Mathematics Machine Learning
  1. Experiment design is about choosing the right measurements to get useful data while reducing errors. It's important in various fields, including medical imaging and randomized trials.
  2. Statistics play a big role in how we analyze and improve measurement processes. They help us understand the noise in our data and guide us in making our experiments more reliable.
  3. Optimization is all about finding the best way to minimize errors in our designs. It's a practical approach rather than just seeking perfection, and we need to accept that some questions might remain unanswered.

Convex Optimization at the Midpoint

198 implied HN points • 17 Oct 24
🚌 Education Optimization Programming Statistics Algorithms
  1. Modeling is really important in optimization classes. It's better to teach students how to set up real problems instead of just focusing on abstract theories.
  2. Introducing programming assignments earlier can help students understand optimization better. Using tools like cvxpy can make solving problems easier without needing to know all the underlying algorithms.
  3. Convex optimization is heavily used in statistics, but there's not much focus on control systems. Adding a section on control applications could help connect optimization with current interests in machine learning.

Inverse Problems

515 implied HN points • 03 Oct 24
🔬 Science Mathematics Statistics Physics Engineering Computer Science
  1. Inverse problems help us create images or models from measurements, like how a CT scan builds a picture of our insides using X-rays.
  2. A key part of working with inverse problems is using linear models, which means we can express our measurements and the related image or signal in straightforward mathematical terms.
  3. Choosing the right functions to handle noise and image characteristics is crucial because it guides how the algorithm makes sense of the data we collect.

Interpolation Is All You Need

317 implied HN points • 08 Oct 24
🕹 Technology AI Optimization Machine Learning Data science
  1. Interpolation is a process where we find a function that fits a specific set of input and output points. It's a useful tool for solving problems in optimization.
  2. We can build more complex function fitting problems by combining simple interpolation constraints. This allows for greater flexibility in how we define functions.
  3. Duality in convex optimization helps solve interpolation problems, enabling efficient computation and application in areas like machine learning and control theory.

An Inversion Cookbook

297 implied HN points • 04 Oct 24
🚌 Education Mathematics Statistics Optimization Regression Data science
  1. Using modularity, we can tackle many inverse problems by turning them into convex optimization problems. This helps us use simple building blocks to solve complex issues.
  2. Linear models can be a good approximation for many situations, and if we rely on them, we can find clear solutions to our inverse problems. However, we should be aware that they don't always represent reality perfectly.
  3. Different regression techniques, like ordinary least squares and LASSO, allow us to handle noise and sparse data effectively. Tuning the right parameters can help us balance accuracy and manageability in our models.

Inverse frontiers

158 implied HN points • 07 Oct 24
🕹 Technology Optimization Machine Learning Algorithms Data science Artificial Intelligence
  1. Convex optimization has benefits, like collecting various modeling tools and always finding a reliable solution. However, not every problem fits neatly into a convex framework.
  2. Some complex problems, like dictionary learning and nonlinear models, often require nonconvex optimization, which can be tricky to handle but might be necessary for accurate results.
  3. Using machine learning methods can help solve inverse problems because they can learn the mapping from measurements to states, making it easier to compute solutions later, though training the model initially can take a lot of time.

Too Much Information

1845 implied HN points • 19 Dec 23
  1. Academic productivity in writing papers has increased dramatically.
  2. Concerns about the quality versus quantity of research papers.
  3. Advice to young scholars to focus on a few passionate projects rather than numerous distractions.

Is the reproducibility crisis reproducible?

694 implied HN points • 08 Jan 24
  1. The study on reproducibility crisis had methodological flaws, starting from the data collection stage.
  2. The conclusions drawn were based on data derived from another project and not directly from reported p-values in RCTs.
  3. The proposed fix for experiments in clinical medicine involves focusing on understanding trial reports and biases, rather than solely relying on p-values.

When Past Performance Is Indicative Of Future Returns

436 implied HN points • 17 Jan 24
  1. Statistics relies on the belief that past rates of events will be similar to future rates.
  2. External validity in statistics involves factors like physical settings, treatments, outcomes, units, time, and mechanisms.
  3. Predicting the transferability of statistical results is complex and requires strong conceptual models.

Unfollow The Science

416 implied HN points • 15 Jan 24
  1. Scientists have always lamented the state of scientific communication.
  2. There is a need for more rigor in scientific communication and publication.
  3. Transparency in scientific communication is crucial to maintaining public trust.

Arbitrage in data exchange rates

416 implied HN points • 10 Jan 24
  1. Don't be too quick to simplify what we learn from data.
  2. Large datasets can lead to less understanding about the individual data points.
  3. Understanding the limits of statistical predictions is crucial in a data-driven world.

Algorithmic Impacts of Jocks versus Nerds

396 implied HN points • 18 Jan 24
  1. JJ Watt criticizes the use of statistics in sports rankings
  2. Advanced statistics in sports analytics can have real-world impacts on player compensation
  3. Players are concerned about the negative consequences of media relying heavily on fan-created stats

More Like One Out of A Million...

277 implied HN points • 26 Jan 24
  1. We should be unsatisfied with unverifiable measurement
  2. Wider intervals are less likely to be wrong
  3. There's a need for measurement procedures that guarantee accuracy within bounds

Guess and check

257 implied HN points • 05 Feb 24
  1. Statistical intervals, like confidence and prediction intervals, help us understand the precision and prediction of measurements.
  2. Prediction intervals promise to contain future measurements with specified probability, allowing for verification and correction of predictions.
  3. Prediction intervals have practical applications in decision-making, enabling us to act based on predictions to achieve desired outcomes and intervene to change outcomes.

Patterns, Predictions, and AGI

575 implied HN points • 24 Aug 23
  1. Claude Shannon's work laid the foundations for modern digital communication and natural language processing.
  2. Predictability is key in language modeling, with redundancy in English text making it around 85% predictable.
  3. Combining Shannon's language models with Rosenblatt's perceptron led to the modern development of artificial general intelligence.

When is the future the same as the past?

238 implied HN points • 06 Feb 24
  1. In prediction, we convert past counts into future probabilities.
  2. To do this, we assume events are independent and identically distributed, or exchangeable.
  3. Exchangeability gives us flexibility in reasoning about the chances of outcomes in a sequence.

Cool Kids Keep

337 implied HN points • 27 Nov 23
  1. Reinforcement learning is viewed differently by RL Maximalists and RL Minimalists.
  2. RL Maximalists see reinforcement learning as encompassing all decision making under uncertainty.
  3. RL Minimalists focus on solving short-horizon policy optimization problems through random trials.

Should you go for 2 down 8?

238 implied HN points • 23 Jan 24
  1. The probability calculations behind going for a 2 down 8 conversion in football may not be as straightforward as they seem.
  2. Realistic calculations show that the strategy of going for a 2 down 8 may actually only slightly increase the chances of winning, potentially by less than 1%.
  3. Psychological and physical factors, as well as nonprobabilistic elements, play a significant role in the outcomes of football games.

A semester of milliblogging

297 implied HN points • 06 Dec 23
  1. Live blogging a class was a rewarding pedagogical challenge.
  2. Committing to blogging requires more thought and effort than social media posting.
  3. Writing and live blogging helped in better understanding and communicating complex concepts.

Cover Songs

198 implied HN points • 09 Feb 24
  1. Conformal prediction relies on the unverifiable assumption that past data is exchangeable with the future.
  2. Conformal prediction can provide coverage guarantees regardless of the quality of the prediction function.
  3. Conformal prediction may not provide the type of coverage needed for practical decision-making, as it lacks conditional coverage.

Randomly Revealing Hidden Truths

218 implied HN points • 25 Jan 24
  1. Confidence intervals in statistics aim to give a range that likely contains the true parameter with a certain probability.
  2. Misinterpretations of confidence intervals are common, leading to false beliefs about what the intervals indicate.
  3. Consider adopting '5-9 confidence intervals' with a higher confidence level to reduce the chance of errors in statistical estimates.

The war of symbolic aggression

456 implied HN points • 01 Sep 23
  1. AI has faced skepticism and challenges in machine learning development
  2. Machine learning is useful for problems where a clear classification rule cannot be articulated in computer code
  3. Using machine learning can lead to a perfect classification rule, but not always

All published results are wrong, but some are useful

337 implied HN points • 02 Nov 23
  1. Published results are often wrong, but can still be useful.
  2. Observational studies in science are frequently flawed.
  3. Greater acceptance and appreciation of engineering may offer solutions in scientific research.

I Was Thinking I Could Read Up for Christmas

257 implied HN points • 21 Dec 23
  1. The author shared a list of books they plan to read over the winter break, including a sci-fi novel about superintelligent cephalopods, a post-apocalyptic novel about robots forming a commune, and a philosophy book about engineering approximations.
  2. The author mentioned taking a break from blogging during vacation but plans to reflect on ongoing conversations in the new year.
  3. The author acknowledged the importance of reading and how it helps them focus on what they want to think about.

Rigor vs. GitHub Descent

416 implied HN points • 06 Sep 23
  1. Optimization in machine learning involves reinventing variants of the same algorithms.
  2. In machine learning, the focus is on models well-fit to training data for competitions.
  3. Having 'principled methods' may not be essential in the practical application of machine learning.

A Casino Where You Never Lose

198 implied HN points • 24 Jan 24
  1. Monte Carlo Algorithms use randomness to save time by inferring properties from samples.
  2. Monte Carlo Algorithms come with a guarantee of correctness with a certain probability.
  3. Statistical intervals are like Monte Carlo algorithms with guarantees but lack efficient validation methods.

The Birth of the Utility Monster

238 implied HN points • 15 Dec 23
  1. Frank Ramsey pioneered rational utility maximization
  2. People are predictably and unpredictably irrational
  3. Ramsey's model led to a gambling-centric view of existence

The Rational Landscapes

238 implied HN points • 12 Dec 23
  1. Rational decision making involves reasoning about uncertainty and objectives.
  2. Modeling uncertainty involves being able to compare the plausibility of different statements.
  3. There is ongoing debate in the field about pragmatic versus universal approaches to decision making and uncertainty.

From coin tosses to option pricing

158 implied HN points • 07 Feb 24
  1. Prediction intervals are sets where we think future events will most likely happen.
  2. Confidence intervals are helpful for random events with a range of potential values.
  3. DKW theorem provides a way to generate prediction intervals for any sequence of random variables.

Apostasy or reformation

317 implied HN points • 26 Sep 23
  1. Good prediction balances bias and variance.
  2. High-capacity models don't always generalize well.
  3. Test error is not always a reliable indicator in machine learning.

Yogi Berra in everything

218 implied HN points • 05 Dec 23
  1. Decision making under uncertainty often involves using probabilistic models to find optimal policies.
  2. The complexity of decision making depends on the level of detail and sophistication in the probabilistic models.
  3. There is ongoing confusion and debate about the interpretation and reliability of probabilistic models in predicting future outcomes.

No one knows how the brain works.

297 implied HN points • 21 Sep 23
  1. No one knows exactly how the brain works.
  2. Optimizing neural networks can be easier than previously believed.
  3. Structuring neural networks after signal processing systems can lead to successful results.

Fact, Fiction, and Forecast

317 implied HN points • 25 Aug 23
  1. The course focuses on patterns, predictions, and actions in machine learning.
  2. The teaching methods in machine learning today have roots dating back to the 1960s.
  3. Understanding the history of machine learning can provide valuable insights for the future.