no code implementations • 11 Apr 2024 • Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
Evaluated across a range of both masked and autoregressive LMs on benchmark GLUE tasks, MeZO-SVRG outperforms MeZO with up to 20% increase in test accuracies in both full- and partial-parameter fine-tuning settings.
no code implementations • 4 Oct 2023 • Tanmay Gautam, Reid Pryzant, ZiYi Yang, Chenguang Zhu, Somayeh Sojoudi
SCQ works like a differentiable convex optimization (DCO) layer: in the forward pass, we solve for the optimal convex combination of codebook vectors that quantize the inputs.
no code implementations • 29 Mar 2023 • Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi
Conventional optimization methods in machine learning and controls rely heavily on first-order update rules.
no code implementations • 15 Aug 2022 • Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi
In this discussion paper, we survey recent research surrounding robustness of machine learning models.
no code implementations • 6 Jan 2022 • Yatong Bai, Tanmay Gautam, Somayeh Sojoudi
We apply the robust convex optimization theory to convex training and develop convex formulations that train ANNs robust to adversarial inputs.
no code implementations • 27 Dec 2021 • Samuel Pfrommer, Tanmay Gautam, Alec Zhou, Somayeh Sojoudi
Real-world reinforcement learning (RL) problems often demand that agents behave safely by obeying a set of designed constraints.
no code implementations • 25 May 2021 • Yatong Bai, Tanmay Gautam, Yu Gai, Somayeh Sojoudi
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.