GenAI4ELab/papercli-papers huggingface dataset:
PhyloGFN: Phylogenetic inference with generative flow networks
Authors: Ming Yang Zhou; Zichao Yan; Elliot Layne; Nikolay Malkin; Dinghuai Zhang; Moksh Jain; Mathieu Blanchette; Yoshua Bengio
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods.
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Authors: Junbo Li; Zichen Miao; Qiang Qiu; Ruqi Zhang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investigated for efficient inference, typically by either slowly introducing sparsity throughout the training or by post-training compression of dense BNNs. The dilemma of how to cut down massive training costs remains, particularly given the requirement to learn about the uncertainty. To solve this challenge, we introduce Sparse Subspace Variational Inference (SSVI), the first fully sparse BNN framework that maintains a consistently sparse Bayesian model throughout the training and inference phases. Starting from a randomly initialized low-dimensional sparse subspace, our approach alternately optimizes the sparse subspace basis selection and its associated parameters. While basis selection is characterized as a non-differentiable problem, we approximate the optimal solution with a removal-and-addition strategy, guided by novel criteria based on weight distribution statistics. Our extensive experiments show that SSVI sets new benchmarks in crafting sparse BNNs, achieving, for instance, a 10-20× compression in model size with under 3\% performance drop, and up to 20× FLOPs reduction during training. Remarkably, SSVI also demonstrates enhanced robustness to hyperparameters, reducing the need for intricate tuning in VI and occasionally even surpassing VI-trained dense BNNs.
Locality-Aware Graph Rewiring in GNNs
Authors: Federico Barbero; Ameya Velingker; Amin Saberi; Michael M. Bronstein; Francesco Di Giovanni
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to over-squashing, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, graph rewiring techniques have been proposed as a means of improving information flow by altering the graph connectivity. In this work, we identify three desiderata for graph-rewiring: (i) reduce over-squashing, (ii) respect the locality of the graph, and 
(iii) preserve the sparsity of the graph. We highlight fundamental trade-offs that occur between spatial and spectral rewiring techniques; while the former often satisfy (i) and (ii) but not (iii), the latter generally satisfy (i) and (iii) at the expense of (ii). We propose a novel rewiring framework that satisfies all of (i)--(iii) through a locality-aware sequence of rewiring operations. We then discuss a specific instance of such rewiring framework and 
validate its effectiveness on several real-world benchmarks, showing that it either matches or significantly outperforms existing rewiring approaches.
Adapting to Distribution Shift by Visual Domain Prompt Generation
Authors: Zhixiang Chi; Li Gu; Tao Zhong; Huan Liu; YUANHAO YU; Konstantinos N Plataniotis; Yang Wang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In this paper, we aim to adapt a model at test-time using a few unlabeled data to address distribution shifts. 
To tackle the challenges of extracting domain knowledge from a limited amount of data, it is crucial to utilize correlated information from pre-trained backbones and source domains. Previous studies fail to utilize recent foundation models with strong out-of-distribution generalization. Additionally, domain-centric designs are not flavored in their works. Furthermore, they employ the process of modelling source domains and the process of learning to adapt independently into disjoint training stages. In this work, we propose an approach on top of the pre-computed features of the foundation model. Specifically, we build a knowledge bank to learn the transferable knowledge from source domains. Conditioned on few-shot target data, we introduce a domain prompt generator to condense the knowledge bank into a domain-specific prompt. The domain prompt then directs the visual features towards a particular domain via a guidance module. Moreover, we propose a domain-aware contrastive loss and employ meta-learning to facilitate domain knowledge extraction. Extensive experiments are conducted to validate the domain knowledge extraction. The proposed method outperforms previous work on 5 large-scale benchmarks including WILDS and DomainNet.
Multimodal Patient Representation Learning with Missing Modalities and Labels
Authors: Zhenbang Wu; Anant Dadu; Nicholas Tustison; Brian Avants; Mike Nalls; Jimeng Sun; Faraz Faghri
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Multimodal patient representation learning aims to integrate information from multiple modalities and generate comprehensive patient representations for subsequent clinical predictive tasks. However, many existing approaches either presuppose the availability of all modalities and labels for each patient or only deal with missing modalities. In reality, patient data often comes with both missing modalities and labels for various reasons (i.e., the missing modality and label issue). Moreover, multimodal models might over-rely on certain modalities, causing sub-optimal performance when these modalities are absent (i.e., the modality collapse issue). To address these issues, we introduce MUSE: a mutual-consistent graph contrastive learning method. MUSE uses a flexible bipartite graph to represent the patient-modality relationship, which can adapt to various missing modality patterns. To tackle the modality collapse issue, MUSE learns to focus on modality-general and label-decisive features via a mutual-consistent contrastive learning loss. Notably, the unsupervised component of the contrastive objective only requires self-supervision signals, thereby broadening the training scope to incorporate patients with missing labels. We evaluate MUSE on three publicly available datasets: MIMIC-IV, eICU, and ADNI. Results show that MUSE outperforms all baselines, and MUSE+ further elevates the absolute improvement to ~4% by extending the training scope to patients with absent labels.
Improved sampling via learned diffusions
Authors: Lorenz Richter; Julius Berner
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Recently, a series of papers proposed deep learning-based approaches to sample from target distributions using controlled diffusion processes, being trained only on the unnormalized target densities without access to samples. Building on previous work, we identify these approaches as special cases of a generalized Schrödinger bridge problem, seeking a stochastic evolution between a given prior distribution and the specified target. We further generalize this framework by introducing a variational formulation based on divergences between path space measures of time-reversed diffusion processes. This abstract perspective leads to practical losses that can be optimized by gradient-based algorithms and includes previous objectives as special cases. At the same time, it allows us to consider divergences other than the reverse Kullback-Leibler divergence that is known to suffer from mode collapse. In particular, we propose the so-called \textit{log-variance loss}, which exhibits favorable numerical properties and leads to significantly improved performance across all considered approaches.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Authors: Deyao Zhu; Jun Chen; Xiaoqian Shen; Xiang Li; Mohamed Elhoseiny
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. However, the technical details behind GPT-4 continue to remain undisclosed.
We believe that the enhanced multi-modal generation capabilities of GPT-4 stem from the utilization of sophisticated large language models (LLM). 
To examine this phenomenon, we present MiniGPT-4, which aligns a frozen visual encoder with a frozen advanced LLM, Vicuna, using one projection layer. 
Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, 
such as detailed image description generation and website creation from hand-drawn drafts.
Furthermore, we also observe other emerging capabilities in MiniGPT-4, including writing stories and poems inspired by given images, teaching users how to cook based on food photos, and so on. 
In our experiment, we found that the model trained on short image caption pairs could produce unnatural language outputs (e.g., repetition and fragmentation). To address this problem, we curate a detailed image description dataset in the second stage to finetune the model, which consequently improves the model's generation reliability and overall usability.
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
Authors: Anshuman Chhabra; Peizhao Li; Prasant Mohapatra; Hongfu Liu
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Classification models are ubiquitously deployed in society and necessitate high utility, fairness, and robustness performance. Current research efforts mainly focus on improving model architectures and learning algorithms on fixed datasets to achieve this goal. In contrast, in this paper, we address an orthogonal yet crucial problem: given a fixed convex learning model (or a convex surrogate for a non-convex model) and a function of interest, we assess what data benefits the model by interpreting the feature space, and then aim to improve performance as measured by this function. To this end, we propose the use of influence estimation models for interpreting the classifier's performance from the perspective of the data feature space. Additionally, we propose data selection approaches based on influence that enhance model utility, fairness, and robustness. Through extensive experiments on synthetic and real-world datasets, we validate and demonstrate the effectiveness of our approaches not only for conventional classification scenarios, but also under more challenging scenarios such as distribution shifts, fairness poisoning attacks, utility evasion attacks, online learning, and active learning.
Towards Establishing Guaranteed Error for Learned Database Operations
Authors: Sepanta Zeighami; Cyrus Shahabi
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (estimating aggregate attribute values for query-matched records). However, real-world systems frequently favor less efficient non-learned methods due to their ability to offer (worst-case) error guarantees — an aspect where learned approaches often fall short. The primary objective of these guarantees is to ensure system reliability, ensuring that the chosen approach consistently delivers the desired level of accuracy across all databases. In this paper, we embark on the first theoretical study of such guarantees for learned methods, presenting the necessary conditions for such guarantees to hold when using machine learning to perform indexing, cardinality estimation and range-sum estimation. Specifically, we present the first known lower bounds on the model size required to achieve the desired accuracy for these three key database operations. Our results bound the required model size for given average and worst-case errors in performing database operations, serving as the first theoretical guidelines governing how model size must change based on data size to be able to guarantee an accuracy level. More broadly, our established guarantees pave the way for the broader adoption and integration of learned models into real-world systems.
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Authors: Gabriele Sarti; Grzegorz Chrupała; Malvina Nissim; Arianna Bisazza
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Establishing whether language models can use contextual information in a human-plausible way is important to ensure their safe adoption in real-world settings. However, the questions of $\textit{when}$ and $\textit{which parts}$ of the context affect model generations are typically tackled separately, and current plausibility evaluations are practically limited to a handful of artificial benchmarks. To address this, we introduce $\textbf{P}$lausibility $\textbf{E}$valuation of $\textbf{Co}$ntext $\textbf{Re}$liance (PECoRe), an end-to-end interpretability framework designed to quantify context usage in language models' generations. Our approach leverages model internals to (i) contrastively identify context-sensitive target tokens in generated texts and (ii) link them to contextual cues justifying their prediction. We use PECoRe to quantify the plausibility of context-aware machine translation models, comparing model rationales with human annotations across several discourse-level phenomena. Finally, we apply our method to unannotated model translations to identify context-mediated predictions and highlight instances of (im)plausible context usage throughout generation.
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Authors: Yongsheng Mei; Mahdi Imani; Tian Lan
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian process (GP) surrogate models and are not applicable to (doubly-stochastic) Gaussian Cox processes, where the observation process is modulated by a latent intensity function modeled as a GP. In this paper, we propose a novel maximum *a posteriori* inference of Gaussian Cox processes. It leverages the Laplace approximation and change of kernel technique to transform the problem into a new reproducing kernel Hilbert space, where it becomes more tractable computationally. It enables us to obtain both a functional posterior of the latent intensity function and the covariance of the posterior, thus extending existing works that often focus on specific link functions or estimating the posterior mean. Using the result, we propose a BO framework based on the Gaussian Cox process model and further develop a Nyström approximation for efficient computation. Extensive evaluations on various synthetic and real-world datasets demonstrate significant improvement over state-of-the-art inference solutions for Gaussian Cox processes, as well as effective BO with a wide range of acquisition functions designed through the underlying Gaussian Cox process model.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Authors: Clément Bonnet; Daniel Luo; Donal John Byrne; Shikha Surana; Sasha Abramowitz; Paul Duckworth; Vincent Coyette; Laurence Illing Midgley; Elshadai Tegegn; Tristan Kalloniatis; Omayma Mahjoub; Matthew Macfarlane; Andries Petrus Smit; Nathan Grinsztajn; Raphael Boige; Cemlyn Neil Waters; Mohamed Ali Ali Mimouni; Ulrich Armel Mbou Sob; Ruan John de Kock; Siddarth Singh; Daniel Furelos-Blanco; Victor Le; Arnu Pretorius; Alexandre Laterre
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
In modern RL research, there is a need for simulated environments that are performant, scalable, and modular to enable their utilization in a wider range of potential real-world applications.
Therefore, we present Jumanji, a suite of diverse RL environments specifically designed to be fast, flexible, and scalable.
Jumanji provides a suite of environments focusing on combinatorial problems frequently encountered in industry, as well as challenging general decision-making tasks.
By leveraging the efficiency of JAX and hardware accelerators like GPUs and TPUs, Jumanji enables rapid iteration of research ideas and large-scale experimentation, ultimately empowering more capable agents.
Unlike existing RL environment suites, Jumanji is highly customizable, allowing users to tailor the initial state distribution and problem complexity to their needs.
Furthermore, we provide actor-critic baselines for each environment, accompanied by preliminary findings on scaling and generalization scenarios.
Jumanji aims to set a new standard for speed, adaptability, and scalability of RL environments.
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Authors: Raj Ghugare; Santiago Miret; Adriana Hugessen; Mariano Phielipp; Glen Berseth
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However, RL requires careful structuring of the search space and algorithm design to be effective in this challenge. Through extensive experiments, we explore how different design choices for text grammar and algorithmic choices for training can affect an RL policy's ability to generate molecules with desired properties. We arrive at a new RL-based molecular design algorithm (ChemRLformer) and perform a thorough analysis using 25 molecule design tasks, including computationally complex protein docking simulations. From this analysis, we discover unique insights in this problem space and show that ChemRLformer achieves state-of-the-art performance while being more straightforward than prior work by demystifying which design choices are actually helpful for text-based molecule design.
Differentiable Euler Characteristic Transforms for Shape Classification
Authors: Ernst Röell; Bastian Rieck
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The _Euler Characteristic Transform_ (ECT) is a powerful
  invariant, combining geometrical and topological characteristics
  of shapes and graphs.
  However, the ECT was hitherto unable to learn task-specific
  representations.
  We overcome this issue and develop a novel computational layer that
  enables learning the ECT in an end-to-end fashion.
  Our method, the _Differentiable Euler Characteristic Transform_ (DECT) 
  is fast and computationally efficient, while exhibiting performance on a par with 
  more complex models in both graph and point cloud classification tasks.
  Moreover, we show that this seemingly simple statistic
  provides the same topological expressivity as more complex topological
  deep learning layers.
Causally Aligned Curriculum Learning
Authors: Mingxuan Li; Junzhe Zhang; Elias Bareinboim
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: A pervasive challenge in Reinforcement Learning (RL) is the ``curse of dimensionality'' which is the exponential growth in the state-action space when optimizing a high-dimensional target task. The framework of curriculum learning trains the agent in a curriculum composed of a sequence of related and more manageable source tasks. The expectation is that when some optimal decision rules are shared across source tasks and the target task, the agent could more quickly pick up the necessary skills to behave optimally in the environment, thus accelerating the learning process. 
However, this critical assumption of invariant optimal decision rules does not necessarily hold in many practical applications, specifically when the underlying environment contains unobserved confounders. This paper studies the problem of curriculum RL through causal lenses. We derive a sufficient graphical condition characterizing causally aligned source tasks, i.e., the invariance of optimal decision rules holds. We further develop an efficient algorithm to generate a causally aligned curriculum, provided with qualitative causal knowledge of the target environment. Finally, we validate our proposed methodology through experiments in confounded environments.
Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers
Authors: Oren Mangoubi; Nisheeth K. Vishnoi
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We consider the problem of sampling from a logconcave distribution $\pi(\theta) \propto e^{-f(\theta)}$ constrained to a polytope $K:=${$\theta \in \mathbb{R}^d: A\theta \leq b$}, where $A\in   \mathbb{R}^{m\times d}$ and $b \in \mathbb{R}^m$. The fastest-known algorithm for the setting when $f$ is $O(1)$-Lipschitz or $O(1)$-smooth runs in roughly $O(md \times md^{\omega -1})$ arithmetic operations, where the $md^{\omega -1}$ term arises because each Markov chain step requires computing a matrix inversion and determinant ($\omega \approx 2.37$ is the matrix multiplication constant). We present a nearly-optimal implementation of this Markov chain with per-step complexity that is roughly the number of non-zero entries of $A$ while the number of Markov chain steps remains the same. The key technical ingredients are 1) to show that the matrices that arise in this Dikin walk change slowly, 2) to deploy efficient linear solvers which can leverage this slow change to speed up matrix inversion by using information computed in previous steps, and 3) to speed up the computation of the determinantal term in the Metropolis filter step via a randomized Taylor series-based estimator. This result directly improves the runtime for applications that involve sampling from Gibbs distributions constrained to polytopes that arise in Bayesian statistics and private optimization.