2 citations · 10 across the 32 of their papers we have counts for
9 papers · 1 filter
StabilityBench: Benchmarking Instability in LLMs
Emma Kondrup, Zachary Yang, Anne Imouza +1
AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior remains poorly understood due to strong co…
Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
Victor Livernoche, Jie Zan, Reihaneh Rabbany
Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the gradient of the log density, fr…
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…
RL Fine-Tuning Heals OOD Forgetting in SFT
Hangzhan Jin, Sitao Luan, Tianwei Ni +5
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (LLM) reasoning, but why it works remain…
Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data
Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing mul…
Higher-Order Transformers With Kronecker-Structured Attention
Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
Modern datasets are increasingly high-dimensional and multiway, often represented as tensor-valued data with multi-indexed variables. While Transformers excel in sequence modeling…
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany +2
Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal graphs, either through a stream of edge…
MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs
Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani +7
Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely con…
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger, Shenyang Huang, Mikhail Galkin +9
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel model…