1 citations · 1 across the 6 of their papers we have counts for
8 papers
Algorithmic Analysis of Dense Associative Memory: Finite-Size Guarantees and Adversarial Robustness
Madhava Gaikwad
Dense Associative Memory (DAM) generalizes Hopfield networks through higher-order interactions and achieves storage capacity that scales as under suitable pattern sepa…
Did You Check the Right Pocket? Cost-Sensitive Store Routing for Memory-Augmented Agents
Madhava Gaikwad
Memory-augmented agents maintain multiple specialized stores, yet most systems retrieve from all stores for every query, increasing cost and introducing irrelevant context. We form…
AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
Large language models are exposed to risks of extraction, distillation, and unauthorized fine-tuning. Existing defenses use watermarking or monitoring, but these act after leakage.…
Murphys Laws of AI Alignment: Why the Gap Always Wins
Madhava Gaikwad
We study reinforcement learning from human feedback under misspecification. Sometimes human feedback is systematically wrong on certain types of inputs, like a broken compass that…
AVEC: Bootstrapping Privacy for Local LLMs
Madhava Gaikwad
This position paper presents AVEC (Adaptive Verifiable Edge Control), a framework for bootstrapping privacy for local language models by enforcing privacy at the edge with explicit…
When Are Two RLHF Objectives the Same?
Madhava Gaikwad
The preference optimization literature contains many proposed objectives, often presented as distinct improvements. We introduce Opal, a canonicalization algorithm that determines…