Posters

This paper introduces a new message-passing operation for coarsened graphs with theoretical guarantees, improving GNN efficiency and accuracy on large datasets.

GRDL: a novel graph classification method boasting 10x speed improvement over competitors, achieved by treating node embeddings as distributions and avoiding global pooling.

GRANOLA: A novel graph-adaptive normalization layer significantly boosts GNN performance by dynamically adjusting node features based on the input graph’s unique structure.

Adaptive Sampling with Approximate Expected Futures (ASAp) ensures LLMs generate grammatically correct outputs that closely match the model’s original probability distribution.

This paper introduces DRODA, a novel method guaranteeing bounded error in gradual domain adaptation by leveraging manifold constraints and adaptive Wasserstein radii.

This paper presents a novel optimistic mirror descent algorithm achieving optimal gradient-variation regret under generalized smoothness, applicable across convex, strongly convex functions, and fast-…

Gradient-free methods conquer nonconvex nonsmooth stochastic compositional optimization, providing non-asymptotic convergence rates and improved efficiency for real-world applications.

This paper introduces a novel gradient-free decoder inversion method for latent diffusion models, improving efficiency and memory usage compared to existing gradient-based methods. The method is theo…

ACS: Automatic Cyclical Scheduling revolutionizes gradient-based discrete sampling by intelligently switching between exploration and exploitation phases to efficiently navigate complex multimodal dis…

Gradient Rewiring (GRE) improves editable GNN training by addressing gradient inconsistencies, preserving training node performance while correcting target node errors.

Novel gradient-based algorithms achieve O(√T) regret and O(T3/4) constraint violation for online DR-submodular maximization with stochastic long-term constraints.

This paper provides a novel optimization framework for guided diffusion models, proving Õ(1/K) convergence for concave objective functions and demonstrating structure-preserving guidance.

Gradient Cuff: A novel defense mechanism against LLM jailbreaks, leveraging refusal loss landscapes for improved malicious query rejection without harming model performance on benign inputs.

Gorilla: a fine-tuned LLaMA model surpasses GPT-4 in generating accurate API calls by using Retriever Aware Training (RAT) to adapt to changing APIs and reduce hallucinations.

GoMatching, a novel video text spotting baseline, enhances tracking efficiency while maintaining strong recognition by integrating long- and short-term matching via a Transformer-based module and a re…

GOMAA-Geo, a novel framework, enables efficient and accurate goal localization using aerial imagery, regardless of goal description modality (text or images), demonstrating impressive zero-shot genera…

HEPO, a novel constrained optimization method, consistently surpasses heuristic-trained policies in reinforcement learning by ensuring policy improvement over heuristics, regardless of heuristic quali…

GCPO: a novel on-policy goal-conditioned reinforcement learning framework tackles limitations of existing HER-based methods by effectively addressing multi-goal Markovian and non-Markovian reward prob…

This paper introduces a goal-conditioned reinforcement learning approach that efficiently tunes photo finishing pipelines, achieving high-quality results in fewer iterations than optimization-based me…

GO4Align: Dynamically aligning multi-task learning to conquer task imbalance with superior efficiency!