Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

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Despite being vital to machine learning, feature engineering is not always given due attention. Feature engineering is a supporting step in machine learning modeling, but with a smart approach to data selection, it can increase a model’s efficiency and lead to more accurate results. It involves extracting meaningful features from raw data, sorting features, dismissing duplicate records, and modifying some data columns to obtain new features more relevant to your goals. From this article, you will learn what […]

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From Mice to Trains: Amortized Bayesian Inference on Graph Data

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arXiv:2601.02241v1 Announce Type: new Abstract: Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging. Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI […]

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Making fairness in LLMs observable, quantifiable, and governable

Making fairness in LLMs observable, quantifiable, and governable

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Making fairness in LLMs observable, quantifiable, and governable A new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models. Conversational AI Weijie Xu November 20, 10:23 AM November 20, 10:24 AM Researchers who build large language models have made major strides in developing reasoning systems that can perform well-defined coding and math tasks, where each problem has one right answer. But real-world, personal, and human-oriented questions will always resist a […]

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HELP: How to train an RL agent for adaptive honeypot.

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So I am currently pursuing my undergrad and want to create an adaptive honeypot using RL(specifically DQN) and Cowrie Honeypot as my project. But I don’t have any idea on how to start or what to do and not do. I have beginner level knowledge of Q-Learning and Deep Q-Learning. Any help will be appreciated… submitted by /u/dn874 [link] [comments]

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Nested and outlier embeddings into trees

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arXiv:2601.15470v1 Announce Type: new Abstract: In this paper, we consider outlier embeddings into HSTs and ultrametrics. In particular, for $(X,d)$, let $k$ be the size of the smallest subset of $X$ such that all but that subset (i.e. the “outlier set”) can be probabilistically embedded into the space of HSTs with expected distortion at most $c$. Our primary result is showing that there exists an efficient algorithm that takes in $(X,d)$ and a target distortion $c$ and samples […]

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How to Properly Indent Python Code

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Knowing how to properly indent Python code is a key skill for becoming an accomplished Python developer. Beginning Python programmers know that indentation is required, but learning to indent code so it’s readable, syntactically correct, and easy to maintain is a skill that takes practice. By the end of this tutorial, you’ll know: How to properly indent Python code in a variety of editors How your choice of editor can impact your Python code How to indent code […]

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Trevisan prize (guest post by Alon Rosen)

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The Trevisan Prize for outstanding work in the Theory of Computing is sponsored by the Department of Computing Sciences at Bocconi University and the Italian Academy of Sciences. The prize is named in honor of Luca Trevisan in recognition of his major contributions to the Theory of Computing. It aims to recognize outstanding work in the field, and to broaden the reach of awards both in terms of research areas and recognition of individuals. The prize is biennial (given once every two years) and consists […]

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CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation

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arXiv:2601.15408v1 Announce Type: new Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present CURE, an error-aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine-tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy-grounded report generation using public datasets. The […]

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Fine-tuning vision-language models on memory-constrained devices

Fine-tuning vision-language models on memory-constrained devices

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Fine-tuning vision-language models on memory-constrained devices A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques. Machine learning Rupak Vignesh Swaminathan Jing Liu Nathan Susanj January 08, 11:41 AM January 09, 12:27 PM Fine-tuned vision-language models (VLMs) have shown remarkable performance across many computer vision tasks. However, backpropagation the standard method for adjusting model weights during fine tuning, which works backward from output […]

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Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

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Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient flow-smoothing controllers, we built fast, data-driven simulations that RL agents interact with, learning to maximize energy efficiency while maintaining throughput and operating safely […]

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