b'Furong Huang'

research

∙ 09/20/2023

Safe and Robust Multi-Agent Reinforcement Learning for Connected Autonomous Vehicles under State Perturbations

Sensing and communication technologies have enhanced learning-based deci...

0 Zhili Zhang, et al. ∙

research

∙ 09/07/2023

Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making

Decisions made by machine learning models may have lasting impacts over ...

0 Yuancheng Xu, et al. ∙

research

∙ 08/03/2023

Aligning Agent Policy with Externalities: Reward Design via Bilevel RL

In reinforcement learning (RL), a reward function is often assumed at th...

0 Souradip Chakraborty, et al. ∙

research

∙ 08/02/2023

More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes

CLIP, as a foundational vision language model, is widely used in zero-sh...

0 Bang An, et al. ∙

research

∙ 07/22/2023

Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations

Robust reinforcement learning (RL) seeks to train policies that can perf...

0 Yongyuan Liang, et al. ∙

research

∙ 06/22/2023

TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

Despite recent progress in reinforcement learning (RL) from raw pixel da...

0 Ruijie Zheng, et al. ∙

research

∙ 06/13/2023

Reviving Shift Equivariance in Vision Transformers

Shift equivariance is a fundamental principle that governs how we percei...

3 Peijian Ding, et al. ∙

research

∙ 06/05/2023

Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing

Locality-sensitive hashing (LSH) based frameworks have been used efficie...

17 Tahseen Rabbani, et al. ∙

research

∙ 05/27/2023

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in Multi-Agent RL

Most existing works consider direct perturbations of victim's state/acti...

15 Xiangyu Liu, et al. ∙

research

∙ 05/25/2023

GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint

Given the growing concerns about fairness in machine learning and the im...

13 Paiheng Xu, et al. ∙

research

∙ 04/10/2023

On the Possibilities of AI-Generated Text Detection

Our work focuses on the challenge of detecting outputs generated by Larg...

29 Souradip Chakraborty, et al. ∙

research

∙ 02/06/2023

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

The robustness of a deep classifier can be characterized by its margins:...

1 Yuancheng Xu, et al. ∙

research

∙ 02/02/2023

Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function

Probabilistic dynamics model ensemble is widely used in existing model-b...

27 Ruijie Zheng, et al. ∙

research

∙ 01/28/2023

STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning

Directed Exploration is a crucial challenge in reinforcement learning (R...

19 Souradip Chakraborty, et al. ∙

research

∙ 01/24/2023

SMART: Self-supervised Multi-task pretrAining with contRol Transformers

Self-supervised pretraining has been extensively studied in language and...

3 Yanchao Sun, et al. ∙

research

∙ 11/17/2022

Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent

Large-scale non-convex optimization problems are expensive to solve due ...

0 Marco Bornstein, et al. ∙

research

∙ 11/02/2022

Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

Data augmentation is a critical contributing factor to the success of de...

33 Kaiwen Yang, et al. ∙

research

∙ 10/25/2022

SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

The decentralized Federated Learning (FL) setting avoids the role of a p...

5 Marco Bornstein, et al. ∙

research

∙ 10/12/2022

Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning

Recent studies reveal that a well-trained deep reinforcement learning (R...

11 Yongyuan Liang, et al. ∙

research

∙ 09/26/2022

An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication

Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new ...

0 Xiaofeng Xue, et al. ∙

research

∙ 08/19/2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Standard diffusion models involve an image transform – adding Gaussian n...

12 Arpit Bansal, et al. ∙

research

∙ 07/25/2022

Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

Model-based reinforcement learning (RL) achieves higher sample efficienc...

9 Xiyao Wang, et al. ∙

research

∙ 06/26/2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

The increasing reliance on ML models in high-stakes tasks has raised a m...

19 Bang An, et al. ∙

research

∙ 06/22/2022

: Calibrating Global and Local Models via Federated Learning Beyond Consensus

In federated learning (FL), the objective of collaboratively learning a ...

2 Amrit Singh Bedi, et al. ∙

research

∙ 06/21/2022

Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

Communication is important in many multi-agent reinforcement learning (M...

2 Yanchao Sun, et al. ∙

research

∙ 06/02/2022

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

In this work, we propose a novel Kernelized Stein Discrepancy-based Post...

8 Souradip Chakraborty, et al. ∙

research

∙ 02/11/2022

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Machine learning systems perform well on pattern matching tasks, but the...

4 Arpit Bansal, et al. ∙

research

∙ 01/01/2022

Transfer RL across Observation Feature Spaces via Model-Based Regularization

In many reinforcement learning (RL) applications, the observation space ...

9 Yanchao Sun, et al. ∙

research

∙ 11/10/2021

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

Machine learning models that are developed to be invariant under certain...

16 Sicheng Zhu, et al. ∙

research

∙ 10/27/2021

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a f...

31 Mucong Ding, et al. ∙

research

∙ 09/17/2021

Comfetch: Federated Learning of Large Networks on Memory-Constrained Clients via Sketching

A popular application of federated learning is using many clients to tra...

0 Tahseen Rabbani, et al. ∙

research

∙ 08/20/2021

Practical and Fast Momentum-Based Power Methods

The power method is a classical algorithm with broad applications in mac...

0 Tahseen Rabbani, et al. ∙

research

∙ 08/13/2021

Datasets for Studying Generalization from Easy to Hard Examples

We describe new datasets for studying generalization from easy to hard e...

0 Avi Schwarzschild, et al. ∙

research

∙ 08/03/2021

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

Conventional saliency maps highlight input features to which neural netw...

2 Roman Levin, et al. ∙

research

∙ 08/01/2021

Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders

Randomized Smoothing (RS), being one of few provable defenses, has been ...

0 Huimin Zeng, et al. ∙

research

∙ 06/16/2021

Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework

Enforcing orthogonality in neural networks is an antidote for gradient v...

2 Jiahao Su, et al. ∙

research

∙ 06/09/2021

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL

Evaluating the worst-case performance of a reinforcement learning (RL) a...

0 Yanchao Sun, et al. ∙

research

∙ 06/08/2021

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Deep neural networks are powerful machines for visual pattern recognitio...

23 Avi Schwarzschild, et al. ∙

research

∙ 05/24/2021

Guided Hyperparameter Tuning Through Visualization and Inference

For deep learning practitioners, hyperparameter tuning for optimizing mo...

0 Hyekang Joo, et al. ∙

research

∙ 03/02/2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations

Data poisoning and backdoor attacks manipulate training data to induce s...

0 Eitan Borgnia, et al. ∙

research

∙ 10/24/2020

Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks

Adversarial Training is proved to be an efficient method to defend again...

0 Huimin Zeng, et al. ∙

research

∙ 09/02/2020

Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics

Poisoning attacks, although have been studied extensively in supervised ...

5 Yanchao Sun, et al. ∙

research

∙ 06/21/2020

Adaptive Learning Rates with Maximum Variation Averaging

Adaptive gradient methods such as RMSProp and Adam use exponential movin...

19 Chen Zhu, et al. ∙

research

∙ 06/17/2020

Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets

Deep learning models extract, before a final classification layer, featu...

11 Roozbeh Yousefzadeh, et al. ∙

research

∙ 02/22/2020

Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers

Convex relaxations are effective for training and certifying neural netw...

5 Chen Zhu, et al. ∙

research

∙ 02/21/2020

Convolutional Tensor-Train LSTM for Spatio-temporal Learning

Higher-order Recurrent Neural Networks (RNNs) are effective for long-ter...

3 Jiahao Su, et al. ∙

research

∙ 02/16/2020

TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL

Transferring knowledge among various environments is important to effici...

32 Yanchao Sun, et al. ∙

research

∙ 01/14/2020

Understanding Generalization in Deep Learning via Tensor Methods

Deep neural networks generalize well on unseen data though the number of...

67 Jingling Li, et al. ∙

research

∙ 12/21/2019

Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning

Model-based reinforcement learning algorithms make decisions by building...

0 Yanchao Sun, et al. ∙

research

∙ 12/06/2019

Sampling-Free Learning of Bayesian Quantized Neural Networks

Bayesian learning of model parameters in neural networks is important in...

0 Jiahao Su, et al. ∙

Furong Huang

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