3.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Inception Institute of Artificial IntelligenceAE16 papers
- Carnegie Mellon UniversityUS15 papers
- École PolytechniqueFR11 papers
- Australian National UniversityAU10 papers
- Nankai UniversityCN8 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR7 papers
- Nanyang Technological UniversitySG7 papers
- National Research University Higher School of EconomicsRU7 papers
- The University of SydneyAU7 papers
- University of WaterlooCA7 papers
- Hamad bin Khalifa UniversityQA6 papers
- Moscow Institute of Physics and TechnologyRU6 papers
25 papers · 1 filter
CacheMuon: Using Temporal Preconditioning To Approximate Polar Factor
Bishnu Dev, Sushil Bohara, Martin Takáč +1
Muon is an optimizer that computes updates using the polar factor of the momentum matrix and has shown strong empirical performance across a range of training settings. A key compo…
Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality
Lingjing Kong, Shaoan Xie, Guangyi Chen +4
Deep generative models, while revolutionizing fields like image and text generation, largely operate as opaque ``black boxes'', hindering human understanding, control, and alignmen…
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
Ruichu Cai, Xiaokai Huang, Wei Chen +2
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interfe…
Constrained Adversarial Perturbation
Virendra Nishad, Bhaskar Mukhoty, Hilal AlQuabeh +2
Deep neural networks have achieved remarkable success in a wide range of classification tasks. However, they remain highly susceptible to adversarial examples - inputs that are sub…
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Guoming Li, Jian Yang, Yifan Chen
Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various gra…
On Global Convergence Rates for Federated Softmax Policy Gradient under Heterogeneous Environments
Safwan Labbi, Paul Mangold, Daniil Tiapkin +1
We provide global convergence rates for vanilla and entropy-regularized federated softmax stochastic policy gradient (FedPG) with local training. We show that FedPG converges to a…
In-context Learning of Evolving Data Streams with Tabular Foundational Models
Afonso Lourenço, João Gama, Eric P. Xing +1
State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the eme…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
Xianzhi Zhang, Yipeng Zhou, Miao Hu +4
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without expo…
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters
Guoming Li, Jian Yang, Shangsong Liang
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite t…
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li +5
Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the…
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
Raphael Lafargue, Luke Smith, Franck Vermet +4
The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear…
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
Hyunchai Jeong, Adiba Ejaz, Jin Tian +1
Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independ…
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
Tianjun Yao, Jiaqi Sun, Defu Cao +2
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frame…
Improving the Expressiveness of -hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Tianjun Yao, Yiongxu Wang, Kun Zhang +1
Graph neural networks (GNNs) have become the \textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-relate…
Local Methods with Adaptivity via Scaling
Savelii Chezhegov, Sergey Skorik, Nikolas Khachaturov +5
The rapid development of machine learning and deep learning has introduced increasingly complex optimization challenges that must be addressed. Indeed, training modern, advanced mo…
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada +1
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or repr…
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
Tian-Le Yang, Kuang-Yao Lee, Kun Zhang +1
In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a Linear Non-Gaussian Acyclic Model (LiNGAM), encompassing both the causal ordering…
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems
Emna Baccour, Aiman Erbad, Amr Mohamed +2
Although Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Thi…
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
Qingsong Zhang, Bin Gu, Zhiyuan Dang +2
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. A complete list o…
Random-reshuffled SARAH does not need a full gradient computations
Aleksandr Beznosikov, Martin Takáč
The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a variance reduced variant of the Stochastic Gradient Descent (SGD) algorithm that needs a gradient of the objectiv…
Self-Improved Retrosynthetic Planning
Junsu Kim, Sungsoo Ahn, Hankook Lee +1
Retrosynthetic planning is a fundamental problem in chemistry for finding a pathway of reactions to synthesize a target molecule. Recently, search algorithms have shown promising r…
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Qingsong Zhang, Bin Gu, Cheng Deng +1
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL a…
Conditional Generative Modeling via Learning the Latent Space
Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan +2
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-mod…