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Paras Jain

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2 papers

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NeurIPS Conference 2021 Conference Paper

Accelerating Quadratic Optimization with Reinforcement Learning

Jeffrey Ichnowski
Paras Jain
Bartolomeo Stellato
Goran Banjac
Michael Luo
Francesco Borrelli
Joseph E. Gonzalez
Ion Stoica

First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes surprisingly well to previously unseen problems with varying dimension and structure from different applications, including the QPLIB, Netlib LP and Maros-M{\'e}sz{\'a}ros problems. Code, models, and videos are available at https: //berkeleyautomation. github. io/rlqp/.

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NeurIPS Conference 2021 Conference Paper

Representing Long-Range Context for Graph Neural Networks with Global Attention

Zhanghao Wu
Paras Jain
Matthew Wright
Azalia Mirhoseini
Joseph E. Gonzalez
Ion Stoica

Graph neural networks are powerful architectures for structured datasets. However, current methods struggle to represent long-range dependencies. Scaling the depth or width of GNNs is insufficient to broaden receptive fields as larger GNNs encounter optimization instabilities such as vanishing gradients and representation oversmoothing, while pooling-based approaches have yet to become as universally useful as in computer vision. In this work, we propose the use of Transformer-based self-attention to learn long-range pairwise relationships, with a novel “readout” mechanism to obtain a global graph embedding. Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module. This simple architecture leads to state-of-the-art results on several graph classification tasks, outperforming methods that explicitly encode graph structure. Our results suggest that purely-learning-based approaches without graph structure may be suitable for learning high-level, long-range relationships on graphs. Code for GraphTrans is available at https: //github. com/ucbrise/graphtrans.

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