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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...
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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 ...
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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...
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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...
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07/22/2023
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
Robust reinforcement learning (RL) seeks to train policies that can perf...
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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...
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06/13/2023
Reviving Shift Equivariance in Vision Transformers
Shift equivariance is a fundamental principle that governs how we percei...
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06/05/2023
Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing
Locality-sensitive hashing (LSH) based frameworks have been used efficie...
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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...
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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...
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04/10/2023
On the Possibilities of AI-Generated Text Detection
Our work focuses on the challenge of detecting outputs generated by Larg...
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02/06/2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
The robustness of a deep classifier can be characterized by its margins:...
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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...
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01/28/2023
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (R...
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01/24/2023
SMART: Self-supervised Multi-task pretrAining with contRol Transformers
Self-supervised pretraining has been extensively studied in language and...
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11/17/2022
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent
Large-scale non-convex optimization problems are expensive to solve due ...
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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...
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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...
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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...
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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 ...
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08/19/2022
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Standard diffusion models involve an image transform – adding Gaussian n...
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07/25/2022
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Model-based reinforcement learning (RL) achieves higher sample efficienc...
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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...
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06/22/2022
: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In federated learning (FL), the objective of collaboratively learning a ...
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06/21/2022
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (M...
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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...
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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...
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01/01/2022
Transfer RL across Observation Feature Spaces via Model-Based Regularization
In many reinforcement learning (RL) applications, the observation space ...
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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...
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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...
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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...
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08/20/2021
Practical and Fast Momentum-Based Power Methods
The power method is a classical algorithm with broad applications in mac...
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08/13/2021
Datasets for Studying Generalization from Easy to Hard Examples
We describe new datasets for studying generalization from easy to hard e...
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08/03/2021
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Conventional saliency maps highlight input features to which neural netw...
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08/01/2021
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Randomized Smoothing (RS), being one of few provable defenses, has been ...
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06/16/2021
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
Enforcing orthogonality in neural networks is an antidote for gradient v...
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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...
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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...
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05/24/2021
Guided Hyperparameter Tuning Through Visualization and Inference
For deep learning practitioners, hyperparameter tuning for optimizing mo...
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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...
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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...
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09/02/2020
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics
Poisoning attacks, although have been studied extensively in supervised ...
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06/21/2020
Adaptive Learning Rates with Maximum Variation Averaging
Adaptive gradient methods such as RMSProp and Adam use exponential movin...
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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...
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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...
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02/21/2020
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Higher-order Recurrent Neural Networks (RNNs) are effective for long-ter...
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02/16/2020
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL
Transferring knowledge among various environments is important to effici...
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01/14/2020
Understanding Generalization in Deep Learning via Tensor Methods
Deep neural networks generalize well on unseen data though the number of...
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12/21/2019
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning
Model-based reinforcement learning algorithms make decisions by building...
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12/06/2019