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Gaurav

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Good Enough: Satisficing in Production Machine Learning

Herbert Simon observed that managers rarely chase the global optimum. Instead, they set an aspiration level, a “good enough” performance, and quit searching once they found an option that met it. Simon called this satisficing. That habit makes sense because every decision is a trade‑off. In modeling, the benefit

Advertising Without Signal: The Rise of the Grifter Equilibrium

Economists credit ads with two welfare‑enhancing roles: 1. Informative – trimming search costs (Stigler 1961 (pdf)). 2. Signaling – In classic models, high-quality sellers are more willing to incur large, sunk ad costs because they expect to recoup them through future sales, especially in experience-good markets where quality is learned over

Boosting Stability: Reducing Instability Under Row Permutation

Shuffle your training data, and XGBoost might give you a different model. Even when you keep features, hyperparameters, and random_state fixed. This behavior violates what most practitioners reasonably expect: that models should be invariant to row permutation. And can lead to silent drift, flaky tests, and spurious alerts. This,

From Vibe Coding Assistants to Coding Assistants

Modern coding assistants often firehose code: a single prompt yields pages of plausible implementation that the developer must reverse‑engineer, reconcile with project conventions, and defend in review. That behavior is optimized for vibe coding—outputs that look complete and fluent—rather than for actual coding, where teams must understand,

A Pink Revolution in Indian Policymaking

The 2025 Union budget earmarks 8.86%* of the total expenditure for gender-related programs (see MWCD). Pair that with the fact that over the last five years, more than 14 states have introduced women-centric unconditional cash transfer programs, reaching over 110 million women—nearly a fifth of India’s adult

(Better) Split Decisions: Toward A More Structurally Stable CART

Classification and Regression Trees (CART) condense data into one transparent decision tree. Their weakness is structural instability: a small change to the training data can replace an early split, such as “age > 65” with “income > $50 k,” propagating a completely new tree. This instability makes trees unsuitable for

Measuring Tree Stability

Consider training a decision tree to predict customer churn. You split your data in half and build a model on one half. The tree identifies tenure, monthly charges, and contract type as key factors. Satisfied with the results, you rebuild the model on the other random split—but now the

What's Wrong? Adversarial LLM Judges With Their Own Evaluation Criteria

How should we evaluate what large language models say? Metrics like BLEU or exact match don't work well when answers are open-ended or involve reasoning. Human judgments are better—but they're costly and inconsistent. So increasingly, researchers have turned to LLMs themselves to act as evaluators.

WAR For Cricket

In baseball, WAR (Wins Above Replacement) is a comprehensive metric to quantify a player’s contribution to team success, relative to a replacement-level player. The idea has slowly migrated to other sports. In cricket, however, WAR remains underdeveloped. Most public evaluations still rely on averages, strike rates, or wickets — useful,

Pareto ML Deployments

In machine learning, a common deployment strategy is to replace an existing model with one that performs better overall. Another common strategy refines this approach by limiting deployment to user segments or regions where the improvements are clear. Both approaches allow regressions: new errors on cases that the old model