Machine Learning

Boost time-series model accuracy with Segment, Shuffle, and Stitch (S3)! This simple layer shuffles data segments to enhance representation learning, improving classification, forecasting, and anomaly…

Dissected Dynamics Modeling (DDM) excels at multi-modal visual reinforcement learning by cleverly separating and integrating common and unique features across different sensory inputs for more accurat…

Revolutionizing large neural networks, this paper introduces a continuous parameterization of structured matrices, discovering that full-rank structures without parameter sharing achieve optimal scali…

Researchers developed a novel algorithm to automatically find optimal schedules for denoising diffusion models (DDMs), significantly improving sample quality and efficiency without manual parameter tu…

FuncMol: A new neural field model generates 3D molecules efficiently, outperforming existing methods by achieving an order of magnitude faster sampling speed.

TRODO: a novel trojan detection method using out-of-distribution samples, effectively identifies trojaned classifiers even against adversarial attacks and with limited data.

Stormer, a simple transformer model, achieves state-of-the-art medium-range weather forecasting accuracy by using weather-specific embedding, randomized dynamics forecasting, and a pressure-weighted l…

Boost machine learning with surrogate data! A novel weighted ERM method effectively integrates surrogate data, significantly reducing test errors even with unrelated data, guided by a predictable sca…

Unlocking the potential of deep learning for time series forecasting: this study reveals a scaling law influenced by dataset size, model complexity, and the crucial look-back horizon, leading to impro…

Scale-invariant optimal subsampling tackles computational challenges in analyzing massive rare-events data with sparse models, enhancing parameter estimation and variable selection without being affec…

Deep learning optimization gets a major upgrade with Modula, a new method that uses the modular norm to normalize weight updates, enabling learning rate transfer across network widths and depths, thus…

Scalable Kernel Inverse Optimization (KIO) efficiently learns unknown objective functions from data using kernel methods and a novel Sequential Selection Optimization (SSO) algorithm, enabling applica…

sDBSCAN: Blazing-fast density-based clustering for massive datasets using random projections!

Scalable MAPPO-L: Decentralized training with local interactions ensures safe, high-reward multi-agent systems, even with limited communication.

FOCALBO, a hierarchical Bayesian optimization algorithm using focalized sparse Gaussian processes, efficiently tackles high-dimensional problems with massive datasets, achieving state-of-the-art perfo…

SCAFFLSA tames heterogeneity in federated learning, achieving logarithmic communication complexity and linear sample complexity.

SARAD: A novel anomaly detection approach for multivariate time series leverages spatial information and association reduction patterns to achieve state-of-the-art performance.

SAND, a novel transformer network variant, smoothly imputes sparse and noisy functional data by leveraging self-attention on derivatives, outperforming existing methods.

Accelerated Primal-Dual Natural Policy Gradient (PD-ANPG) algorithm achieves a theoretical lower bound sample complexity for solving general parameterized CMDPs, improving state-of-the-art by a factor…

Agnostic boosting gets a major efficiency upgrade! A new algorithm leverages sample reuse to drastically reduce the data needed for accurate learning, closing the gap with computationally expensive al…