Machine Learning

f-DIME: a novel class of discriminative mutual information estimators using f-divergence outperforms state-of-the-art methods by achieving an excellent bias-variance trade-off. This is achieved throug…

Boost multivariate time series forecasting accuracy by efficiently learning the complex correlation structure of prediction errors, enhancing reliability without expanding model size.

Multiple Physics Pretraining (MPP) revolutionizes spatiotemporal physical surrogate modeling by pretraining transformers on diverse physics simultaneously, enabling accurate predictions on unseen syst…

Multidimensional Fractional Programming (MFP) efficiently solves the challenging Normalized Cut (NCut) problem for multi-class clustering, outperforming existing methods.

Multi-Stage Predict+Optimize tackles optimization problems where parameters are revealed sequentially, improving predictions and decisions through stage-wise updates.

S3F: a novel multi-scale model achieves state-of-the-art protein fitness prediction by integrating protein sequence, structure, and surface features.

This paper introduces efficient algorithms, MR-NaS and DBMR-BPI, for identifying optimal policies across multiple reward functions in reinforcement learning, achieving competitive performance with the…

Adaptive multi-model ensemble conformal prediction achieves strongly adaptive regret, yielding more efficient prediction sets in dynamic environments.

SLAN: A novel approach for multi-label open-set recognition, enriching sub-labeling info using structural data to identify unknown labels.

Novel surrogate losses with label-independent H-consistency bounds enable stronger guarantees for multi-label learning.

MIPLMA, a novel algorithm, enhances multi-instance partial-label learning by dynamically adjusting margins for attention scores and predicted probabilities, leading to superior performance.

Madoc: A novel multi-agent framework calibrates RL policies for new environments using limited offline data, achieving superior performance in various locomotion tasks.

SeqComm, a novel multi-level communication scheme, tackles multi-agent coordination by leveraging asynchronous decision-making and a two-phase communication process for improved efficiency and theoret…

MSAGPT: Revolutionizing protein structure prediction by generating accurate virtual MSAs from limited data, boosting prediction accuracy by up to +8.5% TM-Score!

Self-supervised generative model MSA-Generator boosts protein structure prediction accuracy by producing high-quality MSAs, especially for challenging sequences lacking homologs.

MOTE-NAS: A new multi-objective training-based estimate drastically improves neural architecture search efficiency, achieving state-of-the-art accuracy with significantly reduced costs.

Monomial-NFNs boost neural network efficiency by leveraging scaling/sign-flipping symmetries, resulting in fewer trainable parameters and competitive performance.

f-RAG: A novel fragment-based molecular generation framework boosts drug discovery by combining retrieval augmentation with a generative model, enabling exploration beyond existing fragments and signi…

gpSLDS, a novel model, balances expressiveness and interpretability in modeling complex neural dynamics by combining Gaussian processes with switching linear dynamical systems, improving accuracy and …

LoRa-PI: a model-free RL algorithm learns and exploits low-rank MDP structures for order-optimal sample complexity, achieving ε-optimal policies with O(poly(A)) samples.