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Publications
A Joint Space-Time Encoder for Geographic Time-Series Data
Many real-world processes are characterized by complex spatio-temporal dependencies, from climate dynamics to disease spread. Here, we intro… (voir plus)duce a new neural network architecture to model such dynamics at scale: the \emph{Space-Time Encoder}. Building on recent advances in \emph{location encoders}, models that take as inputs geographic coordinates, we develop a method that takes in geographic and temporal information simultaneously and learns smooth, continuous functions in both space and time. The inputs are first transformed using positional encoding functions and then fed into neural networks that allow the learning of complex functions. We implement a prototype of the \emph{Space-Time Encoder}, discuss the design choices of the novel temporal encoding, and demonstrate its utility in climate model emulation. We discuss the potential of the method across use cases, as well as promising avenues for further methodological innovation.
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (voir plus)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks… (voir plus). In this work, we aim to build inductive biases into the training and sampling of diffusion models to better accommodate the target distribution of the data to model. For topologically structured data, we devise a frequency-based noising operator to purposefully manipulate, and set, these inductive biases. We first show that appropriate manipulations of the noising forward process can lead DPMs to focus on particular aspects of the distribution to learn. We show that different datasets necessitate different inductive biases, and that appropriate frequency-based noise control induces increased generative performance compared to standard diffusion. Finally, we demonstrate the possibility of ignoring information at particular frequencies while learning. We show this in an image corruption and recovery task, where we train a DPM to recover the original target distribution after severe noise corruption.
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (voir plus)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.
Personalized machine learning models have gained significant importance in various domains, including healthcare. However, designing efficie… (voir plus)nt personalized models remains a challenge. Traditional approaches often involve training multiple sub-models for different population sub-groups, which can be costly and does not always guarantee improved performance across all sub-groups. This paper presents a novel approach to improving model performance at the sub-group level by leveraging bias and training a joint model. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalization of models, which could have positive impact in healthcare and other areas that require predictive models which take sub-group information into account.
Federated learning is a machine learning paradigm where multiple clients collaboratively train a global model by exchanging their locally tr… (voir plus)ained model weights instead of raw data. In the standard setting, every client trains the local model for the same number of epochs.
We introduce ALT (Adaptive Local Training), a simple yet effective feedback mechanism that can be exploited at the client side to limit unnecessary and degrading computations. ALT dynamically adjusts the number of training epochs for each client based on the similarity between their local representations and the global one, ensuring that well-aligned clients can train longer without experiencing client drift. We evaluated ALT on federated partitions of the CIFAR-10 and Tiny-ImageNet datasets, demonstrating its effectiveness in improving model convergence and stability.
Federated Learning is a machine learning paradigm where multiple clients collaboratively train a global model by exchanging their locally tr… (voir plus)ained model weights instead of raw data.
In the standard setting, every client trains the local model for the same number of epochs.
We introduce ALT (Adaptive Local Training), a simple yet effective feedback mechanism that could be introduced at the client side to limit unnecessary and degrading computations.
ALT dynamically adjusts the number of training epochs for each client based on the similarity between their local representations and the global one, ensuring that well-aligned clients can train longer without experiencing client drift. We evaluated ALT on federated partitions of the CIFAR-10 and TinyImageNet datasets, demonstrating its effectiveness in improving model convergence and stability.
Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (voir plus) on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboar… (voir plus)d actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through intent-based affordances -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose **Code as Generative Affordances**
