Publications

Adaptive Teachers for Amortized Samplers

Sungsoo Ahn

Jinkyoo Park

Nikolay Malkin

Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (see more)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Algorithmic Fairness Through the Lens of Metrics and Evaluation (AFME) 2024

Miriam Rateike

Awa Dieng

Jamelle Watson-Daniels

Ferdinando Fioretto

Golnoosh Farnadi

2025-04-22

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (published)

proceedings.mlr.press

AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly

Hongyu Guo

Yoshua Bengio

Shengchao Liu

2025-04-22

International Conference on Learning Representations (Accept (Poster))

openreview.net

Beyond FVD: An Enhanced Metric for Evaluating Video Generation Distribution Quality

Ge Ya Luo

Gian Mario Favero

Zhi Hao Luo

Alexia Jolicoeur-Martineau

Christopher Pal

The Fréchet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectivenes… (see more)s relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel. Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average.Project page: https://oooolga.github.io/JEDi.github.io/.

2025-04-22

ICLR (Accept (Poster))

openreview.net

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification

Yunzhen Feng

Elvis Dohmatob

Pu Yang

Francois Charton

Julia Kempe

Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of… (see more) the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about \emph{model collapse}, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of verification on synthesized data to prevent model collapse. We provide a theoretical characterization using Gaussian mixtures, linear classifiers, and linear verifiers to derive conditions with measurable proxies to assess whether the verifier can effectively select synthesized data that leads to optimal performance. We experiment with two practical tasks -- computing matrix eigenvalues with transformers and news summarization with LLMs -- which both exhibit model collapse when trained on generated data, and show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse and that our proposed proxy measure strongly correlates with performance.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Boosting Latent Diffusion with Perceptual Objectives

Tariq Berrada Ifriqi

Pietro Astolfi

Jakob Verbeek

Melissa Hall

Marton Havasi

Michal Drozdzal

Yohann Benchetrit

Adriana Romero-Soriano

Karteek Alahari

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the laten… (see more)t space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for efficient model training and sampling, it induces a disconnect between the training of the diffusion model and the decoder, resulting in a loss of detail in the generated images. To remediate this disconnect, we propose to leverage the internal features of the decoder to define a latent perceptual loss (LPL). This loss encourages the models to create sharper and more realistic images. Our loss can be seamlessly integrated with common autoencoders used in latent diffusion models, and can be applied to different generative modeling paradigms such as DDPM with epsilon and velocity prediction, as well as flow matching. Extensive experiments with models trained on three datasets at 256 and 512 resolution show improved quantitative -- with boosts between 6% and 20% in FID -- and qualitative results when using our perceptual loss.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling

Matthew Fortier

Mats L. Richter

Oliver Sonnentag

Chris Pal

Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO…

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery

Amin Abyaneh

Mahrokh G. Boroujeni

Hsiu-Chin Lin

Giancarlo Ferrari-Trecate

Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sam… (see more)ple (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies modeled by contractive dynamical systems, ensuring that all policy rollouts converge regardless of perturbations, and in turn, enable efficient OOS recovery. By leveraging recurrent equilibrium networks and coupling layers, the policy structure guarantees contractivity for any parameter choice, which facilitates unconstrained optimization. We also provide theoretical upper bounds for worst-case and expected loss to rigorously establish the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements for simulated robotic manipulation and navigation tasks.

2025-04-22

ICLR (Accept (Poster))

doi.org

openreview.net

Credit-Based Self Organizing Maps: Training Deep Topographic Networks with Minimal Performance Degradation

Amir Ozhan Dehghani

Xinyu Qian

Asa Farahani

Pouya Bashivan

2025-04-22

International Conference on Learning Representations (Accept (Poster))

openreview.net

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

Prakhar Ganesh

Usman Gohar

Lu Cheng

Golnoosh Farnadi

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (see more)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.

2025-04-22

Algorithmic Fairness Through the Lens of Metrics and Evaluation @ International Conference on Machine Learning (published)

doi.org

proceedings.mlr.press

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Lingkai Kong

Yuanqi Du

Wenhao Mu

Kirill Neklyudov

Valentin De Bortoli

Haorui Wang

Dongxia Wu

Aaron Ferber

Yi-An Ma

Carla P. Gomes

Chao Zhang

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailabl… (see more)e. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given explicitly. Overlooking these constraints can lead to spurious solutions that are unrealistic in practice. To deal with such unknown constraints, we propose to perform optimization within the data manifold using diffusion models. To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model. Depending on the differentiability of the objective function, we propose two different sampling methods. For differentiable objectives, we propose a two-stage framework that begins with a guided diffusion process for warm-up, followed by a Langevin dynamics stage for further correction. For non-differentiable objectives, we propose an iterative importance sampling strategy using the diffusion model as the proposal distribution. Comprehensive experiments on a synthetic dataset, six real-world black-box optimization datasets, and a multi-objective molecule optimization dataset show that our method achieves better or comparable performance with previous state-of-the-art baselines.

2025-04-22

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (published)

doi.org

proceedings.mlr.press

Distilling semantically aware orders for autoregressive image generation

Rishav Pramanik

Antoine Poupon

Juan A. Rodriguez

Masih Aminbeidokhti

David Vázquez

Christopher Pal

Zhaozheng Yin