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07/05/2023
Scaling In-Context Demonstrations with Structured Attention
The recent surge of large language models (LLMs) highlights their abilit...
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05/28/2023
Reward Collapse in Aligning Large Language Models
The extraordinary capabilities of large language models (LLMs) such as C...
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05/26/2023
Large Language Models as Tool Makers
Recent research shows the potential of enhancing the problem-solving abi...
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10/17/2022
What Makes Convolutional Models Great on Long Sequence Modeling?
Convolutional models have been widely used in multiple domains. However,...
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07/19/2022
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond
We consider vertical logistic regression (VLR) trained with mini-batch g...
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06/23/2021
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
The attention module, which is a crucial component in Transformer, canno...
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06/15/2021
First Place Solution of KDD Cup 2021 OGB Large-Scale Challenge Graph-Level Track
In this technical report, we present our solution of KDD Cup 2021 OGB La...
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06/09/2021
Do Transformers Really Perform Bad for Graph Representation?
The Transformer architecture has become a dominant choice in many domain...
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06/08/2021
Towards a Theoretical Framework of Out-of-Distribution Generalization
Generalization to out-of-distribution (OOD) data, or domain generalizati...
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02/22/2021
A Theory of Label Propagation for Subpopulation Shift
One of the central problems in machine learning is domain adaptation. Un...
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02/10/2021
Towards Certifying ℓ_∞ Robustness using Neural Networks with ℓ_∞-dist Neurons
It is well-known that standard neural networks, even with a high classif...
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09/22/2020
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Network pruning is a method for reducing test-time computational resourc...
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09/07/2020
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
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07/27/2020
RANDOM MASK: Towards Robust Convolutional Neural Networks
Robustness of neural networks has recently been highlighted by the adver...
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06/01/2020
Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this p...
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11/19/2019
Defective Convolutional Layers Learn Robust CNNs
Robustness of convolutional neural networks has recently been highlighte...
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06/19/2019
Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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06/03/2019
Adversarially Robust Generalization Just Requires More Unlabeled Data
Neural network robustness has recently been highlighted by the existence...
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05/28/2019
