February 2026

Modern video recommendation systems aim to optimize user engagement and platform objectives, yet often face structural exposure imbalances caused by behavioral biases. In this work, we focus on the post-ranking stage and present LAFB (Learning to Alleviate Familiarity Bias), a lightweight and model-agnostic framework designed to mitigate familiarity bias in recommendation outputs. LAFB models user-content familiarity using discrete and continuous interaction features, and estimates personalized debiasing factors to adjust user rating prediction scores, thereby reducing the dominance of […]

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We study the problem of learning neural classifiers in a neurosymbolic setting where the hidden gold labels of input instances must satisfy a logical formula. Learning in this setting proceeds by first computing (a subset of) the possible combinations of labels that satisfy the formula and then computing a loss using those combinations and the classifiers’ scores. One challenge is that the space of label combinations can grow exponentially, making learning difficult. We propose a technique that prunes […]

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The previous post discussed two ways to compute the nth Fibonacci number. The first is to compute all the Fibonacci numbers up to the nth iteratively using the defining property of Fibonacci numbers Fn + 2 = Fn + Fn + 1 with extended integer arithmetic. The second approach is to use Binet’s formula Fn = round( φn / √ 5 ) where φ is the golden ratio. It’s not clear which approach is more efficient. You could say […]

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A few days ago I wrote about Fibonacci numbers and certificates. As I pointed out in the article, there’s no need to certify Fibonacci numbers, but the point of the post was to illustrate the idea of a solution certificate in a simple context. Practical uses of certificates are more complicated. This time I want to use Fibonacci numbers to illustrate space tradeoffs. The post on Fibonacci certificates imagined providing someone a pair (F, r) where F is a large […]

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Partial Differential Equations are precise in modelling the physical, biological and graphical phenomena. However, the numerical methods suffer from the curse of dimensionality, high computation costs and domain-specific discretization. We aim to explore pros and cons of different PDE solvers, and apply them to specific scientific simulation problems, including forwarding solution, inverse problems and equations discovery. In particular, we extend the recent CNF (NeurIPS 2023) framework solver to multi-dependent-variable and non-linear settings, together with down-stream applications. The outcomes […]

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Learning performed over finite time is necessarily irreversible. In Part~I of this series, we modeled learning as a transport process in the space of parameter distributions and derived the Epistemic Speed Limit, which lower-bounds entropy production under finite-time learning. In this work (Part~II), we study the consequences of this irreversibility for continual learning from a trajectory-level perspective. We show that finite dissipation constrains not only which solutions are reachable, but which learning paths remain dynamically accessible. Although a […]

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I’m very much interested in applied RL and in my third year of undergrad majoring in physics but learning RL side by side but rl being my main moat .. my vision is to create a applied rl startup which has a good impact and solves a problem something like warehouse optimisation for energy grid .. or im also motivated equally by rl applications in brain computer interfaces so i think of pursuing a phd in computation neuroscience […]

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Early and accurate detection of Parkinson’s disease (PD) remains a critical challenge in medical diagnostics due to the subtlety of early-stage symptoms and the complex, non-linear relationships inherent in biomedical data. Traditional machine learning (ML) models, though widely applied to PD detection, often rely on extensive feature engineering and struggle to capture complex feature interactions. This study investigates the effectiveness of attention-based deep learning models for early PD detection using tabular biomedical data. We present a comparative evaluation […]

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