Publications

PAC-Bayesian Generalization Bounds for Adversarial Generative Models
Sokhna Diarra Mbacke
Florence Clerc
Pascal Germain
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo
Kamalika Chaudhuri
Pierre Stock
Michael G. Rabbat
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Étienne Marcotte
Valentina Zantedeschi
Alexandre Drouin
Nicolas Chapados
Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expect… (voir plus)ation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the"region of reliability"of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Robust Perception through Equivariance
Chengzhi Mao
Lingyu Zhang
Abhishek Vaibhav Joshi
Junfeng Yang
Hao Wang
Carl Vondrick
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Daniel D. Johnson
Daniel Tarlow
Christian Walder
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Target-based Surrogates for Stochastic Optimization
Jonathan Wilder Lavington
Sharan Vaswani
Reza Babanezhad Harikandeh
Mark Schmidt
Nicolas Roux
We consider minimizing functions for which it is expensive to compute the gradient. Such functions are prevalent in reinforcement learning, … (voir plus)imitation learning and bilevel optimization. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
Ghada Sokar
Rishabh Agarwal
Pablo Samuel Castro
Utku Evci
In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing n… (voir plus)umber of inactive neurons, thereby affecting network expressivity. We demonstrate the presence of this phenomenon across a variety of algorithms and environments, and highlight its effect on learning. To address this issue, we propose a simple and effective method (ReDo) that Recycles Dormant neurons throughout training. Our experiments demonstrate that ReDo maintains the expressive power of networks by reducing the number of dormant neurons and results in improved performance.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland
Yunhao Tang
Clare Lyle
Remi Munos
Bellemare Marc-Emmanuel
Will Dabney
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Towards Reliable Neural Specifications
Chuqin Geng
Nham Le
Xiaojie Xu
Zhaoyue Wang
Arie Gurfinkel
Xujie Si
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Andreas Munk
Alexander Mead
Frank N. Wood
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred… (voir plus) to as "uncertain evidence.'' We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed method "distributional evidence'' as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency. To showcase the impact of different interpretations of the same uncertain evidence, we carry out experiments in which one interpretation is defined as "correct.'' We then compare inference results from each different interpretation illustrating the importance of careful consideration of uncertain evidence.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Chat2Code: A Chatbot for Model Specification and Code Generation, The Case of Smart Contracts
Ilham Qasse
Shailesh Mishra
Björn þór Jónsson
Foutse Khomh
Mohammad Hamdaqa
The potential of automatic code generation through Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to… (voir plus) help software professionals write more accurate, reusable code, MDE frameworks could make programming accessible for a new class of domain experts. However, domain experts have been slow to embrace these tools, as they still need to learn how to specify their applications' requirements using the concrete syntax (i.e., textual or graphical) of the new and unified domain-specific language. Conversational interfaces (chatbots) could smooth the learning process and offer a more interactive way for domain experts to specify their application requirements and generate the desired code. If integrated with MDE frameworks, chatbots may offer domain experts with richer domain vocabulary without sacrificing the power of agnosticism that unified modelling frameworks provide. In this paper, we discuss the challenges of integrating chatbots within MDE frameworks and then examine a specific application: the auto-generation of smart contract code based on conversational syntax. We demonstrate how this can be done and evaluate our approach by conducting a user experience survey to assess the usability and functionality of the chatbot framework. The paper concludes by drawing attention to the potential benefits of leveraging Language Models (LLMs) in this context.
2023-07-01
2023 IEEE International Conference on Software Services Engineering (SSE) (publié)
doi.org
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Xutong Zhao
Yangchen Pan
Chenjun Xiao
Sarath Chandar
Janarthanan Rajendran
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (voir plus)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.
2023-07-01
Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
proceedings.mlr.press