arXiv:2602.04270v1 Announce Type: cross Abstract: Many fields collect large-scale temporal data through repeated measurements (trials), where each trial is labeled with a set of metadata variables spanning several categories. For example, a trial in a neuroscience study may be linked to a value from category (a): task difficulty, and category (b): animal choice. A critical challenge in time-series analysis is to understand how these labels are encoded within the multi-trial observations, and disentangle the distinct effect of each […]

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I’m working on a credit risk / default prediction problem using CatBoost on tabular data (numerical + categorical, imbalanced). here is Dataset I used for catboost: https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset/data submitted by /u/abv_codes [link] [comments]

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I have been recently using focal loss for heavily imbalanced image and text classification tasks and have been seeing a very large boost in a production environment. For those that don’t know how focal loss works: focal loss reduces the importance of “easy” examples so that the model can focus its learning on “hard” examples. Now i have been thinking that LLM models based on the transformer architecture are essentially an overglorified classifier during training (forced prediction of […]

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The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient exploration of the chemical space for potential drug candidates and reduces the cost of early design efforts. Despite these needs, current chemical language models that generate molecules as SMILES strings are vulnerable to compounding token errors: many samples are unparseable or chemically implausible, and hard constraints meant to prevent failure can restrict exploration. To address this gap, we […]

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arXiv:2601.00087v1 Announce Type: new Abstract: Robotic systems operating in dynamic and uncertain environments increasingly require planners that satisfy complex task sequences while adhering to strict temporal constraints. Metric Interval Temporal Logic (MITL) offers a formal and expressive framework for specifying such time-bounded requirements; however, integrating MITL with reinforcement learning (RL) remains challenging due to stochastic dynamics and partial observability. This paper presents a unified automata-based RL framework for synthesizing policies in both Markov Decision Processes (MDPs) and Partially […]

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arXiv:2505.12225v3 Announce Type: replace-cross Abstract: Best-of-N sampling is a powerful method for improving Large Language Model (LLM) performance, but it is often limited by its dependence on massive, text-based reward models. These models are not only computationally expensive but also data-hungry, requiring extensive labeled datasets for training. This creates a significant data challenge, as they overlook a rich, readily available data source: the LLM’s own internal hidden states. To address this data and efficiency gap, we introduce SWIFT […]

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arXiv:2601.15294v1 Announce Type: new Abstract: Mathematical knowledge exists in many forms, ranging from informal textbooks and lecture notes to large formal proof libraries, yet moving between these representations remains difficult. Informal texts hide dependencies, while formal systems expose every detail in ways that are not always human-readable. Dependency graphs offer a middle ground by making visible the structure of results, definitions, and proofs. We present KnowTeX, a standalone, user-friendly tool that extends the ideas of Lean’s Blueprints, enabling […]

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arXiv:2602.03258v1 Announce Type: new Abstract: Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning setting. Unlike most federated learning approaches, the piecewise-constant nature of RF prevents exact gradient-based optimization. As a result, existing federated RF implementations rely on unprincipled heuristics: for instance, aggregating decision trees trained independently on clients fails to optimize the global impurity criterion, even under simple […]

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