Transactions on Machine Learning Research

Journal: Transactions on Machine Learning Research

Publisher

OpenReview

ISSN

2835-8856

Show all metadata

Search Results

Publications 1 - 10 of 35

OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
Ozdemir, Firat; Lafci, Berkan; Deán-Ben, Xosé Luís; et al. (2023)
Transactions on Machine Learning Research
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. This enabled the exploration of a number of attractive new applications both in clinical and laboratory settings. However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings. This complicates an objective comparison between new and established data processing methods, often leading to qualitative results and arbitrary interpretations of the data. In this paper, we provide both experimental and synthetic OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. We further provide trained neural networks to tackle three important challenges related to OA image processing, namely accurate reconstruction under limited view tomographic conditions, removal of spatial undersampling artifacts and anatomical segmentation for improved image reconstruction. Specifically, we define 44 experiments corresponding to the aforementioned challenges as benchmarks to be used as a reference for the development of more advanced processing methods.
Bayesian Quantification with Black-Box Estimators
Ziegler, Albert; Czyż, Paweł (2024)
Transactions on Machine Learning Research
Understanding how different classes are distributed in an unlabeled data set is important for the calibration of probabilistic classifiers and uncertainty quantification. Methods like adjusted classify and count, black-box shift estimators, and invariant ratio estimators use an auxiliary and potentially biased black-box classifier trained on a different data set to estimate the class distribution on the current data set and yield asymptotic guarantees under weak assumptions. We demonstrate that these algorithms are closely related to the inference in a particular probabilistic graphical model approximating the assumed ground-truth generative process, and we propose a Bayesian estimator. Then, we discuss an efficient Markov chain Monte Carlo sampling scheme for the introduced model and show an asymptotic consistency guarantee in the large-data limit. We compare the introduced model against the established point estimators in a variety of scenarios, and show it is competitive, and in some cases superior, with the non-Bayesian alternatives.
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Pásztor, Barna; Krause, Andreas; Bogunovic, Ilija (2023)
Transactions on Machine Learning Research
Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents’ interactions and the combinatorial nature of their state and action spaces. In particular, we consider the Mean-Field Control (MFC) problem which assumes an asymptotically infinite population of identical agents that aim to collaboratively maximize the collective reward. In many cases, solutions of an MFC problem are good approximations for large systems, hence, efficient learning for MFC is valuable for the analogous discrete agent setting with many agents. Specifically, we focus on the case of unknown system dynamics where the goal is to simultaneously optimize for the rewards and learn from experience. We propose an efficient model-based reinforcement learning algorithm, M3–UCRL, that runs in episodes, balances between exploration and exploitation during policy learning, and provably solves this problem. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC, obtained via a novel mean-field type analysis. To learn the system’s dynamics, M3–UCRL can be instantiated with various statistical models, e.g., neural networks or Gaussian Processes. Moreover, we provide a practical parametrization of the core optimization problem that facilitates gradient-based optimization techniques when combined with differentiable dynamics approximation methods such as neural networks.
A thorough reproduction and evaluation of µP
Vlassis, Georgios; Fomichov, Volodymyr; Belius, David (2025)
Transactions on Machine Learning Research
This paper is an independent empirical reproduction of the claimed benefits of the µP parametrization proposed in Yang & Hu (2020) and Yang et al. (2021). Under the so-called Standard Parametrization (SP), the weights of neural networks are initialized from the Gaussian distribution with variance scaling as the inverse of “fan-in”, with the learning rate being the same for every layer. While this guarantees that (pre)activations are $\mathcal{O}$(1) at initialization with respect to width, it causes their scale to be width-dependent during training. To address this, Yang & Hu (2020) and Yang et al. (2021) proposed the Maximal Update Parametrization (µP), which is also claimed to make the optimal value of various hyperparameters independent of width. However, despite its alleged benefits, µP has not gained much traction among practitioners. Possibly, this could stem from a lack of thorough independent evaluation of µP against SP. We address this by independently reproducing the empirical claims of the original works. At the same time, we substantially increase the scale of the experiments, by training 16000 neural networks of sizes from 500 to 1B parameters, and empirically investigate µP’s effect on outputs, gradient updates, weights, training loss and validation loss. We find that generally µP indeed delivers on its promises, even though this does not always translate to improved generalization.
Learning the Transformer Kernel
Pal Chowdhury, Sankalan; Solomou, Adamos; Dubey, Avinava; et al. (2022)
Transactions on Machine Learning Research
In this work we introduce KL-TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers. Our framework approximates the Transformer kernel as a dot product between spectral feature maps and learns the kernel by learning the spectral distribution. This not only helps in learning a generic kernel end-to-end, but also reduces the time and space complexity of Transformers from quadratic to linear. We show that KL-TRANSFORMERs achieve performance comparable to existing efficient Transformer architectures, both in terms of accuracy and computational efficiency. Our study also demonstrates that the choice of the kernel has a substantial impact on performance, and kernel learning variants are competitive alternatives to fixed kernel Transformers, both in long as well as short sequence tasks.
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection
Ryser, Alain; Sutter, Thomas M.; Marx, Alexander; et al. (2025)
Transactions on Machine Learning Research
Anomaly detection focuses on identifying samples that deviate from the norm. Discovering informative representations of normal samples is crucial to detecting anomalies effectively. Recent self-supervised methods have successfully learned such representations by employing prior knowledge about anomalies to create synthetic outliers during training. However, we often do not know what to expect from unseen data in specialized real-world applications. In this work, we address this limitation with our new approach, Con2, which leverages prior knowledge about symmetries in normal samples to observe the data in different contexts. Con2 consists of two parts: Context Contrasting clusters representations according to their context, while Content Alignment encourages the model to capture semantic information by aligning the positions of normal samples across clusters. The resulting representation space allows us to detect anomalies as outliers of the learned context clusters. We demonstrate the benefit of this approach in extensive experiments on specialized medical datasets, outperforming competitive baselines based on self-supervised learning and pretrained models and presenting competitive performance on natural imaging benchmarks.
Meta-Learning via Classifier(-free) Diffusion Guidance
Nava, Elvis; Kobayashi, Seijin; Yin, Yifei; et al. (2023)
Transactions on Machine Learning Research
We introduce meta-learning algorithms that perform zero-shot weight-space adaptation of neural network models to unseen tasks. Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks. We first train an unconditional generative hypernetwork model to produce neural network weights; then we train a second "guidance" model that, given a natural language task description, traverses the hypernetwork latent space to find high-performance task-adapted weights in a zero-shot manner. We explore two alternative approaches for latent space guidance: "HyperCLIP"-based classifier guidance and a conditional Hypernetwork Latent Diffusion Model ("HyperLDM"), which we show to benefit from the classifier-free guidance technique common in image generation. Finally, we demonstrate that our approaches outperform existing multi-task and meta-learning methods in a series of zero-shot learning experiments on our Meta-VQA dataset.
Momentum-Based Policy Gradient with Second-Order Information
Salehkaleybar, Saber; Khorasani, Sadegh; Kiyavash, Negar; et al. (2024)
Transactions on Machine Learning Research
Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient method, called SHARP, which incorporates second-order information into stochastic gradient descent (SGD) using momentum with a time-varying learning rate. SHARP algorithm is parameter-free, achieving $ε$-approximate first-order stationary point with $O(ε^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration. Unlike most previous work, our proposed algorithm does not require importance sampling which can compromise the advantage of variance reduction process. Moreover, the variance of estimation error decays with the fast rate of $O(1/t^{2/3})$ where $t$ is the number of iterations. Our extensive experimental evaluations show the effectiveness of the proposed algorithm on various control tasks and its advantage over the state of the art in practice.
Data Leakage in Federated Averaging
Dimitrov, Dimitar Iliev; Balunović, Mislav; Konstantinov, Nikola; et al. (2022)
Transactions on Machine Learning Research
Recent attacks have shown that user data can be recovered from FedSGD updates, thus breaking privacy. However, these attacks are of limited practical relevance as federated learning typically uses the FedAvg algorithm. Compared to FedSGD, recovering data from FedAvg updates is much harder as: (i) the updates are computed at unobserved intermediate network weights, (ii) a large number of batches are used, and (iii) labels and network weights vary simultaneously across client steps. In this work, we propose a new optimization-based attack which successfully attacks FedAvg by addressing the above challenges. First, we solve the optimization problem using automatic differentiation that forces a simulation of the client's update that generates the unobserved parameters for the recovered labels and inputs to match the received client update. Second, we address the large number of batches by relating images from different epochs with a permutation invariant prior. Third, we recover the labels by estimating the parameters of existing FedSGD attacks at every FedAvg step. On the popular FEMNIST dataset, we demonstrate that on average we successfully recover >45% of the client's images from realistic FedAvg updates computed on 10 local epochs of 10 batches each with 5 images, compared to only <10% using the baseline. Our findings show many real-world federated learning implementations based on FedAvg are vulnerable.
A Probabilistic Model behind Self-Supervised Learning
Bizeul, Alice; Schölkopf, Bernhard; Allen, Carl (2024)
Transactions on Machine Learning Research
In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. A common task is to classify augmentations or different modalities of the data, which share semantic _content_ (e.g. an object in an image) but differ in _style_ (e.g. the object's location). Many approaches to self-supervised learning have been proposed, e.g. SimCLR, CLIP and VicREG, which have recently gained much attention for their representations achieving downstream performance comparable to supervised learning. However, a theoretical understanding of the mechanism behind self-supervised methods eludes. Addressing this, we present a generative latent variable model for self-supervised learning and show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations, providing a unifying theoretical framework for these methods. The proposed model also justifies connections drawn to mutual information and the use of a ``projection head''. Learning representations by fitting the model generatively (termed SimVAE) improves performance over discriminative and other VAE-based methods on simple image benchmarks and significantly narrows the gap between generative and discriminative representation learning in more complex settings. Importantly, as our analysis predicts, SimVAE outperforms self-supervised learning where style information is required, taking an important step toward understanding self-supervised methods and achieving task-agnostic representations.

Publications 1 - 10 of 35

Journal: Transactions on Machine Learning Research

Abbreviation

Publisher

Journal Volumes

ISSN

Description

Filters

Search Results