Posters

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

Deep learning’s generalization ability defies conventional wisdom; this paper proves that overfitting in deep neural networks is ’tempered’, neither catastrophic nor perfectly benign, for both minimal…

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…

PREPARED efficiently edits DNNs to provably satisfy properties by relaxing the problem to a linear program, minimizing parameter changes.

Complex numbers boost neural network performance! This study proves that complex parameterizations in structured state space models (SSMs) enable more efficient and practical learning of complex mappi…

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

This paper proves Nesterov’s Accelerated Gradient achieves faster convergence for rectangular matrix factorization and linear neural networks, using a novel unbalanced initialization.

ProTransformer robustifies transformers with a novel plug-and-play attention mechanism, significantly improving robustness across various tasks and domains without retraining.

Prototypical Hash Encoding (PHE) significantly boosts on-the-fly fine-grained category discovery by using multiple prototypes per category to generate highly discriminative hash codes, thus resolving …

ProtGO: A novel unified framework integrating protein sequence, structure & function for superior representation learning, significantly outperforming current methods.

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

IBProtector shields LLMs from harmful outputs via prompt compression, selectively preserving essential information using a trainable extractor.

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.

This paper introduces proportionally fair non-centroid clustering, achieving fairness guarantees via novel algorithms and auditing methods, demonstrating significant improvements over traditional meth…

This paper reveals the surprising connection between individual and proportional fairness in clustering, showing that any approximation to one directly implies an approximation to the other, enabling …

Unlocking multimodal learning’s potential with propensity scores: This novel approach aligns unpaired data across modalities, significantly improving representation learning.