3 citations · 6 across the 15 of their papers we have counts for
16 papers
Low-Rank Dependence Decomposition via Accelerated Symmetric Non-negative Matrix Factorization
Lavinia Ghita, Dhruv Desai, Jake Goldberg +1
Symmetric non-negative matrix factorization (SymNMF) recovers latent group structure from a dependence matrix, but its dense, quadratic-memory objective has confined prior work to…
Scaling Laws for Task-Specific LLM Distillation
Lavinia Ghita, Dhruv Desai, Ioana Boier
Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constr…
STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades
Mingshu Li, Dhruv Desai, Jerinsh Jeyapaulraj +4
Accurately measuring portfolio similarity is critical for a wide range of financial applications, including Exchange-traded Fund (ETF) recommendation, portfolio trading, and risk a…
Explainable Unsupervised Anomaly Detection with Random Forest
Joshua S. Harvey, Joshua Rosaler, Mingshu Li +2
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real d…
Can an unsupervised clustering algorithm reproduce a categorization system?
Nathalia Castellanos, Dhruv Desai, Sebastian Frank +2
Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not a…
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
Gregory Yampolsky, Dhruv Desai, Mingshu Li +2
The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated indus…
Quantile Regression using Random Forest Proximities
Mingshu Li, Bhaskarjit Sarmah, Dhruv Desai +4
Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determini…
Machine Learning-based Relative Valuation of Municipal Bonds
Preetha Saha, Jingrao Lyu, Dhruv Desai +4
The trading ecosystem of the Municipal (muni) bond is complex and unique. With nearly 2\% of securities from over a million securities outstanding trading daily, determining the va…
Open Set Recognition for Random Forest
Guanchao Feng, Dhruv Desai, Stefano Pasquali +1
In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowled…
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
Joshua Rosaler, Dhruv Desai, Bhaskarjit Sarmah +4
We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF…
Company Similarity using Large Language Models
Dimitrios Vamvourellis, Máté Toth, Snigdha Bhagat +3
Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution. When a rigoro…
Quantifying Outlierness of Funds from their Categories using Supervised Similarity
Dhruv Desai, Ashmita Dhiman, Tushar Sharma +3
Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection,…
Learning Mutual Fund Categorization using Natural Language Processing
Dimitrios Vamvourellis, Mate Attila Toth, Dhruv Desai +2
Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analys…
Supervised similarity learning for corporate bonds using Random Forest proximities
Jerinsh Jeyapaulraj, Dhruv Desai, Peter Chu +3
Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc. However, going beyond corr…
Fund2Vec: Mutual Funds Similarity using Graph Learning
Vipul Satone, Dhruv Desai, Dhagash Mehta
Identifying similar mutual funds with respect to the underlying portfolios has found many applications in financial services ranging from fund recommender systems, competitors anal…
Machine Learning Fund Categorizations
Dhagash Mehta, Dhruv Desai, Jithin Pradeep
Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment…