1 citations · 3 across the 10 of their papers we have counts for
11 papers
Learning Gradients of Homogeneous Functions
Mayur S. Gowda, Srinivasa Pranav, Shreyas Chaudhari +1
Homogeneous gradient functions have widespread significance, including applications in maximum energy subspace selection and retrieval problems. Euler's homogeneous function theore…
Black-Box Inference of LLM Architectural Properties with Restrictive API Access
Christopher Ellis, Shreyas Chaudhari, Mei-Yu Wang +3
In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given limited API access to an LLM (n…
Representation Matters in Randomized Smoothing for Audio Classification
Jong-Ik Park, Shreyas Chaudhari, José M. F. Moura +1
Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uniquely defined as standard pipe…
RRISE: Robust Radius Inference via a Surrogate Estimator
Jong-Ik Park, Shreyas Chaudhari, Carlee Joe-Wong +1
Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of classification robustness, but its dependence on per-input Monte Carl…
GLUE: Gradient-free Learning to Unify Experts
Jong-Ik Park, Shreyas Chaudhari, Srinivasa Pranav +2
In many deployed systems (multilingual ASR, cross-hospital imaging, region-specific perception), multiple pretrained specialist models coexist. Yet, new target domains often requir…
GradNetOT: Learning Optimal Transport Maps with GradNets
Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
Monotone gradient functions play a central role in solving the Monge formulation of the optimal transport (OT) problem, which arises in modern applications ranging from fluid dynam…
ReLU Networks as Random Functions: Their Distribution in Probability Space
Shreyas Chaudhari, José M. F. Moura
This paper presents a novel framework for understanding trained ReLU networks as random, affine functions, where the randomness is induced by the distribution over the inputs. By c…
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
Jong-Ik Park, Srinivasa Pranav, José M. F. Moura +1
Foundation models are now a major focus of leading technology organizations due to their ability to generalize across diverse tasks. Existing approaches for adapting foundation mod…
Peer-to-Peer Learning Dynamics of Wide Neural Networks
Shreyas Chaudhari, Srinivasa Pranav, Emile Anand +1
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving man…
Peer-to-Peer Learning + Consensus with Non-IID Data
Srinivasa Pranav, José M. F. Moura
Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a centr…
Peer-to-Peer Deep Learning for Beyond-5G IoT
Srinivasa Pranav, José M. F. Moura
We present P2PL, a practical multi-device peer-to-peer deep learning algorithm that, unlike the federated learning paradigm, does not require coordination from edge servers or the…