6 papers
Symmetries and Singularities
Vishnu Varadarajan, Mihir More, Aritra Das +1
Deep neural networks are highly over-parameterized, and different parameter values represent the same predictive function. This makes their effective complexity difficult to measur…
You Can't Escape Your Own Activations : Evaluation Awareness and Multi-Agent Monitoring
Aritra Das, Jaee Ponde, Mihir More +1
LLM agents are increasingly deployed in multi-agent systems, where they can collude while keeping their actions benign. Output monitors designed to detect such collusions can be fo…
On the Indistinguishability of Human v/s AI Generated Text
Jaee Ponde, Aritra Das, Mihir More +1
The rapid improvement of LLMs has made distinguishing AI-generated text from human writing a pressing problem. This challenge is further amplified by paraphrasing tools designed to…
Parameterized Complexity of -Lipschitz Constants for Input Convex Neural Networks and -Norm Maximization over Zonotopes
Aritra Das, Vincent Froese, Moritz Grillo +6
Lipschitz constants are a standard way to quantify the sensitivity of neural networks to small input perturbations, but computing them is difficult even for shallow ReLU networks.…
A Rate Separation for Agnostic Direct Sums
Mihir More, Aritra Das, Debayan Gupta
Hanneke, Moran, and Waknine \cite{HannekeMoranWaknine2024} asked how the agnostic PAC learning curve of the direct sum depends on the single-instance learning curve $\epsagn(…
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Saptarishi Dhanuka, Sarvesh Iyer, Manmeet Singh +5
Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors. But no model consistently do…