3 citations · 8 across the 9 of their papers we have counts for
7 papers · 1 filter
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Tanmay Gautam, Youngsuk Park, Hao Zhou +2
Fine-tuning language models (LMs) has demonstrated success in a wide array of downstream tasks. However, as LMs are scaled up, the memory requirements for backpropagation become pr…
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization
Tanmay Gautam, Reid Pryzant, Ziyi Yang +2
Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in…
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization
Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi
Conventional optimization methods in machine learning and controls rely heavily on first-order update rules. Selecting the right method and hyperparameters for a particular task of…
An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models
Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi
In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven contro…
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training
Yatong Bai, Tanmay Gautam, Somayeh Sojoudi
The non-convexity of the artificial neural network (ANN) training landscape brings inherent optimization difficulties. While the traditional back-propagation stochastic gradient de…
Safe Reinforcement Learning with Chance-constrained Model Predictive Control
Samuel Pfrommer, Tanmay Gautam, Alec Zhou +1
Real-world reinforcement learning (RL) problems often demand that agents behave safely by obeying a set of designed constraints. We address the challenge of safe RL by coupling a s…
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Yatong Bai, Tanmay Gautam, Yu Gai +1
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program. Unfortu…