24 citations · 118 across the 50 of their papers we have counts for
6 papers · 1 filter
CURATRON: Complete and Robust Preference Data for Rigorous Alignment of Large Language Models
Son The Nguyen, Niranjan Uma Naresh, Theja Tulabandhula
This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), focusing on incomplete and corrupted data in preference…
User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
Son The Nguyen, Theja Tulabandhula, Mary Beth Watson-Manheim
While there is significant interest in using generative AI tools as general-purpose models for specific ML applications, discriminative models are much more widely deployed current…
InteraSSort: Interactive Assortment Planning Using Large Language Models
Saketh Reddy Karra, Theja Tulabandhula
Assortment planning, integral to multiple commercial offerings, is a key problem studied in e-commerce and retail settings. Numerous variants of the problem along with their integr…
Off-Policy Optimization of Portfolio Allocation Policies under Constraints
Nymisha Bandi, Theja Tulabandhula
The dynamic portfolio optimization problem in finance frequently requires learning policies that adhere to various constraints, driven by investor preferences and risk. We motivate…
Deep Reinforcement Learning for Crowdsourced Urban Delivery: System States Characterization, Heuristics-guided Action Choice, and Rule-Interposing Integration
Tanvir Ahamed, Bo Zou, Nahid Parvez Farazi +1
This paper investigates the problem of assigning shipping requests to ad hoc couriers in the context of crowdsourced urban delivery. The shipping requests are spatially distributed…
Optimizing Revenue while showing Relevant Assortments at Scale
Theja Tulabandhula, Deeksha Sinha, Saketh Karra
Scalable real-time assortment optimization has become essential in e-commerce operations due to the need for personalization and the availability of a large variety of items. While…