2 citations · 5 across the 10 of their papers we have counts for
10 papers
DRAGON: Distributional Rewards Optimize Diffusion Generative Models
Yatong Bai, Jonah Casebeer, Somayeh Sojoudi +1
We present Distributional RewArds for Generative OptimizatioN (DRAGON), a versatile framework for fine-tuning media generation models towards a desired outcome. Compared with tradi…
Ranking Manipulation for Conversational Search Engines
Samuel Pfrommer, Yatong Bai, Tanmay Gautam +1
Major search engine providers are rapidly incorporating Large Language Model (LLM)-generated content in response to user queries. These conversational search engines operate by loa…
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
Yatong Bai, Mo Zhou, Vishal M. Patel +1
Adversarial robustness often comes at the cost of degraded accuracy, impeding real-life applications of robust classification models. Training-based solutions for better trade-offs…
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Yatong Bai, Utsav Garg, Apaar Shanker +10
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collec…
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…
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…
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…
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…
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…