Publications

Code as Reward: Empowering Reinforcement Learning with VLMs
David Venuto
Mohammad Sami Nur Islam
Martin Klissarov
Doina Precup
Sherry Yang
Ankit Anand
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net
A Distributional Analogue to the Successor Representation
Harley Wiltzer
Jesse Farebrother
Arthur Gretton
Yunhao Tang
Andre Barreto
Will Dabney
Marc Gendron-Bellemare
Mark Rowland
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure … (see more)and reward in the learning process. Analogous to how the successor representation (SR) describes the expected consequences of behaving according to a given policy, our distributional successor measure (SM) describes the distributional consequences of this behaviour. We formulate the distributional SM as a distribution over distributions and provide theory connecting it with distributional and model-based reinforcement learning. Moreover, we propose an algorithm that learns the distributional SM from data by minimizing a two-level maximum mean discrepancy. Key to our method are a number of algorithmic techniques that are independently valuable for learning generative models of state. As an illustration of the usefulness of the distributional SM, we show that it enables zero-shot risk-sensitive policy evaluation in a way that was not previously possible.
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net
Fairness-aware data-driven-based model predictive controller: A study on thermal energy storage in a residential building
Ying Sun
Fariborz Haghighat
Benjamin Fung
2024-05-01
Journal of Energy Storage (published)
doi.org
Faithfulness Measurable Masked Language Models
Andreas Madsen
Siva Reddy
Sarath Chandar Anbil Parthipan
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net
Generative AI in Software Engineering Must Be Human-Centered: The Copenhagen Manifesto
Daniel Russo
Sebastian Baltes
Niels van Berkel
Paris Avgeriou
Fabio Calefato
Beatriz Cabrero-Daniel
Gemma Catolino
Jürgen Cito
Neil Ernst
Thomas Fritz
Hideaki Hata
Reid Holmes
Maliheh Izadi
Foutse Khomh
Mikkel Baun Kjærgaard
Grischa Liebel
Alberto Lluch Lafuente
Stefano Lambiase
Walid Maalej
Gail Murphy … (see 15 more)
Nils Brede Moe
Gabrielle O'Brien
Elda Paja
Mauro Pezzè
John Stouby Persson
Rafael Prikladnicki
Paul Ralph
Martin P. Robillard
Thiago Rocha Silva
Klaas-Jan Stol
Margaret-Anne Storey
Viktoria Stray
Paolo Tell
Christoph Treude
Bogdan Vasilescu
2024-05-01
Journal of Systems and Software (published)
doi.org
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Eugene Belilovsky
Guy Wolf
Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (see more)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
2024-05-01
ICML.cc/2024/Conference (poster)
openreview.net
openreview.net
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization
Idan Attias
Gintare Karolina Dziugaite
MAHDI HAGHIFAM
Roi Livni
Daniel M. Roy
In this work, we investigate the interplay between memorization and learning in the context of \emph{stochastic convex optimization} (SCO). … (see more)We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke and Zakynthinou (2020). Our main result is a precise characterization of the tradeoff between the accuracy of a learning algorithm and its CMI, answering an open question posed by Livni (2023). We show that, in the
2024-05-01
ICML.cc/2024/Conference (oral)
doi.org
openreview.net
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh
Jarrid Rector-Brooks
Joey Bose
Sarthak Mittal
Pablo Lemos
Cheng-Hao Liu
Marcin Sendera
Siamak Ravanbakhsh
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Alexander Tong
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (see more)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient---and no data samples---to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is *simulation-free*, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim
Joohwan Ko
Dinghuai Zhang
Ling Pan
Taeyoung Yun
Woo Chang Kim
Jinkyoo Park
Emmanuel Bengio
Yoshua Bengio
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, tempera… (see more)ture-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. Also, using Logit-GFN, GFlowNets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. Our code is available at https://github.com/dbsxodud-11/logit-gfn
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, … (see more)however, leverage expensive attention mechanisms, limiting their applications, particularly in low-resource settings. In this work, we propose Constant Memory Attention Block (CMAB), a novel general-purpose attention block that (1) is permutation invariant, (2) computes its output in constant memory, and (3) performs updates in constant computation. Building on CMAB, we propose Constant Memory Attentive Neural Processes (CMANPs), an NP variant which only requires \textbf{constant} memory. Empirically, we show CMANPs achieve state-of-the-art results on popular NP benchmarks (meta-regression and image completion) while being significantly more memory efficient than prior methods.
2024-05-01
ICML.cc/2024/Conference (poster)
openreview.net
openreview.net
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Samir Obando Ceron
Ghada Sokar
Timon Willi
Clare Lyle
Jesse Farebrother
Jakob Nicolaus Foerster
Gintare Karolina Dziugaite
Doina Precup
Pablo Samuel Castro
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance s… (see more)cales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Côme Fiegel
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Traditionally, RLHF involves the initial step of learning a reward model from pairwise human feedback, i.e., expressed as preferences between pairs of text generations. Subsequently, the LLM’s policy is fine-tuned to maximize the reward through a reinforcement learning algorithm. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a pairwise preference model, which is conditioned on two inputs (instead of a single input in the case of a reward model) given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. We illustrate the effectiveness of our approach by presenting experimental results on a text summarization task. We believe NLHF offers a compelling avenue for fine-tuning LLMs and enhancing the alignment of LLMs with human preferences.
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net