[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] Firstly, a huge thank-you to the participants in the study group that led to this guide, for their enthusiastic participation, interesting perspectives and insights, and useful feedback and contributions: Scott Cameron, Jean Michel Sarr, Suvarna Kadam, James Allingham, Bharati Srinivasan, Lood van Niekerk, and Witold Szejgis. Thank you too to the Depth First Learning team for bringing me on board, and especially to Avital […]

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Key Highlights: These days, social media app companies are under massive scrutiny by regulators and lawmakers around the world. The major point of concern is related to social media’s effect on minors’ mental health. TikTok, in the past, has been summoned by U.S. lawmakers for its addictive design that makes minor audiences hooked to the app, reportedly affecting their mental health and behavior. U.S. court case puts Instagram and YouTube’s app design under scrutiny Speaking of social media […]

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arXiv:2601.18844v1 Announce Type: new Abstract: Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates, especially in large-scale enterprise systems. These false alarms demand substantial manual inspection, creating severe inefficiencies in industrial code review. While recent work has demonstrated the potential of large language models (LLMs) for false alarm reduction on open-source benchmarks, their effectiveness in real-world enterprise settings remains […]

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Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations from structured data. Despite their growing popularity and success across various applications, GNNs encounter several challenges that limit their performance. in their generalization, robustness to adversarial perturbations, and the effectiveness of their representation learning capabilities. In this dissertation, I investigate these core aspects through three main contributions: (1) developing new representation learning techniques based on Graph Shift Operators (GSOs, aiming for enhanced performance across various contexts […]

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arXiv:2503.16382v2 Announce Type: replace Abstract: We study the benefits of sparsity in nonparametric contextual bandit problems, in which the set of candidate features is countably or uncountably infinite. Our contribution is two-fold. First, using a novel reduction to sequences of multi-armed bandit problems, we provide lower bounds on the minimax regret, which show that polynomial dependence on the number of actions is generally unavoidable in this setting. Second, we show that a variant of the Feel-Good Thompson Sampling […]

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arXiv:2602.08933v1 Announce Type: new Abstract: Regression neural networks (NNs) are most commonly trained by minimizing the mean squared prediction error, which is highly sensitive to outliers and data contamination. Existing robust training methods for regression NNs are often limited in scope and rely primarily on empirical validation, with only a few offering partial theoretical guarantees. In this paper, we propose a new robust learning framework for regression NNs based on the $beta$-divergence (also known as the density power […]

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Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring significant amounts of storage for each user or item to be added to the database. This is a problem whenever the collaborative filtering task is larger than the medium-sized Netflix Prize data. In this paper, we propose a new model for representing and compressing matrix factors via hashing. This allows for […]

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Can traditional equity markets and cryptocurrency exchanges coexist on the same platform? BitMEX, the derivatives exchange that has operated since 2014 without losing customer funds to hacks, is testing this proposition with a month-long campaign offering 70,000 USDT in prizes to traders who engage with equity perpetual contracts. The campaign, running from February 12 to March 12, 2026, targets users who trade contracts based on Apple (AAPL), Tesla (TSLA), and other publicly traded companies, marking a continuation of […]

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