Posters

This research introduces a novel framework that overcomes the brittleness of Pareto-optimal learning-augmented algorithms by enforcing smoothness in performance using user-specified profiles and devel…

Over-parameterized Distillation Framework (OPDF) boosts knowledge distillation by efficiently over-parameterizing student models via tensor decomposition, significantly improving performance without i…

Outlier-robust distributionally robust optimization achieved via a novel Unbalanced Optimal Transport (UOT) distance, improving efficiency and accuracy.

Boost OOD detection accuracy with diverseMix: a novel method enhancing auxiliary outlier diversity, provably improving generalization and achieving state-of-the-art results.

Single diffusion model achieves competitive out-of-distribution detection across diverse tasks by analyzing diffusion path characteristics.

OTTER effortlessly adapts zero-shot models to new tasks by adjusting predictions using optimal transport, improving accuracy significantly without extra training data.

OT4P: a novel temperature-controlled differentiable transformation efficiently relaxes permutation matrices onto the orthogonal group for gradient-based optimization.

One-shot label-only attack (OSLO) achieves high membership inference accuracy with only one query, surpassing existing methods by a large margin.

Causal discovery algorithm CaPS efficiently handles mixed linear and nonlinear relationships in observational data, outperforming existing methods on synthetic and real-world datasets.

Ordered Momentum (OrMo) significantly boosts asynchronous stochastic gradient descent (ASGD) convergence by cleverly incorporating momentum, resolving prior convergence issues. This novel approach is…

Set-Based Prompting guarantees order-independent LLM outputs by modifying input representations, eliminating unwanted inconsistencies without fine-tuning.

Orchid: a novel deep learning architecture using data-dependent convolution achieves quasilinear scalability and outperforms attention-based models on various sequence modeling tasks.

Boost RL performance in large state spaces by efficiently learning a policy competitive with the best combination of existing base policies!

This paper introduces computationally efficient algorithms for differentially private learning by leveraging public data, overcoming previous computational limitations and enabling broader practical a…

OPUS: a novel, real-time occupancy prediction framework using a sparse set prediction paradigm, outperforms state-of-the-art methods on Occ3D-nuScenes.

Optimus-1: Hybrid Multimodal Memory empowers AI agents to excel in complex, long-horizon tasks by integrating hierarchical knowledge graphs and multimodal experience for superior planning and reflecti…

Two novel algorithms, Local-Greedy and Greedy-Grid, optimize coalition gain in online auctions with limited observations, achieving constant regret and problem-independent guarantees while respecting …

This paper proposes ‘generalized quasar-convexity’ to optimize problems with multiple probability distributions, offering adaptive algorithms with superior iteration complexities compared to existing …

LLMs boost tabular data prediction by generating optimized features via decision tree reasoning, outperforming existing methods.

Optimization can learn optimal Johnson-Lindenstrauss embeddings, avoiding the limitations of randomized methods and achieving comparable theoretical guarantees.