Machine Learning

PCA’s denoising effect is quantified via a novel metric: compression ratio. This metric reveals PCA’s ability to reduce intra-community distances while preserving inter-community distances in noisy d…

C-GAIL stabilizes Generative Adversarial Imitation Learning by applying control theory, resulting in faster convergence, reduced oscillation, and better expert policy matching.

Byzantine-tolerant Variance-Reduced MARINA with Partial Participation (Byz-VR-MARINA-PP) is the first distributed method to simultaneously achieve Byzantine robustness and partial client participation…

A novel graph-theoretic framework bridges OOD detection & generalization, offering theoretical error bounds and competitive empirical performance.

BRAID: A novel, conservative fine-tuning method surpasses offline design optimization by cleverly combining generative diffusion models with reward models, preventing over-optimization and generating …

Geometric Diffusion Bridge (GDB) accurately predicts geometric state evolution in complex systems by leveraging a probabilistic approach and equivariant diffusion processes, surpassing existing deep l…

FMCSC: A novel federated multi-view clustering framework bridging client and view gaps in heterogeneous hybrid views, achieving superior performance through local-synergistic contrastive learning and …

Researchers break the curse of dimensionality in structured density estimation using graph resilience, a novel graphical parameter that effectively reduces the sample complexity.

ACON: Adversarial CO-learning Networks enhances time series domain adaptation by cleverly combining temporal and frequency features. Frequency features boost within-domain discriminability, while temp…

Equivariant Graph Neural Networks boost multi-agent reinforcement learning by improving sample efficiency and generalization, overcoming inherent exploration biases.

Boosting graph neural networks: Topology-Invariant Pooling (TIP) leverages persistent homology to enhance graph pooling, achieving consistent performance gains across diverse datasets.

GEPS enhances parametric PDE solver generalization by using adaptive conditioning, achieving superior performance with limited data.

Boosting conformal prediction intervals improves accuracy and precision by tailoring them to specific desired properties via machine learning.

Diversified Block Sparse Bayesian Learning (DivSBL) improves block sparse signal recovery by adapting to unknown block structures, enhancing accuracy and robustness over existing methods.

Lifelong learning in linear bandits gets a boost! A new algorithm, BOSS, achieves low regret without the usual ‘task diversity’ assumption, opening doors for more realistic sequential multi-task lear…

Researchers drastically sped up high-fidelity deep learning model extraction, improving efficiency by up to 14.8x and challenging previous assumptions on the extraction bottleneck.

InfoMGF, a novel framework, tackles the limitations of unsupervised multiplex graph learning by refining graph structures, maximizing task-relevant information (both shared and unique), and achieving …

MolPeg, a novel molecular data pruning framework, enhances model generalization in transfer learning by using a source-free approach and consistently outperforming other methods, even surpassing full-…

Strong pre-tuned MLPs and meta-tuned default parameters for GBDTs and MLPs improve tabular data classification and regression.

BECAUSE: a novel algorithm for generalizable offline model-based reinforcement learning that leverages bilinear causal representation to mitigate objective mismatch caused by confounders in offline da…