Stories with Tag Unsupervised Learning

• Understanding Machine Learning: From Theory to Algorithms

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  Posted: 2025-04-04 18:25:23

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  "Understanding Machine Learning: From Theory to Algorithms" provides a comprehensive overview of machine learning, bridging the gap between theoretical principles and practical applications. The book covers a wide range of topics, from basic concepts like supervised and unsupervised learning to advanced techniques like Support Vector Machines, boosting, and dimensionality reduction. It emphasizes the theoretical foundations, including statistical learning theory and PAC learning, to provide a deep understanding of why and when different algorithms work. Practical aspects are also addressed through the presentation of efficient algorithms and their implementation considerations. The book aims to equip readers with the necessary tools to both analyze existing learning algorithms and design new ones.

  "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David offers a comprehensive exploration of the fascinating field of machine learning, bridging the gap between theoretical foundations and practical algorithmic implementations. The book meticulously constructs a conceptual framework for understanding how machines learn from data, starting with fundamental concepts like the Probably Approximately Correct (PAC) learning model. This model provides a rigorous mathematical framework for analyzing the ability of learning algorithms to generalize from a limited set of training examples to unseen data, taking into account factors such as sample complexity, error rates, and computational efficiency.

  The authors delve into the core tenets of learnability, examining the conditions under which a concept can be effectively learned by a machine. They discuss various hypothesis classes and their representational power, highlighting the trade-off between expressiveness and the risk of overfitting, where a model learns the training data too well and fails to generalize to new instances. The book extensively covers key learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, specific techniques such as linear regression, logistic regression, support vector machines, and decision trees are explored in detail, both in terms of their mathematical underpinnings and practical implementation considerations.

  Unsupervised learning, which involves learning patterns from unlabeled data, is also given considerable attention. Clustering algorithms, dimensionality reduction techniques, and generative models are discussed, providing the reader with a diverse toolkit for extracting knowledge from unstructured data. Furthermore, the book touches upon the exciting field of reinforcement learning, where agents learn to interact with an environment to maximize rewards, introducing fundamental concepts like Markov Decision Processes and various reinforcement learning algorithms.

  A significant portion of the book is dedicated to a rigorous treatment of the theoretical foundations of machine learning. Concepts like Rademacher complexity, VC dimension, and stability are introduced and used to derive generalization bounds for different learning algorithms. These theoretical tools provide valuable insights into the behavior of learning algorithms and help explain why certain algorithms perform better than others in specific scenarios. The authors also address the computational aspects of machine learning, discussing optimization algorithms and their role in training complex models efficiently. They explore techniques such as gradient descent, stochastic gradient descent, and convex optimization, providing a thorough understanding of how these methods are used to find optimal model parameters.

  Beyond the core theoretical and algorithmic concepts, the book also touches upon more advanced topics, including online learning, multi-class classification, structured output prediction, and learning theory in the context of non-i.i.d. data. Throughout the text, the authors maintain a balance between theoretical rigor and practical applicability, providing numerous examples, illustrations, and exercises to help the reader solidify their understanding. This detailed and comprehensive approach makes the book a valuable resource for both students embarking on their machine learning journey and seasoned practitioners seeking to deepen their understanding of the field's theoretical foundations.

  • machine learning
  • algorithms
  • theory
  • supervised learning
  • Unsupervised Learning
  • reinforcement learning
  • statistical learning theory
  • PAC learning
  • VC dimension
  • bias-variance tradeoff
  • Overfitting
  • regularization
  • model selection
  • optimization
  • Gradient Descent
  • linear regression
  • logistic regression
  • support vector machines
  • Decision Trees
  • neural networks
  • deep learning
  • clustering
  • dimensionality reduction
  • Computer Science
  • artificial intelligence
  • Data Science

  Summary of Comments ( 45 )
  https://news.ycombinator.com/item?id=43586073

  Verbose tl;dr

  HN users largely praised Shai Shalev-Shwartz and Shai Ben-David's "Understanding Machine Learning" as a highly accessible and comprehensive introduction to the field. Commenters highlighted the book's clear explanations of fundamental concepts, its rigorous yet approachable mathematical treatment, and the helpful inclusion of exercises. Several pointed out its value for both beginners and those with prior ML experience seeking a deeper theoretical understanding. Some compared it favorably to other popular ML resources, noting its superior balance between theory and practice. A few commenters also shared specific chapters or sections they found particularly insightful, such as the treatment of PAC learning and the VC dimension. There was a brief discussion on the book's coverage (or lack thereof) of certain advanced topics like deep learning, but the overall sentiment remained strongly positive.

  The Hacker News post titled "Understanding Machine Learning: From Theory to Algorithms" linking to Shai Shalev-Shwartz and Shai Ben-David's book has a moderate number of comments, discussing various aspects of the book and machine learning education in general.

  Several commenters praise the book for its clarity and accessibility, especially for those with a stronger mathematical background. One user describes it as the "most digestible theory book," highlighting its helpful explanations of fundamental concepts. Another appreciates the book's focus on proving the theory behind ML algorithms, which they found lacking in other resources. The balance between theory and practical application is also commended, with some users noting how the book helped them bridge the gap between abstract concepts and real-world implementations. Specific chapters on PAC learning and VC dimension are singled out as particularly valuable.

  A recurring theme in the comments is the comparison of this book with other popular machine learning resources. "The Elements of Statistical Learning" is frequently mentioned as a more statistically-focused alternative, often considered more challenging. Some users suggest using both books in conjunction, leveraging Shalev-Shwartz and Ben-David's book as a starting point before tackling the more advanced "Elements of Statistical Learning." Another comparison is made with the "Hands-On Machine Learning" book, which is characterized as more practically oriented.

  Some commenters discuss the role of mathematical prerequisites in understanding machine learning. While the book is generally praised for its clarity, a few users acknowledge that a solid foundation in linear algebra, probability, and calculus is still necessary to fully grasp the material. One comment even suggests specific resources to brush up on these mathematical concepts before diving into the book.

  Beyond the book itself, the discussion touches upon broader topics in machine learning education. The importance of understanding the theoretical underpinnings of algorithms is emphasized, with several comments cautioning against relying solely on practical implementations without a deeper understanding of the underlying principles. The evolving nature of the field is also acknowledged, with some users mentioning more recent advancements that aren't covered in the book. Finally, there's a brief discussion about the role of online courses versus traditional textbooks in learning machine learning, with varying opinions on their respective merits.

• Stop using the elbow criterion for k-means

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  Posted: 2025-03-23 02:51:38

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  The paper "Stop using the elbow criterion for k-means" argues against the common practice of using the elbow method to determine the optimal number of clusters (k) in k-means clustering. The authors demonstrate that the elbow method is unreliable, often identifying spurious elbows or missing genuine ones. They show this through theoretical analysis and empirical examples across various datasets and distance metrics, revealing how the within-cluster sum of squares (WCSS) curve, on which the elbow method relies, can behave unexpectedly. The paper advocates for abandoning the elbow method entirely in favor of more robust and theoretically grounded alternatives like the gap statistic, silhouette analysis, or information criteria, which offer statistically sound approaches to k selection.

  The arXiv preprint "Stop using the elbow criterion for k-means" argues vehemently against the common practice of employing the elbow method for determining the optimal number of clusters (k) in k-means clustering. The authors meticulously demonstrate that the elbow method, which relies on identifying a "kink" or "elbow" in the plot of within-cluster sum of squares (WCSS) against the number of clusters, is fundamentally flawed and often leads to inaccurate and misleading results. They highlight the subjective nature of visually identifying this "elbow," making the method prone to interpreter bias and lacking reproducibility. Different observers might identify different optimal k values based on the same WCSS plot, rendering the method unreliable for scientific rigor.

  The paper underscores that the WCSS metric inherently decreases monotonically with increasing k. This means that adding more clusters will always reduce the WCSS, albeit at a diminishing rate. The elbow, representing the point of diminishing returns, is thus not a definitive indicator of an inherently optimal clustering structure within the data but rather a natural consequence of the algorithm's behavior. Furthermore, the paper illustrates how the elbow, even if discernible, can occur at an incorrect k, particularly in datasets exhibiting complex cluster shapes or varying cluster densities. The authors provide numerous simulated and real-world examples where the elbow method fails to identify the true number of clusters, sometimes dramatically overestimating or underestimating the optimal k.

  As a compelling alternative to the elbow method, the authors advocate for the use of gap statistics. The gap statistic compares the within-cluster dispersion of the observed data to the expected dispersion under a null reference distribution representing a dataset with no discernible clustering structure. By calculating the gap statistic for different k values and identifying the k for which the gap is maximized, one obtains a more statistically principled and robust estimate of the optimal cluster number. This approach avoids the subjective interpretation inherent in the elbow method and provides a quantifiable measure for comparing different clustering solutions. The authors emphasize that the gap statistic, while computationally more intensive than the elbow method, offers a significantly more reliable and objective way to determine k, leading to more accurate and insightful clustering results. They conclude by strongly recommending abandoning the elbow method in favor of more robust alternatives like the gap statistic, promoting a more rigorous and statistically sound approach to k-means clustering analysis.

  • k-means
  • clustering
  • elbow method
  • gap statistic
  • silhouette analysis
  • machine learning
  • Unsupervised Learning
  • statistical analysis
  • Data Science
  • Algorithm
  • optimization
  • cluster analysis
  • dimensional reduction

  Summary of Comments ( 13 )
  https://news.ycombinator.com/item?id=43450550

  Verbose tl;dr

  HN users discuss the problems with the elbow method for determining the optimal number of clusters in k-means, agreeing it's often unreliable and subjective. Several commenters suggest superior alternatives, such as the silhouette coefficient, gap statistic, and information criteria like AIC/BIC. Some highlight the importance of considering the practical context and the "business need" when choosing the number of clusters, rather than relying solely on statistical methods. Others point out that k-means itself may not be the best clustering algorithm for all datasets, recommending DBSCAN and hierarchical clustering as potentially better suited for certain situations, particularly those with non-spherical clusters. A few users mention the difficulty in visualizing high-dimensional data and interpreting the results of these metrics, emphasizing the iterative nature of cluster analysis.

  The Hacker News post titled "Stop using the elbow criterion for k-means" (https://news.ycombinator.com/item?id=43450550) discusses the linked arXiv paper which argues against using the elbow method for determining the optimal number of clusters in k-means clustering. The comments section is relatively active, featuring a variety of perspectives on the topic.

  Several commenters agree with the premise of the article. They point out that the elbow method is often subjective and unreliable, leading to arbitrary choices for the number of clusters. Some users share anecdotal experiences of the elbow method failing to produce meaningful results or being difficult to interpret. One commenter suggests the gap statistic as a more robust alternative.

  A recurring theme in the comments is the inherent difficulty of choosing the "right" number of clusters, especially in high-dimensional spaces. Some users argue that the optimal number of clusters is often dependent on the specific application and downstream analysis, rather than being an intrinsic property of the data. They suggest that domain knowledge and interpretability should play a significant role in the decision-making process.

  One commenter points out that the elbow method is particularly problematic when the clusters are not well-separated or when the data has a complex underlying structure. They suggest using visualization techniques, like dimensionality reduction, to gain a better understanding of the data before attempting to cluster it.

  Another comment thread discusses the limitations of k-means clustering itself, regardless of the method used to choose k. Users highlight the algorithm's sensitivity to initial conditions and its assumption of spherical clusters. They propose alternative clustering methods, such as DBSCAN and hierarchical clustering, which may be more suitable for certain types of data.

  A few commenters defend the elbow method, arguing that it can be a useful starting point for exploratory data analysis. They acknowledge its limitations but suggest that it can provide a rough estimate of the number of clusters, which can be refined using other techniques.

  Finally, some commenters discuss the practical implications of choosing the wrong number of clusters. They highlight the potential for misleading results and incorrect conclusions, emphasizing the importance of careful consideration and validation. One commenter suggests using metrics like silhouette score or Calinski-Harabasz index to assess the quality of the clustering.

  Overall, the comments section reflects a general consensus that the elbow method is not a reliable technique for determining the optimal number of clusters in k-means. Commenters offer various alternative approaches, emphasize the importance of domain knowledge and data visualization, and discuss the broader challenges of clustering high-dimensional data.

• LLMs can teach themselves to better predict the future

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  Posted: 2025-02-11 16:40:20

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  Large language models (LLMs) can improve their future prediction abilities through self-improvement loops involving world modeling and action planning. Researchers demonstrated this by tasking LLMs with predicting future states in a simulated text-based environment. The LLMs initially used their internal knowledge, then refined their predictions by taking actions, observing the outcomes, and updating their world models based on these experiences. This iterative process allows the models to learn the dynamics of the environment and significantly improve the accuracy of their future predictions, exceeding the performance of supervised learning methods trained on environment logs. This research highlights the potential of LLMs to learn complex systems and make accurate predictions through active interaction and adaptation, even with limited initial knowledge of the environment.

  This research paper, titled "LLMs can teach themselves to better predict the future," delves into the fascinating realm of enhancing Large Language Models' (LLMs) predictive capabilities through self-improvement methodologies. Specifically, the authors explore how LLMs can be trained to generate future segments of a given sequence, essentially learning to anticipate what comes next. This predictive capacity is evaluated using a diverse range of sequential data, encompassing areas such as text, mathematical calculations, and even simulated physical phenomena.

  The core innovation presented is a novel training procedure wherein the LLM isn't simply trained to passively predict the immediate future based on existing data. Instead, it's actively encouraged to generate multiple potential future continuations of a sequence. These generated continuations are then evaluated based on their consistency and coherence with the established patterns within the original sequence. This evaluation process effectively allows the model to learn from its own predictions, refining its understanding of the underlying generative process governing the sequence. Furthermore, the model is trained to recognize and prioritize the most plausible future trajectories among the generated options, thus improving its ability to select the most likely outcome.

  The paper meticulously details the architecture and training process of these self-improving LLMs, elaborating on how the feedback loop from generated continuations strengthens the model's predictive accuracy. It also presents a comparative analysis of this novel approach against traditional sequence prediction methods, demonstrating significant performance gains achieved through self-improvement. The results highlight the potential of this technique to enhance LLMs' understanding of complex sequential data and their ability to extrapolate future events.

  The authors further investigate the impact of various factors, such as the number of generated continuations and the evaluation metrics employed, on the overall performance of the self-improvement process. This in-depth analysis provides valuable insights into the dynamics of LLM self-learning and offers guidance for optimizing the training procedure. The research concludes by emphasizing the broader implications of this work for advancing the field of sequential data analysis and unlocking the full potential of LLMs in predictive modeling across diverse domains. The potential applications extend beyond simple sequence prediction to encompass more complex tasks like strategic planning, scenario generation, and even creative content generation, where anticipating future developments is crucial.

  • Large Language Models
  • LLMs
  • Predictive Models
  • forecasting
  • Time Series Analysis
  • machine learning
  • artificial intelligence
  • AI
  • Self-Learning
  • Unsupervised Learning
  • arXiv
  • Preprint
  • Future Prediction
  • Temporal Modeling

  Summary of Comments ( 60 )
  https://news.ycombinator.com/item?id=43014918

  Verbose tl;dr

  Hacker News users discuss the implications of LLMs learning to predict the future by self-improving their world models. Some express skepticism, questioning whether "predicting the future" is an accurate framing, arguing it's more akin to sophisticated pattern matching within a limited context. Others find the research promising, highlighting the potential for LLMs to reason and plan more effectively. There's concern about the potential for these models to develop undesirable biases or become overly reliant on simulated data. The ethics of allowing LLMs to interact and potentially manipulate real-world systems are also raised. Several commenters debate the meaning of intelligence and consciousness in the context of these advancements, with some suggesting this work represents a significant step toward more general AI. A few users delve into technical details, discussing the specific methods used in the research and potential limitations.

  The Hacker News post titled "LLMs can teach themselves to better predict the future" (linking to an arXiv preprint about Large Language Models improving world model prediction through self-play) sparked a moderate discussion with a handful of comments focusing primarily on the limitations and specific nature of the improvement demonstrated.

  One commenter pointed out that the "future prediction" being discussed is highly specific to the simulated environments used in the research, not general real-world prediction. They emphasized that the LLMs are learning to predict game states in simplified environments, not complex real-world events. This commenter cautioned against misinterpreting the title's broad implications.

  Another commenter elaborated on this limitation by specifying that the LLMs were improving their predictive ability within the confines of the game rules. The learned predictions are essentially extrapolations within a closed system defined by pre-programmed rules, not open-ended real-world scenarios. This reinforces the idea that the LLMs aren't developing a general ability to "predict the future" in a commonly understood sense.

  A further comment questioned the novelty of the approach, suggesting that using simulations to train AI models is a well-established technique and that the research primarily showcases a specific application of this technique to LLMs rather than a fundamentally new approach. This commenter also mentioned the potential relevance of this research to reinforcement learning.

  One commenter expressed skepticism towards the idea of "self-play" as framed in the research, arguing that the LLM isn't truly playing against itself, but rather interacting with a model of itself. They suggest the term "self-play" is a misnomer, potentially overselling the level of agency involved.

  While several commenters acknowledge the interesting aspects of the research, the overall tone leans towards cautious interpretation. The main thread running through the comments is a clarification that the "future prediction" discussed is restricted to specific simulated game environments and shouldn't be extrapolated to broader real-world prediction capabilities. There isn't a strong sense of excitement or groundbreaking discovery in the comments, but rather a measured analysis of the research's scope and limitations.