6 papers
HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models
Isuru Herath, Arin Gopakumar, Sharan Sahu
Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear tra…
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
Sharan Sahu, Cameron J. Hogan, Martin T. Wells
In this paper, we provide a comprehensive theoretical analysis of Stochastic Gradient Descent (SGD) and its momentum variants (Polyak Heavy-Ball and Nesterov) for tracking time-var…
Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
Sharan Sahu, Abir Sarkar, Cameron J. Hogan +1
We provide a theoretical analysis of Adam under non-stationary stochastic objectives, separating two regimes: Euclidean tracking under adaptive strong monotonicity of the Adam-prec…
Online Distributionally Robust LLM Alignment via Regression to Relative Reward
Sharan Sahu, Martin T. Wells
Reinforcement Learning with Human Feedback (RLHF) has become crucial for aligning Large Language Models (LLMs) with human intent. However, existing offline RLHF approaches suffer f…
Provably Reliable Classifier Guidance via Cross-Entropy Control
Sharan Sahu, Arisina Banerjee, Yuchen Wu
Classifier-guided diffusion models generate conditional samples by augmenting the reverse-time score with the gradient of the log-probability predicted by a probabilistic classifie…
Towards Optimal Differentially Private Regret Bounds in Linear MDPs
Sharan Sahu
We study regret minimization under privacy constraints in episodic inhomogeneous linear Markov Decision Processes (MDPs), motivated by the growing use of reinforcement learning (RL…