1 citations · 1 across the 4 of their papers we have counts for
5 papers
Evaluating Escalation Signals for LLM Routing: Targets, Controls, and Five Ways to Fool Yourself
Ramin Pishehvar, Andrea Morandi, Mahesh Viswanathan
Deciding when to escalate a query from a small language model to a larger one requires a cheap signal that predicts, before the large model is called, whether escalating would help…
An Emerging Retail Portfolio Management Application: Personalized, Tax-Aware Reinforcement Learning with Natural Language Goals
Ramin Pishehvar
Retail investors lack access to the kind of personalized, tax-aware portfolio management that institutional clients take for granted -- existing robo-advisors use static, rule-base…
A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management
Ramin Pishehvar
We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker…
From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
Kishan Athrey, Ramin Pishehvar, Brian Riordan +1
Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of su…
SPADE: Self-supervised Pretraining for Acoustic DisEntanglement
John Harvill, Jarred Barber, Arun Nair +1
Self-supervised representation learning approaches have grown in popularity due to the ability to train models on large amounts of unlabeled data and have demonstrated success in d…