3 papers
cs.LG2026
FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization
Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri
Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations and the need to prevent "ri…
cs.LG2026
Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents
Himanshu Thakur, Anusha Kamath, Anurag Muthyala +3
Recent advances in code generation models have unlocked unprecedented opportunities for automating feature engineering, yet their adoption in real-world ML teams remains constraine…
cs.LG2026
ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition
Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri
Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. Howe…