Machine Learning

Provably robust diffusion posterior sampling for plug-and-play image reconstruction is achieved via a novel algorithmic framework, DPnP, offering both asymptotic and non-asymptotic performance guarant…

Faster bilevel optimization is achieved via without-replacement sampling, improving convergence rates compared to independent sampling methods.

This paper presents novel RL algorithms using multinomial logit function approximation, achieving O(1) computation and storage while nearly closing the regret gap with linear methods.

Provably efficient algorithms are introduced for interaction-grounded learning (IGL) with context-dependent feedback, addressing the lack of theoretical guarantees in existing approaches for personali…

OPT-AIL: Provably efficient adversarial imitation learning with general function approximation, achieving polynomial sample and interaction complexity, outperforming existing deep AIL methods.

Boosting posterior sampling in challenging high-dimensional inverse problems, this paper introduces ’tilted transport’, a novel technique leveraging denoising oracles for provably easier sampling.

This paper provides the first provable efficiency guarantees for practically-used RL algorithms leveraging privileged information, addressing limitations of previous empirical paradigms and opening ne…

One data point per class suffices for efficient and provable dataset distillation in kernel ridge regression, significantly reducing computational costs.

Unsupervised learning predicts protein-nucleic acid binding using contact map prediction, significantly improving aptamer screening via FAFormer, a novel equivariant transformer.

POEM: a novel test-time adaptation approach using online self-training improves accuracy under distribution shifts by dynamically updating the classifier, ensuring invariance to shifts while maintaini…

ProSST, a novel protein language model, integrates protein sequences and structures using quantized structure representation and disentangled attention, achieving state-of-the-art performance in zero-…

Prospective Representation Learning (PRL) revolutionizes non-exemplar class-incremental learning by proactively reserving embedding space for new classes and minimizing the shock of new data on previo…

Prospective Learning: a new framework enabling machines to learn effectively in dynamic environments where data distributions and goals shift over time.

Progressive Entropic Optimal Transport (PROGOT) solvers efficiently and robustly compute optimal transport plans and maps, even at large scales, by progressively scheduling parameters.

This study introduces a novel Bayesian functional mixed model that reliably recovers the size and shape of fixed effects from noisy functional data with phase variability, outperforming current state-…

IPR-MPNNs revolutionize graph neural networks by implicitly rewiring graphs using virtual nodes, achieving state-of-the-art performance with significantly faster computation.

Probabilistic Federated Prompt Tuning (PFPT) significantly improves federated learning accuracy on heterogeneous and imbalanced data by using a probabilistic model for prompt aggregation, outperformin…

Probabilistic Decomposed Linear Dynamical Systems (p-dLDS) improve latent variable inference in nonlinear neural systems by using a probabilistic approach that’s robust to noise and includes a time-va…

This paper introduces a novel privacy-preserving algorithm for personalized frequency estimation in federated settings, significantly improving accuracy and efficiency over existing methods.

Orthogonal regularization prevents dimensional collapse in self-supervised learning, significantly boosting model performance across diverse benchmarks.