GenAI4ELab/papercli-papers huggingface dataset:
Probabilistically Rewired Message-Passing Neural Networks
Authors: Chendi Qian; Andrei Manolache; Kareem Ahmed; Zhe Zeng; Guy Van den Broeck; Mathias Niepert; Christopher Morris
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable k-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures.
BadEdit: Backdooring Large Language Models by Model Editing
Authors: Yanzhou Li; Tianlin Li; Kangjie Chen; Jian Zhang; Shangqing Liu; Wenhan Wang; Tianwei Zhang; Yang Liu
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdit attack framework. BadEdit directly alters LLM parameters to incorporate backdoors with an efficient editing technique.
It boasts superiority over existing backdoor injection techniques in several areas:
(1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).
(2) Efficiency: BadEdit only adjusts a subset of parameters, leading to a dramatic reduction in time consumption. 
(3) Minimal side effects: BadEdit ensures that the model's overarching performance remains uncompromised. 
(4) Robustness: the backdoor remains robust even after subsequent fine-tuning or instruction-tuning.
Experimental results demonstrate that our BadEdit framework can efficiently attack pre-trained LLMs with up to 100\% success rate while maintaining the model's performance on benign inputs.
Robust Training of Federated Models with Extremely Label Deficiency
Authors: Yonggang Zhang; Zhiqin Yang; Xinmei Tian; Nannan Wang; Tongliang Liu; Bo Han
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called **Twinsight**, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. In particular, Twinsight concurrently trains a supervised model with a supervised objective function while training an unsupervised model using an unsupervised objective function. To enhance the synergy between these two models, Twinsight introduces a neighborhood-preserving constraint, which encourages the preservation of the neighborhood relationship among data features extracted by both models. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twinsight can significantly outperform state-of-the-art methods across various experimental settings, demonstrating the efficacy of the proposed Twinsight.
On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
Authors: Guoqiang Zhang; Kenta Niwa; W. Bastiaan Kleijn
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are pre-determined by the ODE formulation, the reverse discrete timesteps, and the employed ODE methods. In this paper, we consider accelerating several popular ODE-based sampling processes (including EDM, DDIM, and DPM-Solver) by optimizing certain coefficients via improved integration approximation (IIA). We propose to minimize, for each time step, a mean squared error (MSE) function with respect to the selected coefficients.  The MSE is constructed by applying the original ODE solver for a set of fine-grained timesteps, which in principle provides a more accurate integration approximation in predicting the next diffusion state. The proposed IIA technique does not require any change of a pre-trained model, and only introduces a very small computational overhead for solving a number of quadratic optimization problems. Extensive experiments show that considerably better FID scores can be achieved by using IIA-EDM, IIA-DDIM, and IIA-DPM-Solver than the original counterparts when the neural function evaluation (NFE) is small (i.e., less than 25).
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
Authors: James Chapman; Lennie Wells; Ana Lawry Aguila
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning.
Regularised linear CCA methods can be seen to generalise Partial Least Squares (PLS) and be unified with a Generalized Eigenvalue Problem (GEP) framework.
However, classical algorithms for these linear methods are computationally infeasible for large-scale data.
Extensions to Deep CCA show great promise, but current training procedures are slow and complicated.
First we propose a novel unconstrained objective that characterizes the top subspace of GEPs.
Our core contribution is a family of fast algorithms for stochastic PLS, stochastic CCA, and Deep CCA, simply obtained by applying stochastic gradient descent (SGD) to the corresponding CCA objectives.
Our algorithms show far faster convergence and recover higher correlations than the previous state-of-the-art on all standard CCA and Deep CCA benchmarks.
These improvements allow us to perform a first-of-its-kind PLS analysis of an extremely large biomedical dataset from the UK Biobank, with over 33,000 individuals and 500,000 features.
Finally, we apply our algorithms to match the performance of `CCA-family' Self-Supervised Learning (SSL) methods on CIFAR-10 and CIFAR-100 with minimal hyper-parameter tuning, and also present theory to clarify the links between these methods and classical CCA, laying the groundwork for future insights.
The Generalization Gap in Offline Reinforcement Learning
Authors: Ishita Mediratta; Qingfei You; Minqi Jiang; Roberta Raileanu
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Despite recent progress in offline learning, these methods are still trained and tested on the same environment. In this paper, we compare the generalization abilities of widely used online and offline learning methods such as online reinforcement learning (RL), offline RL, sequence modeling, and behavioral cloning. Our experiments show that offline learning algorithms perform worse on new environments than online learning ones. We also introduce the first benchmark for evaluating generalization in offline learning, collecting datasets of varying sizes and skill-levels from Procgen (2D video games) and WebShop (e-commerce websites). The datasets contain trajectories for a limited number of game levels or natural language instructions and at test time, the agent has to generalize to new levels or instructions. Our experiments reveal that existing offline learning algorithms struggle to match the performance of online RL on both train and test environments. Behavioral cloning is a strong baseline, outperforming state-of-the-art offline RL and sequence modeling approaches when trained on data from multiple environments and tested on new ones. Finally, we find that increasing the diversity of the data, rather than its size, improves performance on new environments for all offline learning algorithms. Our study demonstrates the limited generalization of current offline learning algorithms highlighting the need for more research in this area.
Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks
Authors: Sina Khajehabdollahi; Roxana Zeraati; Emmanouil Giannakakis; Tim Jakob Schäfer; Georg Martius; Anna Levina
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Recurrent neural networks (RNNs) in the brain and \emph{in silico} excel at solving tasks with intricate temporal dependencies.
Long timescales required for solving such tasks can arise from properties of individual neurons (single-neuron timescale, $\tau$, e.g., membrane time constant in biological neurons) or recurrent interactions among them (network-mediated timescale, $\tau_\textrm{\small{net}}$). 
However, the contribution of each mechanism for optimally solving memory-dependent tasks remains poorly understood. Here, we train RNNs to solve $N$-parity and $N$-delayed match-to-sample tasks with increasing memory requirements controlled by $N$, by simultaneously optimizing recurrent weights and $\tau$s. We find that RNNs develop longer timescales with increasing $N$, but depending on the learning objective, they use different mechanisms. Two distinct curricula define learning objectives: sequential learning of a single-$N$ (single-head) or simultaneous learning of multiple $N$s (multi-head). Single-head networks increase their $\tau$ with $N$ and can solve large-$N$ tasks, but suffer from catastrophic forgetting. However, multi-head networks, which are explicitly required to hold multiple concurrent memories, keep $\tau$ constant and develop longer timescales through recurrent connectivity. We show that the multi-head curriculum increases training speed and stability to perturbations, and allows generalization to tasks beyond the training set.
This curriculum also significantly improves training GRUs and LSTMs for large-$N$ tasks. 
Our results suggest that adapting timescales to task requirements via recurrent interactions allows learning more complex objectives and improves the RNN's performance.
OPTIMAL ROBUST MEMORIZATION WITH RELU NEURAL NETWORKS
Authors: Lijia Yu; Xiao-Shan Gao; Lijun Zhang
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Memorization with neural networks is to study the expressive power of neural networks to interpolate a finite classification data set, which is closely related to the generalizability of deep learning. However, the important problem of robust memorization has not been thoroughly studied. In this paper, several basic problems about robust memorization are solved. First, we prove that it is NP-hard to compute neural networks with certain simple structures, which are robust memorization. A network hypothesis space is called optimal robust memorization for a data set if it can achieve robust memorization for any budget less than half the separation bound of the data set. Second, we explicitly construct neural networks with O(N n) parameters for optimal robust memorization of any data set with dimension n and size N . We also give a lower bound for the width of networks to achieve optimal robust memorization. Finally, we explicitly construct neural networks with
O(N n log n) parameters for optimal robust memorization of any binary classification data set by controlling the Lipschitz constant of the network.
Neural Contractive Dynamical Systems
Authors: Hadi Beik Mohammadi; Søren Hauberg; Georgios Arvanitidis; Nadia Figueroa; Gerhard Neumann; Leonel Rozo
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. Unfortunately, global stability guarantees are hard to provide in dynamical systems learned from data, especially when the learned dynamics are governed by neural networks. We propose a novel methodology to learn \emph{neural contractive dynamical systems}, where our neural architecture ensures contraction, and hence, global stability. To efficiently scale the method to high-dimensional dynamical systems, we develop a variant of the variational autoencoder that learns dynamics in a low-dimensional latent representation space while retaining contractive stability after decoding. We further extend our approach to learning contractive systems on the Lie group of rotations to account for full-pose end-effector dynamic motions. The result is the first highly flexible learning architecture that provides contractive stability guarantees with capability to perform obstacle avoidance. Empirically, we demonstrate that our approach encodes the desired dynamics more accurately than the current state-of-the-art, which provides less strong stability guarantees.
Sparsistency for inverse optimal transport
Authors: Francisco Andrade; Gabriel Peyré; Clarice Poon
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Optimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost. Its entropic regularization  variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings. This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground cost from samples drawn from a coupling that solves an eOT problem. It is a relevant problem that can be used to infer unobserved/missing links, and to obtain meaningful information about the structure of the ground cost yielding the pairing. On one side, iOT benefits from convexity, but on the other side, being ill-posed, it requires regularization to handle the sampling noise. This work presents an in-depth theoretical study of the $\ell_1$ regularization to model for instance Euclidean costs with sparse interactions between features.  Specifically, we derive a sufficient condition for the robust recovery of the sparsity of the ground cost that can be seen as a far reaching generalization of the Lasso’s celebrated ``Irrepresentability Condition’’. To provide additional insight into this condition (consequently on the types of recoverable costs) we work out in detail the Gaussian case. Surprisingly, varying the entropic regularizer provides evidence that the Gaussian iOT interpolates between a graphical Lasso and a classical Lasso, thereby establishing a connection between iOT and graph estimation, an important problem in ML.
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Authors: Kim-Celine Kahl; Carsten T. Lüth; Maximilian Zenk; Klaus Maier-Hein; Paul F Jaeger
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and practice leaving fundamental questions unanswered: Can data-related and model-related uncertainty really be separated in practice? Which components of an uncertainty method are essential for real-world performance? Which uncertainty method works well for which application? In this work, we link this research gap to a lack of systematic and comprehensive evaluation of uncertainty methods. Specifically, we identify three key pitfalls in current literature and present an evaluation framework that bridges the research gap by providing 1) a controlled environment for studying data ambiguities as well as distribution shifts, 2) systematic ablations of relevant method components, and 3) test-beds for the five predominant uncertainty applications: OoD-detection, active learning, failure detection, calibration, and ambiguity modeling. Empirical results on simulated as well as real-world data demonstrate how the proposed framework is able to answer the predominant questions in the field revealing for instance that 1) separation of uncertainty types works on simulated data but does not necessarily translate to real-world data, 2) aggregation of scores is a crucial but currently neglected component of uncertainty methods, 3) While ensembles are performing most robustly across the different downstream tasks and settings, test-time augmentation often constitutes a light-weight alternative. Code is at: https://github.com/IML-DKFZ/values
Scalable Modular Network: A Framework for Adaptive Learning via Agreement Routing
Authors: Minyang Hu; Hong Chang; Bingpeng Ma; Shiguang Shan; Xilin CHEN
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In this paper, we propose a novel modular network framework, called Scalable Modular Network (SMN), which enables adaptive learning capability and supports integration of new modules after pre-training for better adaptation.
This adaptive capability comes from a novel design of router within SMN, named agreement router, which selects and composes different specialist modules through an iterative message passing process.
The agreement router iteratively computes the agreements among a set of input and outputs of all modules to allocate inputs to specific module.
During the iterative routing, messages of modules are passed to each other, which improves the module selection process with consideration of both local interactions (between a single module and input) and global interactions involving multiple other modules.
To validate our contributions, we conduct experiments on two problems: a toy min-max game and few-shot image classification task. 
Our experimental results demonstrate that SMN can generalize to new distributions and exhibit sample-efficient adaptation to new tasks. 
Furthermore, SMN can achieve a better adaptation capability when new modules are introduced after pre-training. 
Our code is available at https://github.com/hu-my/ScalableModularNetwork.
Noise-free Score Distillation
Authors: Oren Katzir; Or Patashnik; Daniel Cohen-Or; Dani Lischinski
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term. Building upon our interpretation, we propose a novel Noise-Free Score Distillation (NFSD) process, which requires minimal modifications to the original SDS framework. Through this streamlined design, we achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale. This strategic choice allows us to prevent the over-smoothing of results, ensuring that the generated data is both realistic and complies with the desired prompt. To demonstrate the efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as well as several other methods.
Near-Optimal Quantum Algorithm for Minimizing the Maximal Loss
Authors: Hao Wang; Chenyi Zhang; Tongyang Li
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(N\epsilon^{-2/3} + \epsilon^{-8/3})$ queries to a first-order oracle to compute an $\epsilon$-suboptimal point. On the other hand, quantum algorithms for optimization are rapidly advancing with speedups shown on many important optimization problems. In this paper, we conduct a systematic study of quantum algorithms and lower bounds for minimizing the maximum of $N$ convex, Lipschitz functions. On one hand, we develop quantum algorithms with an improved complexity bound of $\tilde{O}(\sqrt{N}\epsilon^{-5/3} + \epsilon^{-8/3})$. On the other hand, we prove that quantum algorithms must take $\tilde{\Omega}(\sqrt{N}\epsilon^{-2/3})$ queries to a first-order quantum oracle, showing that our dependence on $N$ is optimal up to poly-logarithmic factors.
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video
Authors: Yanqin Jiang; Li Zhang; Jin Gao; Weiming Hu; Yao Yao
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In this paper, we present Consistent4D, a novel approach for generating 4D dynamic objects from uncalibrated monocular videos. Uniquely, we cast the 360-degree dynamic object reconstruction as a 4D generation problem, eliminating the need for tedious multi-view data collection and camera calibration. This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training dynamic Neural Radiance Fields (DyNeRF). Specifically, we propose a cascade DyNeRF to facilitate stable convergence and temporal continuity under the time-discrete supervision signal. To achieve spatial and temporal consistency of the 4D generation, an interpolation-driven consistency loss is further introduced, which aligns the rendered frames with the interpolated frames from a pre-trained video interpolation model. Extensive experiments show that the proposed Consistent4D significantly outperforms previous 4D reconstruction approaches as well as per-frame 3D generation approaches, opening up new possibilities for 4D dynamic object generation from a single-view uncalibrated video. Project page: https://consistent4d.github.io
Diverse Projection Ensembles for Distributional Reinforcement Learning
Authors: Moritz Akiya Zanger; Wendelin Boehmer; Matthijs T. J. Spaan
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In contrast to classical reinforcement learning, distributional RL algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of the return distribution is generally unknown a priori or arbitrarily complex, a common approach finds approximations within a set of representable, parametric distributions. Typically, this involves a projection of the unconstrained distribution onto the set of simplified distributions. We argue that this projection step entails a strong inductive bias when coupled with neural networks and gradient descent, thereby profoundly impacting the generalization behavior of learned models. In order to facilitate reliable uncertainty estimation through diversity, this work studies the combination of several different projections and representations in a distributional ensemble. We establish theoretical properties of such projection ensembles and derive an algorithm that uses ensemble disagreement, measured by the average $1$-Wasserstein distance, as a bonus for deep exploration. We evaluate our algorithm on the behavior suite benchmark and find that diverse projection ensembles lead to significant performance improvements over existing methods on a wide variety of tasks with the most pronounced gains in directed exploration problems.