7 papers
Replicable Bandits with UCB based Exploration
Rohan Deb, Udaya Ghai, Karan Singh +1
We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration. A bandit algorithm is -replicabl…
Inference Time Policy Optimization for Offline RL with Differentiable World Models
Rohan Deb, Stephen J. Wright, Arindam Banerjee
Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without further refinement. Inspired by m…
Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
Eun Go, Rohan Deb, Arindam Banerjee
Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for using price forecasts at inference time. We prop…
KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun
Uncertainty quantification is essential for deploying machine learning models in high-stakes domains such as scientific discovery and healthcare. Conformal Prediction (CP) provides…
Beyond Johnson-Lindenstrauss: Uniform Bounds for Sketched Bilinear Forms
Rohan Deb, Qiaobo Li, Mayank Shrivastava +1
Uniform bounds on sketched inner products of vectors or matrices underpin several important computational and statistical results in machine learning and randomized algorithms, inc…
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
Rohan Deb, Kiran Thekumparampil, Kousha Kalantari +3
Supervised fine-tuning (SFT) is a standard approach to adapting large language models (LLMs) to new domains. In this work, we improve the statistical efficiency of SFT by selecting…