57 citations · 200 across the 66 of their papers we have counts for
15 papers · 1 filter
Evolutionary Games on Infinite Strategy Sets: Convergence to Nash Equilibria via Dissipativity
Brendon G. Anderson, Jingqi Li, Somayeh Sojoudi +1
We consider evolutionary dynamics for population games in which players have a continuum of strategies at their disposal. Models in this setting amount to infinite-dimensional diff…
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off
Yatong Bai, Brendon G. Anderson, Somayeh Sojoudi
Deep neural classifiers have recently found tremendous success in data-driven control systems. However, existing models suffer from a trade-off between accuracy and adversarial rob…
Tempo Adaptation in Non-stationary Reinforcement Learning
Hyunin Lee, Yuhao Ding, Jongmin Lee +3
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-…
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
Ziye Ma, Javad Lavaei, Somayeh Sojoudi
Gradient descent (GD) is crucial for generalization in machine learning models, as it induces implicit regularization, promoting compact representations. In this work, we examine t…
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks
Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack set…
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…
Projected Randomized Smoothing for Certified Adversarial Robustness
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers. While randomized smoothing typically yields robust -ball certificates…
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
Yatong Bai, Trung Dang, Dung Tran +2
Diffusion models are instrumental in text-to-audio (TTA) generation. Unfortunately, they suffer from slow inference due to an excessive number of queries to the underlying denoisin…
Initial State Interventions for Deconfounded Imitation Learning
Samuel Pfrommer, Yatong Bai, Hyunin Lee +1
Imitation learning suffers from causal confusion. This phenomenon occurs when learned policies attend to features that do not causally influence the expert actions but are instead…
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +6
Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochast…
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…
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
Ziye Ma, Igor Molybog, Javad Lavaei +1
This paper studies the role of over-parametrization in solving non-convex optimization problems. The focus is on the important class of low-rank matrix sensing, where we propose an…
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Samuel Pfrommer, Brendon G. Anderson, Julien Piet +1
Recent works have introduced input-convex neural networks (ICNNs) as learning models with advantageous training, inference, and generalization properties linked to their convex str…
Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +3
In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and…
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
Yatong Bai, Brendon G. Anderson, Aerin Kim +1
While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to…