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Machine Learning

Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
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Machine Learning Reinforcement Learning 🏢 National University of Singapore
PSɛBAI+ is a near-optimal algorithm for best arm identification in piecewise stationary linear bandits, efficiently detecting changepoints and aligning contexts for improved accuracy and minimal sampl…
Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering
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Machine Learning Unsupervised Learning 🏢 National University of Defense Technology
AIMC-CVR: A novel approach that alleviates anchor-shift in incomplete multi-view clustering via cross-view reconstruction, improving accuracy and scalability.
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
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AI Generated Machine Learning Deep Learning 🏢 Stanford University
ALIDIFF aligns target-aware molecule diffusion models with exact energy optimization, generating molecules with state-of-the-art binding energies and improved properties.
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
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Machine Learning Reinforcement Learning 🏢 University of Manchester
AI agents learn to cooperate effectively even when individual and group goals clash using the new Altruistic Gradient Adjustment (AgA) algorithm.
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
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Machine Learning Unsupervised Learning 🏢 DI ENS, CRNS, PSL University, INRIA Paris
This paper presents novel informational results and a new algorithm (‘Ping-Pong’) for solving the Procrustes-Wasserstein problem, significantly advancing high-dimensional data alignment.
Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control
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Machine Learning Reinforcement Learning 🏢 Tsinghua University
Efficient Diffusion Alignment (EDA) leverages pretrained diffusion models and Q-functions for efficient continuous control, exceeding all baselines with minimal annotation.
Alias-Free Mamba Neural Operator
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Machine Learning Deep Learning 🏢 Zhejiang University of Technology
MambaNO: a novel neural operator achieving linear complexity and state-of-the-art accuracy in solving PDEs by cleverly balancing global and local information using an alias-free architecture.
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
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Machine Learning Active Learning 🏢 University of Wisconsin-Madison
AHA: Human-assisted OOD learning maximizes OOD generalization and detection by strategically labeling data in a novel maximum disambiguation region, significantly outperforming existing methods with o…
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
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AI Generated Machine Learning Reinforcement Learning 🏢 Washington State University
WSAC, a novel algorithm, robustly optimizes safe offline RL policies using adversarial training, guaranteeing improved performance over reference policies with limited data.
Adversarially Robust Multi-task Representation Learning
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Machine Learning Transfer Learning 🏢 Johns Hopkins University
Multi-task learning boosts adversarial robustness in transfer learning by leveraging diverse source data to build a shared representation, enabling effective learning in data-scarce target tasks, as p…
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
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Machine Learning Deep Learning 🏢 Zhejiang University
Rate-based backpropagation boosts deep spiking neural network training efficiency by leveraging rate coding, achieving comparable performance to BPTT with reduced complexity.
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
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Machine Learning Meta Learning 🏢 Karlsruhe Institute of Technology
EBiL-HaDS, a novel adaptive domain scheduler, significantly boosts open-set domain generalization by strategically sequencing training domains based on model reliability assessments.
ADOPT: Modified Adam Can Converge with Any $eta_2$ with the Optimal Rate
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Machine Learning Deep Learning 🏢 University of Tokyo
ADOPT, a novel adaptive gradient method, achieves optimal convergence rates without restrictive assumptions, unlike Adam, significantly improving deep learning optimization.
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
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Machine Learning Deep Learning 🏢 Department of Mathematics and Statistics, Old Dominion University
Multi-Grade Deep Learning (MGDL) conquers spectral bias in deep neural networks by incrementally learning low-frequency components, ultimately capturing high-frequency features through composition.
Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment
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AI Generated Machine Learning Self-Supervised Learning 🏢 College of Control Science and Engineering, Zhejiang University, China
MiTSformer, a novel framework, recovers latent continuous variables from discrete data to enable complete spatial-temporal modeling of mixed time series, achieving state-of-the-art performance on mult…
Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
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AI Generated Machine Learning Meta Learning 🏢 Peking University
MetaDebias tackles hidden confounding in recommender systems using heterogeneous observational data, achieving state-of-the-art performance without expensive RCT data.
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
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Machine Learning Optimization 🏢 Nanjing University
Adaptive STORM achieves optimal convergence rates for stochastic optimization of non-convex functions under weaker assumptions, eliminating the need for bounded gradients or function values and removi…
Adaptive Sampling for Efficient Softmax Approximation
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Machine Learning Optimization 🏢 Stanford University
AdaptiveSoftmax: Achieve 10x+ speedup in softmax computation via adaptive sampling!
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
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AI Generated Machine Learning Reinforcement Learning 🏢 Georgia Institute of Technology
Adaptive Preference Scaling boosts Reinforcement Learning from Human Feedback by using a novel loss function that adapts to varying preference strengths, resulting in improved policy performance and s…
Adaptive Passive-Aggressive Framework for Online Regression with Side Information
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Machine Learning Deep Learning 🏢 Hong Kong University of Science and Technology
Adaptive Passive-Aggressive framework with Side information (APAS) significantly boosts online regression accuracy by dynamically adjusting thresholds and integrating side information, leading to supe…