1 citations · 1 across the 10 of their papers we have counts for
11 papers
Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks
Xinming Tu, Tianze Wang, Yingzhou +4
As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all---they are failures of the benchmark itself: broken specifications, implicit ass…
Be Wary of Your Time Series Preprocessing
Sofiane Ennadir, Tianze Wang, Oleg Smirnov +2
Normalization and scaling are fundamental preprocessing steps in time series modeling, yet their role in Transformer-based models remains underexplored from a theoretical perspecti…
Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
Tianze Wang, Sofiane Ennadir, John Pertoft +7
Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial se…
Secret-Protected Evolution for Differentially Private Synthetic Text Generation
Tianze Wang, Zhaoyu Chen, Jian Du +3
Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI). A lot of high-quality text in the real world is priv…
Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models
Sofiane Ennadir, Levente Zólyomi, Oleg Smirnov +4
Transformer models have become the dominant backbone for sequence modeling, leveraging self-attention to produce contextualized token representations. These are typically aggregate…
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Tianci Liu, Haoxiang Jiang, Tianze Wang +5
Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. In cont…
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment
Tianze Wang, Dongnan Gui, Yifan Hu +2
Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning large language models (LLMs). Yet its reliance on a singular reward model often overlooks the divers…
Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects
Levente Zólyomi, Tianze Wang, Sofiane Ennadir +2
The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the generation of vast volumes of event strea…
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell +6
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning…
Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou +5
This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile ga…
player2vec: A Language Modeling Approach to Understand Player Behavior in Games
Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou +4
Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. H…