GenAI4ELab/papercli-papers huggingface dataset:
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
Authors: Irene Cannistraci; Luca Moschella; Marco Fumero; Valentino Maiorca; Emanuele Rodolà
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases. From a geometric perspective, identifying the classes of transformations and the related invariances that connect these representations is fundamental to unlocking applications, such as merging, stitching, and reusing different neural modules. However, estimating task-specific transformations a priori can be challenging and expensive due to several factors (e.g., weights initialization, training hyperparameters, or data modality). To this end, we introduce a versatile method to directly incorporate a set of invariances into the representations, constructing a product space of invariant components on top of the latent representations without requiring prior knowledge about the optimal invariance to infuse. We validate our solution on classification and reconstruction tasks, observing consistent latent similarity and downstream performance improvements in a zero-shot stitching setting. The experimental analysis comprises three modalities (vision, text, and graphs), twelve pretrained foundational models, nine benchmarks, and several architectures trained from scratch.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Authors: Sumeet Batra; Bryon Tjanaka; Matthew Christopher Fontaine; Aleksei Petrenko; Stefanos Nikolaidis; Gaurav S. Sukhatme
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Training generally capable agents that thoroughly explore their environment and
learn new and diverse skills is a long-term goal of robot learning. Quality Diversity
Reinforcement Learning (QD-RL) is an emerging research area that blends the
best aspects of both fields – Quality Diversity (QD) provides a principled form
of exploration and produces collections of behaviorally diverse agents, while
Reinforcement Learning (RL) provides a powerful performance improvement
operator enabling generalization across tasks and dynamic environments. Existing
QD-RL approaches have been constrained to sample efficient, deterministic off-
policy RL algorithms and/or evolution strategies and struggle with highly stochastic
environments. In this work, we, for the first time, adapt on-policy RL, specifically
Proximal Policy Optimization (PPO), to the Differentiable Quality Diversity (DQD)
framework and propose several changes that enable efficient optimization and
discovery of novel skills on high-dimensional, stochastic robotics tasks. Our new
algorithm, Proximal Policy Gradient Arborescence (PPGA), achieves state-of-
the-art results, including a 4x improvement in best reward over baselines on the
challenging humanoid domain.
Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
Authors: Haoqi Yuan; Zhancun Mu; Feiyang Xie; Zongqing Lu
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Pre-training on task-agnostic large datasets is a promising approach for enhancing the sample efficiency of reinforcement learning (RL) in solving complex tasks. We present PTGM, a novel method that pre-trains goal-based models to augment RL by providing temporal abstractions and behavior regularization. PTGM involves pre-training a low-level, goal-conditioned policy and training a high-level policy to generate goals for subsequent RL tasks. To address the challenges posed by the high-dimensional goal space, while simultaneously maintaining the agent's capability to accomplish various skills, we propose clustering goals in the dataset to form a discrete high-level action space. Additionally, we introduce a pre-trained goal prior model to regularize the behavior of the high-level policy in RL, enhancing sample efficiency and learning stability. Experimental results in a robotic simulation environment and the challenging open-world environment of Minecraft demonstrate PTGM’s superiority in sample efficiency and task performance compared to baselines. Moreover, PTGM exemplifies enhanced interpretability and generalization of the acquired low-level skills.
Memorization Capacity of Multi-Head Attention in Transformers
Authors: Sadegh Mahdavi; Renjie Liao; Christos Thrampoulidis
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive. This paper investigates the memorization abilities of multi-head attention mechanisms, examining how many example sequences they can memorize, as a function of the number of heads and sequence length. Motivated by experimental findings on vision transformers, we introduce novel assumptions about the linear independence of input data, distinct from the commonly used general-position assumption. Under these assumptions, we demonstrate that an attention layer with $H$ heads, dimension $d$, and context size $n < d,$ featuring $\Theta(Hd^2)$ parameters, can memorize $\Omega(Hn)$ examples. Our analysis sheds light on how different attention heads handle various example sequences, aided by the softmax operator’s saturation property. We validate our findings through experiments on synthetic data.
Modeling Boundedly Rational Agents with Latent Inference Budgets
Authors: Athul Paul Jacob; Abhishek Gupta; Jacob Andreas
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We study the problem of modeling a population of agents pursuing unknown goals subject to unknown computational constraints. In standard models of bounded rationality, sub-optimal decision-making is simulated by adding homoscedastic noise to optimal decisions rather than actually simulating constrained inference. In this work, we introduce a latent inference budget model (L-IBM) that models these constraints explicitly, via a latent variable (inferred jointly with a model of agents’ goals) that controls the runtime of an iterative inference algorithm. L-IBMs make it possible to learn agent models using data from diverse populations of suboptimal actors. In three modeling tasks—inferring navigation goals from routes, inferring communicative intents from human utterances, and predicting next moves in human chess games—we show that L-IBMs match or outperforms Boltzmann models of decision-making under uncertainty. Moreover, the inferred inference budgets are themselves meaningful, efficient to compute, and correlated with measures of player skill, partner skill and task difficulty.
The Effectiveness of Random Forgetting for Robust Generalization
Authors: Vijaya Raghavan T Ramkumar; Bahram Zonooz; Elahe Arani
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Deep neural networks are susceptible to adversarial attacks, which can compromise their performance and accuracy. Adversarial Training (AT) has emerged as a popular approach for protecting neural networks against such attacks. However, a key challenge of AT is robust overfitting, where the network's robust performance on test data deteriorates with further training, thus hindering generalization. Motivated by the concept of active forgetting in the brain, we introduce a novel learning paradigm called "Forget to Mitigate Overfitting (FOMO)". FOMO alternates between the forgetting phase, which randomly forgets a subset of weights and regulates the model's information through weight reinitialization, and the relearning phase, which emphasizes learning generalizable features. Our experiments on benchmark datasets and adversarial attacks show that FOMO alleviates robust overfitting by significantly reducing the gap between the best and last robust test accuracy while improving the state-of-the-art robustness. Furthermore, FOMO provides a better trade-off between the standard and robust accuracy outperforming baseline adversarial methods. Finally, our framework is robust to AutoAttacks and increases generalization in many real-world scenarios.
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
Authors: Thanh Tung Le; Khai Nguyen; shanlin sun; Kun Han; Nhat Ho; Xiaohui Xie
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predicted and target meshes remains an open problem. A prevalent approach in current deep learning is the set-based approach which measures the discrepancy between two surfaces by comparing two randomly sampled point-clouds from the two meshes with Chamfer pseudo-distance. Nevertheless, the set-based approach still has limitations such as lacking a theoretical guarantee for choosing the number of points in sampled point-clouds, and the pseudo-metricity and the quadratic complexity of the Chamfer divergence. To address these issues, we propose a novel metric for learning mesh deformation. The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach. By leveraging probability measure space, we gain flexibility in encoding meshes using diverse forms of probability measures, such as continuous, empirical, and discrete measures via \textit{varifold} representation. After having encoded probability measures, we can compare meshes by using the sliced Wasserstein distance which is an effective optimal transport distance with linear computational complexity and can provide a fast statistical rate for approximating the surface of meshes. To the end, we employ a neural ordinary differential equation (ODE) to deform the input surface into the target shape by modeling the trajectories of the points on the surface. Our experiments on cortical surface reconstruction demonstrate that our approach surpasses other competing methods in multiple datasets and metrics.
Lie Group Decompositions for Equivariant Neural Networks
Authors: Mircea Mironenco; Patrick Forré
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Invariance and equivariance to geometrical transformations have proven to be very useful inductive biases when training (convolutional) neural network models, especially in the low-data regime.
Much work has focused on the case where the symmetry group employed is compact or abelian, or both.
Recent work has explored enlarging the class of transformations used to the case of Lie groups, principally through the use of their Lie algebra, as well as the group exponential and logarithm maps.
The applicability of such methods to larger transformation groups is limited by the fact that depending on the group of interest $G$, the exponential map may not be surjective.
Further limitations are encountered when $G$ is neither compact nor abelian.
Using the structure and geometry of Lie groups and their homogeneous spaces, we present a framework by which it is possible to work with such groups primarily focusing on the Lie groups $G = \textnormal{GL}^{+}(n, \mathbb{R})$ and $G = \textnormal{SL}(n, \mathbb{R})$, as well as their representation as affine transformations $\mathbb{R}^{n} \rtimes G$.
Invariant integration as well as a global parametrization is realized by decomposing the "larger" groups into subgroups and submanifolds which can be handled individually.
Under this framework, we show how convolution kernels can be parametrized to build models equivariant with respect to affine transformations.
We evaluate the robustness and out-of-distribution generalisation capability of our model on the standard affine-invariant benchmark classification task, where we outperform all previous equivariant models as well as all Capsule Network proposals.
Efficient Heterogeneous Meta-Learning via Channel Shuffling Modulation
Authors: Minh Hoang; Carl Kingsford
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We tackle the problem of meta-learning across heterogenous tasks. This problem seeks to extract and generalize transferable meta-knowledge through streaming task sets from a multi-modal task distribution. The extracted meta-knowledge can be used to create predictors for new tasks using a small number of labeled samples. Most meta-learning methods assume a homogeneous task distribution, thus limiting their generalization capacity when handling multi-modal task distributions. Recent work has shown that the generalization of meta-learning depends on the similarity of tasks in the training distribution, and this has led to many clustering approaches that aim to detect homogeneous clusters of tasks. However, these methods suffer from a significant increase in parameter complexity. To overcome this weakness, we propose a new heterogeneous meta-learning strategy that efficiently captures the multi-modality of the task distribution via modulating the routing between convolution channels in the network, instead of directly modulating the network weights. This new mechanism can be cast as a permutation learning problem. We further introduce a novel neural permutation layer based on the classical Benes routing network, which has sub-quadratic parameter complexity in the total number of channels, as compared to the quadratic complexity of the state-of-the-art Gumbel-Sinkhorn layer. We demonstrate our approach on various multi-modal meta-learning benchmarks, showing that our framework outperforms previous methods in both generalization accuracy and convergence speed.
To Grok or not to Grok: Disentangling Generalization and Memorization on Corrupted Algorithmic Datasets
Authors: Darshil Doshi; Aritra Das; Tianyu He; Andrey Gromov
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Robust generalization is a major challenge in deep learning, particularly when the number of trainable parameters is very large. In general, it is very difficult to know if the network has memorized a particular set of examples or understood the underlying rule (or both). Motivated by this challenge, we study an interpretable model where generalizing representations are understood analytically, and are easily distinguishable from the memorizing ones. Namely, we consider multi-layer perceptron (MLP) and Transformer architectures trained on modular arithmetic tasks, where ($\xi \cdot 100\\%$) of labels are corrupted (*i.e.* some results of the modular operations in the training set are incorrect). We show that (i) it is possible for the network to memorize the corrupted labels *and* achieve $100\\%$ generalization at the same time; (ii) the memorizing neurons can be identified and pruned, lowering the accuracy on corrupted data and improving the accuracy on uncorrupted data; (iii) regularization methods such as weight decay, dropout and BatchNorm force the network to ignore the corrupted data during optimization, and achieve $100\\%$ accuracy on the uncorrupted dataset; and (iv) the effect of these regularization methods is ("mechanistically") interpretable: weight decay and dropout force all the neurons to learn generalizing representations, while BatchNorm de-amplifies the output of memorizing neurons and amplifies the output of the generalizing ones. Finally, we show that in the presence of regularization, the training dynamics involves two consecutive stages: first, the network undergoes *grokking* dynamics reaching high train *and* test accuracy; second, it unlearns the memorizing representations, where the train accuracy suddenly jumps from $100\\%$ to $100 (1-\xi)\\%$.
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Authors: Dongqi Fu; Zhigang Hua; Yan Xie; Jin Fang; Si Zhang; Kaan Sancak; Hao Wu; Andrey Malevich; Jingrui He; Bo Long
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation vectors, resulting in quadratic computational costs that are unaffordable for large-scale graph data. Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations. Facing this bottleneck, (1) we start by assigning each node a token list that is sampled by personalized PageRank (PPR) and then apply standard multi-head self-attention only on this list to compute its node representations. This PPR tokenization method decouples model training from complex graph topological information and makes heavy feature engineering offline and independent, such that mini-batch training of graph transformers is possible by loading each node's token list in batches. We further prove this PPR tokenization is viable as a graph convolution network with a fixed polynomial filter and jumping knowledge. However, only using personalized PageRank may limit information carried by a token list, which could not support different graph inductive biases for model training. To this end, (2) we rewire graphs by introducing multiple types of virtual connections through structure- and content-based super nodes that enable PPR tokenization to encode local and global contexts, long-range interaction, and heterophilous information into each node's token list, and then formalize our $\underline{\textbf{V}}$irtual $\underline{\textbf{C}}$onnection $\underline{\textbf{R}}$anking based $\underline{\textbf{Graph}}$ Trans$\underline{\textbf{former}}$ (VCR-Graphormer). Overall, VCR-Graphormer needs $O(m+klogk)$ complexity for graph tokenization as compared to $O(n^{3})$ of previous works. The [code](https://github.com/DongqiFu/VCR-Graphormer) is provided.
Optimistic Bayesian Optimization with Unknown Constraints
Authors: Quoc Phong Nguyen; Wan Theng Ruth Chew; Le Song; Bryan Kian Hsiang Low; Patrick Jaillet
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Though some research efforts have been dedicated to constrained Bayesian optimization (BO), there remains a notable absence of a principled approach with a theoretical performance guarantee in the decoupled setting. Such a setting involves independent evaluations of the objective function and constraints at different inputs, and is hence a relaxation of the commonly-studied coupled setting where functions must be evaluated together. As a result, the decoupled setting requires an adaptive selection between evaluating either the objective function or a constraint, in addition to selecting an input (in the coupled setting). This paper presents a novel constrained BO algorithm with a provable performance guarantee that can address the above relaxed setting. Specifically, it considers the fundamental trade-off between exploration and exploitation in constrained BO, and, interestingly, affords a noteworthy connection to active learning. The performance of our proposed algorithms is also empirically evaluated using several synthetic and real-world optimization problems.
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Authors: Shoumik Saha; Wenxiao Wang; Yigitcan Kaya; Soheil Feizi; Tudor Dumitras
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for malware-detection models against evasion attacks. However, most if not all existing defenses against evasion attacks suffer from sizable performance degradation and/or can defend against only specific attacks, which makes them less practical in real-world settings. In this work, we develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the *de-randomized smoothing* technique for the domain of malware detection. Specifically, we propose a *window ablation* scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. After showing how DRSM is theoretically robust against attacks with contiguous adversarial bytes, we verify its performance and certified robustness experimentally, where we observe only marginal accuracy drops as the cost of robustness. To our knowledge, we are the first to offer certified robustness in the realm of static detection of malware executables. More surprisingly, through evaluating DRSM against $9$ empirical attacks of different types, we observe that the proposed defense is empirically robust to some extent against a diverse set of attacks, some of which even fall out of the scope of its original threat model. In addition, we collected $15.5K$ recent benign raw executables from diverse sources, which will be made public as a dataset called PACE (Publicly Accessible Collection(s) of Executables) to alleviate the scarcity of publicly available benign datasets for studying malware detection and provide future research with more representative data of the time. Our code and dataset are available at - https://github.com/ShoumikSaha/DRSM
On the Variance of Neural Network Training with respect to Test Sets and Distributions
Authors: Keller Jordan
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Neural network trainings are stochastic, causing the performance of trained networks to vary across repeated runs of training.
We contribute the following results towards understanding this variation.
(1) Despite having significant variance on their test-sets, we demonstrate that standard CIFAR-10 and ImageNet trainings have little variance in their performance on the test-distributions from which their test-sets are sampled.
(2) We introduce the independent errors assumption and show that it suffices to recover the structure and variance of the empirical accuracy distribution across repeated runs of training.
(3) We prove that test-set variance is unavoidable given the observation that ensembles of identically trained networks are calibrated (Jiang et al., 2021), and demonstrate that the variance of binary classification trainings closely follows a simple formula based on the error rate and number of test examples.
(4) We conduct preliminary studies of data augmentation, learning rate, finetuning instability and distribution-shift through the lens of variance between runs.