Olivier Bachem
Olivier is a research scientist in the Google Brain Team interested in fundamental problems in machine learning and artificial intelligence. He received his PhD from ETH Zurich where he was supervised by Andreas Krause in the Learning & Adaptive Systems group. In his dissertation, he investigated coresets - small summaries of large data sets with theoretical guarantees - and other sampling methods for large-scale clustering. He also held a Google PhD Fellowship in Machine Learning and was an Associated Fellow at the Max Planck ETH Center for Learning Systems. Before that, he obtained a bachelor’s degree in economics (University of St. Gallen), a master’s degree in quantitative finance (ETH Zurich & University of Zurich) as well as a master’s degree in statistics (ETH Zurich) where he was awarded an ETH medal for his master thesis.
Authored Publications
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Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit
Johan Ferret
Lior Shani
Geoffrey Cideron
Matthieu Geist
Sertan Girgin
Léonard Hussenot
Nikola Momchev
Piotr Stanczyk
Nino Vieillard
Olivier Pietquin
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics(2023), 6252–6272
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Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization, in which the generated summaries should be corroborated by their source article. In this work we leverage recent progress on textual entailment models to directly address this problem for abstractive summarization systems. We use reinforcement learning with reference-free, textual-entailment rewards to optimize for factual consistency and explore the ensuing trade-offs, as improved consistency may come at the cost of less informative or more extractive summaries. Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience and conciseness of the generated summaries.
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Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
Matthieu Geist
Julien Perolat
Mathieu Laurière
Romuald Elie
Sarah Perrin
Remi Munos
Olivier Pietquin
AAMAS(2022)
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Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities in the occupancy measure induced by the agent's policy. This encompasses not only RL but also imitation learning and exploration, among others. Yet, this more general paradigm invalidates the classical Bellman equations, and calls for new algorithms. Mean-field Games (MFGs) are a continuous approximation of many-agent RL. They consider the limit case of a continuous distribution of identical agents, anonymous with symmetric interests, and reduce the problem to the study of a single representative agent in interaction with the full population. Our core contribution consists in showing that CURL is a subclass of MFGs. We think this important to bridge together both communities. It also allows to shed light on aspects of both fields: we show the equivalence between concavity in CURL and monotonicity in the associated MFG, between optimality conditions in CURL and Nash equilibrium in MFG, or that Fictitious Play (FP) for this class of MFGs is simply Frank-Wolfe, bringing the first convergence rate for discrete-time FP for MFGs. We also experimentally demonstrate that, using algorithms recently introduced for solving MFGs, we can address the CURL problem more efficiently.
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Decoding A Neural Retriever's Latent Space for Query Suggestion
Leonard Adolphs
Michelle Chen Huebscher
Sertan Girgin
Thomas Hofmann
EMNLP(2022)
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Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-words models; it is not trivial to connect a query change to a change in the latent space that ultimately determines the retrieval results. To shed light on this embedding space, we learn a "query decoder" that, given a latent representation of a neural search engine, generates the corresponding query. We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. In particular, the query decoder can be useful to understand "what should have been asked" to retrieve a particular paragraph from the collection. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco, leading to improved retrieval performance. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion that outperforms both query reformulation and PRF information retrieval baselines.
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Offline Reinforcement Learning as Anti-Exploration
Shideh Rezaeifar
Nino Vieillard
Léonard Hussenot
Olivier Pietquin
Matthieu Geist
AAAI(2022)
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Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. An agent in this setting should avoid selecting actions whose consequences cannot be predicted from the data. This is the converse of exploration in RL, which favors such actions. We thus take inspiration from the literature on bonus-based exploration to design a new offline RL agent. The core idea is to subtract a prediction-based exploration bonus from the reward instead of adding it for exploration. This allows the policy to stay close to the support of the dataset. We connect this approach to a more usual regularization of the learnt policy towards the data. Instantiated with a bonus based on the prediction error of a variational autoencoder, we show that our agent is competitive with the state of the art on a set of continuous control locomotion and manipulation tasks.
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A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani
Simone Totaro
Robert Müller
Shivam Garg
Matthieu Geist
Marlos C. Machado
Nicolas Le Roux
AISTATS(2022)
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Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong theoretical guarantees, leading to algorithms such as TRPO, PPO, or MPO. Rather than design yet another surrogate function, we instead propose a general framework (FMA-PG) based on functional mirror ascent that gives rise to an entire family of surrogate functions. We construct surrogate functions that enable policy improvement guarantees, a property not shared by most existing surrogate functions. Crucially, these guarantees hold regardless of the choice of policy parameterization. Moreover, a particular instantiation of FMA-PG recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) resulting in a variant of TRPO with additional desirable properties. Via experiments on simple reinforcement learning problems, we evaluate the algorithms instantiated by FMA-PG. The proposed framework also suggests an improved variant of PPO, whose robustness and efficiency we empirically demonstrate on the MuJoCo suite.
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What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz
Piotr Michal Stanczyk
Manu Orsini
Sertan Girgin
Léonard Hussenot
Matthieu Geist
Olivier Pietquin
Marcin Michalski
Sylvain Gelly
ICLR(2021)
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In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``"choices" in a unified on-policy deep actor-critic framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for the training of on-policy deep actor-critic RL agents.
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What Matters for Adversarial Imitation Learning?
Manu Orsini
Léonard Hussenot
Damien Vincent
Sertan Girgin
Matthieu Geist
Olivier Pietquin
Marcin Andrychowicz
NeurIPS(2021)
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Adversarial imitation learning has become a standard framework for imitation in continuous control. Over the years, several variations of its components were proposed to enhance the performance of the learned policies as well as the sample complexity of the algorithm. In practice, many of these choices are rarely tested all together in rigorous empirical studies. It is therefore difficult to discuss and understand what choices, among the high-level algorithmic options as well as low-level implementation details, matter.
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations. We analyze the key results and highlight the most surprising findings.
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Hyperparameter Selection for Imitation Learning
Léonard Hussenot
Marcin Andrychowicz
Damien Vincent
Lukasz Piotr Stafiniak
Sertan Girgin
Nikola M Momchev
Manu Orsini
Matthieu Geist
Olivier Pietquin
ICML(2021)
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We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms when the underlying reward function of the demonstrating expert cannot be observed at any time. The vast literature in imitation learning mostly considers this reward function to be available for HP selection, although this is not a realistic setting. Indeed, would this reward function be available, it should then directly be used for policy training and imitation would not make sense. To tackle this mostly ignored problem, we propose and study, for different representative agents and benchmarks, a number of possible proxies to the return, within an extensive empirical study. We observe that, depending on the algorithm and the environment, some methods allow good performance to be achieved without using the unknown return.
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Evaluating Generative Models using Divergence Frontiers
Josip Djolonga
Marco Cuturi
Sylvain Gelly
International Conference on Artificial Intelligence and Statistics(2020)
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Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace. Very recent developments have investigated metrics that quantify which parts of the true distribution is well modeled, and, on the contrary, what the model fails to capture, akin to precision and recall in information retrieval. In this paper we present a general evaluation framework for generative models that measures the trade-off between precision and recall using R\'enyi divergences. Our framework provides a novel perspective on existing techniques and extends them to more general domains.
As a key advantage, it allows for efficient algorithms that are directly applicable to continuous distributions directly without discretization. We further showcase the proposed techniques on a set of image synthesis models.
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A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Gunnar Rätsch
Sylvain Gelly
Bernhard Scholkopf
AAAI Conference on Artificial Intelligence(2020)
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The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of (Locatello et al. 2019b) and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.
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