8 papers · 1 filter
Read What Matters: Query-Adaptive Quantization for KV Caches
Siddharth Bhandari, Lucas Gretta, Krishna Balasubramanian +1
KV-cache entries are stored before their future queries are known, but each decoding query needs precision in different places. We study this mismatch using separate budgets for re…
Adaptive Mean Estimation by In-Context Learning: A Gradient-Flow Analysis
Martin Eppert, Krishna Balasubramanian, Subhro Ghosh +2
Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation is that PFNs have the propert…
Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Yunrui Guan, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Counterfactual generation seeks to sample outcomes under a hypothetical intervention or decision using observational data collected under the factual assignment mechanism. We devel…
Sample Complexity of Multicalibration for Multilevel Properties
Jiuyao Lu, Krishnakumar Balasubramanian, Aleksandr Podkopaev +1
Calibration requires a predictor to be unbiased after conditioning on its own predictions. Multicalibration asks for this guarantee simultaneously across a collection of groups. Ma…
All Routes Lead to Collapse
K. R. Balasubramanian
Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to attention: they are what content…
FoundCause: Causal Discovery with Latent Confounders from Observational Data
Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an a…
A Quantitative Characterization of Forgetting in Post-Training
Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Continual post-training of generative models is widely used, yet a principled understanding of when and why forgetting occurs remains limited. We develop theoretical results under…
Dense associative memory for Gaussian distributions
Chandan Tankala, Krishnakumar Balasubramanian
Dense associative memories (DAMs) store and retrieve patterns via energy-function based fixed points, but existing models are limited to vector representations. We extend DAMs to G…