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06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
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05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
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05/30/2023
Are Large Kernels Better Teachers than Transformers for ConvNets?
This paper reveals a new appeal of the recently emerged large-kernel Con...
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05/18/2023
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Warning: This paper contains content that may be offensive or upsetting....
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05/15/2023
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
In this study, we analyze NLG automatic metrics based on whether human e...
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05/05/2023
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree
Concerns regarding unfairness and discrimination in the context of artif...
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03/15/2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques t...
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03/10/2023
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Feature selection that selects an informative subset of variables from d...
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02/13/2023
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Tomorrow's robots will need to distinguish useful information from noise...
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12/19/2022
Dynamic Sparse Network for Time Series Classification: Learning What to "see”
The receptive field (RF), which determines the region of time series to ...
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11/28/2022
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Recent works have impressively demonstrated that there exists a subnetwo...
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11/26/2022
Where to Pay Attention in Sparse Training for Feature Selection?
A new line of research for feature selection based on neural networks ha...
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09/08/2022
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
We study a posterior sampling approach to efficient exploration in const...
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09/04/2022
FairSNA: Algorithmic Fairness in Social Network Analysis
In recent years, designing fairness-aware methods has received much atte...
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08/23/2022
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lottery tickets (LTs) is able to discover accurate and sparse subnetwork...
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07/08/2022
Memory-free Online Change-point Detection: A Novel Neural Network Approach
Change-point detection (CPD), which detects abrupt changes in the data d...
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07/07/2022
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the ...
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05/30/2022
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Recent works on sparse neural network training (sparse training) have sh...
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05/22/2022
Phrase-level Textual Adversarial Attack with Label Preservation
Generating high-quality textual adversarial examples is critical for inv...
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05/20/2022
Survey on Fair Reinforcement Learning: Theory and Practice
Fairness-aware learning aims at satisfying various fairness constraints ...
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02/17/2022
Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning
Despite an abundance of fairness-aware machine learning (fair-ml) algori...
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02/14/2022
The Impact of Batch Learning in Stochastic Linear Bandits
We consider a special case of bandit problems, named batched bandits, in...
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02/05/2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Random pruning is arguably the most naive way to attain sparsity in neur...
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12/16/2021
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Few-shot classification tasks aim to classify images in query sets based...
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11/03/2021
The Impact of Batch Learning in Stochastic Bandits
We consider a special case of bandit problems, namely batched bandits. M...
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10/11/2021
Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning
Continual learning agents should incrementally learn a sequence of tasks...
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10/01/2021
Calibrated Adversarial Training
Adversarial training is an approach of increasing the robustness of mode...
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09/21/2021
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Local decision rules are commonly understood to be more explainable, due...
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08/27/2021
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
The ability to detect Out-of-Domain (OOD) inputs has been a critical req...
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08/07/2021
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Exposure bias is a well-known issue in recommender systems where items a...
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07/07/2021
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems tha...
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07/07/2021
Hierarchical Semantic Segmentation using Psychometric Learning
Assigning meaning to parts of image data is the goal of semantic image s...
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07/06/2021
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model's resilience ag...
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06/28/2021
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity
Recent works on sparse neural networks have demonstrated that it is poss...
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06/19/2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Works on lottery ticket hypothesis (LTH) and single-shot network pruning...
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06/08/2021
Dynamic Sparse Training for Deep Reinforcement Learning
Deep reinforcement learning has achieved significant success in many dec...
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04/19/2021
Direction-Aggregated Attack for Transferable Adversarial Examples
Deep neural networks are vulnerable to adversarial examples that are cra...
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04/16/2021
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Recent years have witnessed an upsurge of interest in the problem of ano...
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02/04/2021
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
In this paper, we introduce a new perspective on training deep neural ne...
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01/28/2021
Self-Attention Meta-Learner for Continual Learning
Continual learning aims to provide intelligent agents capable of learnin...
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01/22/2021
Selfish Sparse RNN Training
Sparse neural networks have been widely applied to reduce the necessary ...
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01/15/2021
Learning Invariant Representation for Continual Learning
Continual learning aims to provide intelligent agents that are capable o...
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12/01/2020
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Major complications arise from the recent increase in the amount of high...
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11/30/2020
ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic
In the light of the COVID-19 pandemic, deep learning methods have been w...
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11/07/2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Deep learning achieves state-of-the-art performance in many tasks but ex...
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09/30/2020
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Effectively detecting anomalous nodes in attributed networks is crucial ...
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07/25/2020
Feedback Loop and Bias Amplification in Recommender Systems
Recommendation algorithms are known to suffer from popularity bias; a fe...
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07/15/2020
SpaceNet: Make Free Space For Continual Learning
The continual learning (CL) paradigm aims to enable neural networks to l...
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06/24/2020
Topological Insights in Sparse Neural Networks
Sparse neural networks are effective approaches to reduce the resource r...
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05/03/2020
