Learning from human preference data is the dominant route to aligning language models with human values. In linear social choice, where rewards are linear in a fixed feature representation of prompt-response pairs, Ge et al.[2024] show that fitting such a reward by minimizing any non-decreasing convex loss, including B...
Soumya Nasipuri, Sayak Ray Chowdhury, Sanjukta Roy· 0 citations
Instruction Distillation is proposed: an offline procedure in which the MLLM itself generates, for each individual training image, a structured identification instruction encoding general appearance cues, features that differentiate the class from visually similar ones, and a common confusion point.
Hardik Jindal, Soumyabrata Pal, Sayak Ray Chowdhury· 0 citations
This work introduces UCB-HARE (Harmonic Anchored Rank Exploration), which replaces uniform exploration with an inverse-weighted harmonic rank schedule protected by a certified positive-mean anchor, and proves an algorithm-independent lower bound of $\sigma(\sigma\sqrt{k^{\max(1,q)}/T})$; for $q>1$, this shows that the...
Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury· arXiv.org· 1 citation
Private Inference-Time Pessimism (PrivITP) is introduced, which combines $\chi^2$-regularized rejection sampling with a two-phase Gaussian mechanism, and achieves ex-post $(\epsilon,\delta)$-DP with a privacy cost independent of the number of responses, cleanly decouples the regularization parameter from the privacy pa...
I. Jain, Nandini Bhattad, Sayak Ray Chowdhury· 0 citations
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