1 citations · 1 across the 16 of their papers we have counts for
19 papers
Considering Context: When World Models Need Context Encoders
Oleg Smirnov, Sofiane Ennadir, John Pertoft +2
Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environment dynamics from its own ex…
Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance
Jayasimha Talur, Oleg Smirnov, Paul Missault
Conversational agents like chatbots and voice assistants are trained to understand and respond to user intents. On encountering an utterance with an intent different from the ones…
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…
The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents
Oleg Smirnov
Large language models are increasingly used to interpret politically contested questions, value-laden material on which there is no single correct answer, only competing interpreti…
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…
Enhancing Graph Classification Robustness with Singular Pooling
Sofiane Ennadir, Oleg Smirnov, Yassine Abbahaddou +2
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remain…
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…
Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction
Filip Cornell, Oleg Smirnov, Gabriela Zarzar Gandler +1
Recent work has questioned the reliability of graph learning benchmarks, citing concerns around task design, methodological rigor, and data suitability. In this extended abstract,…
On the Power of Heuristics in Temporal Graphs
Filip Cornell, Oleg Smirnov, Gabriela Zarzar Gandler +1
Dynamic graph datasets often exhibit strong temporal patterns, such as recency, which prioritizes recent interactions, and popularity, which favors frequently occurring nodes. We d…
Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompting has emerged as the dominant paradigm for adapting large, pre-trained transformer-based models to downstream tasks. The Prompting Decision Transformer (PDT) enables large-…
Prompt Tuning Decision Transformers with Structured and Scalable Bandits
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompt tuning has emerged as a key technique for adapting large pre-trained Decision Transformers (DTs) in offline Reinforcement Learning (RL), particularly in multi-task and few-s…
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…
Behavior Structformer: Learning Players Representations with Structured Tokenization
Oleg Smirnov, Labinot Polisi
In this paper, we introduce the Behavior Structformer, a method for modeling user behavior using structured tokenization within a Transformer-based architecture. By converting trac…
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
Zineb Senane, Axel Karlsson, Lele Cao +6
Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pa…
Kotlin ML Pack: Technical Report
Sergey Titov, Mikhail Evtikhiev, Anton Shapkin +7
In this technical report, we present three novel datasets of Kotlin code: KStack, KStack-clean, and KExercises. We also describe the results of fine-tuning CodeLlama and DeepSeek m…
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…
Rooms with Text: A Dataset for Overlaying Text Detection
Oleg Smirnov, Aditya Tewari
In this paper, we introduce a new dataset of room interior pictures with overlaying and scene text, totalling to 4836 annotated images in 25 product categories. We provide details…