Olivier Teytaud — accepted AI-conference papers

2025

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets

Mathurin VIDEAU, Badr Youbi Idrissi, Alessandro Leite, Marc Schoenauer, Olivier Teytaud, David Lopez-Paz

NeurIPS 2025poster

Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidit…

Cited by 0 Source ↗code ↗Scholar ↗

2020

Adversarial Attacks on Linear Contextual Bandits

Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric +1

NeurIPS 2020poster

Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious agents may have incentives to force a bandit algorithm into a desired behavior For instance, an unscrupulous ad publish…

Cited by 68 Source ↗PDF ↗Scholar ↗

2020

Fully Parallel Hyperparameter Search: Reshaped Space-Filling

Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jeremy Rapin, Morgane Riviere +3

ICML 2020poster

Space-filling designs such as Low Discrepancy Sequence (LDS), Latin Hypercube Sampling (LHS) and Jittered Sampling (JS) were proposed for fully parallel hyperparameter search, and were shown to be more effective than random and grid search. We prove that LHS and JS outperform random search only by a…

Cited by 30 Source ↗PDF ↗Scholar ↗