GenAI4ELab/papercli-papers huggingface dataset:
Critical Sampling for Robust Evolution Behavior Learning of Unknown Dynamical Systems
Authors: Ce Zhang; Siqi Wu; Kailiang Wu; Zhihai He
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the following new and important problem: given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples? In this work, we propose to explore this problem based on a design approach. Specifically, starting from a small initial set of samples, we adaptively discover and collect critical samples to achieve increasingly accurate learning of the system evolution. One central challenge here is that we do not know the network modeling error of the ground-truth system state, which is however needed for critical sampling. To address this challenge, we introduce a multi-step reciprocal prediction network where a forward evolution network and a backward evolution network are designed to learn and predict the temporal evolution behavior in the forward and backward time directions, respectively. Very interestingly, we find that the desired network modeling error is highly correlated with the multi-step reciprocal prediction error. More importantly, this multi-step reciprocal prediction error can be directly computed from the current system state without knowing the ground-truth or data statistics. This allows us to perform dynamic selection of critical samples from regions with high network modeling errors and develop an adaptive sampling-learning method for dynamical systems. To achieve accurate and robust learning from this small set of critical samples, we introduce a joint spatial-temporal evolution network which incorporates spatial dynamics modeling into the temporal evolution prediction for robust learning of the system evolution operator with  few samples. Our extensive experimental results demonstrate that our proposed method is able to dramatically reduce the numbers of samples needed for effective learning and accurate prediction of evolution behaviors of unknown dynamical systems by up to hundreds of times, especially for high-dimensional dynamical systems.
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
Authors: Linfeng Zhao; Xupeng Zhu; Lingzhi Kong; Robin Walters; Lawson L.S. Wong
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: To achieve this, we draw inspiration from equivariant convolution networks and model the path planning problem as a set of signals over grids. We demonstrate that value iteration can be treated as a linear equivariant operator, which is effectively a steerable convolution. Building upon Value Iteration Networks (VIN), we propose a new Symmetric Planning (SymPlan) framework that incorporates rotation and reflection symmetry using steerable convolution networks. We evaluate our approach on four tasks: 2D navigation, visual navigation, 2 degrees of freedom (2-DOF) configuration space manipulation, and 2-DOF workspace manipulation. Our experimental results show that our symmetric planning algorithms significantly improve training efficiency and generalization performance compared to non-equivariant baselines, including VINs and GPPN.
An Extensible Multi-modal Multi-task Object Dataset with Materials
Authors: Trevor Scott Standley; Ruohan Gao; Dawn Chen; Jiajun Wu; Silvio Savarese
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present EMMa, an Extensible, Multimodal dataset of Amazon product listings that contains rich Material annotations. It contains more than 2.8 million objects, each with image(s), listing text, mass, price, product ratings, and position in Amazon’s product-category taxonomy. We also design a comprehensive taxonomy of 182 physical materials (e.g., Plastic → Thermoplastic → Acrylic). Objects areannotated with one or more materials from this taxonomy. With the numerous attributes available for each object, we develop a Smart Labeling framework to quickly add new binary labels to all objects with very little manual labeling effort, making the dataset extensible. Each object attribute in our dataset can be included in either the model inputs or outputs, leading to combinatorial possibilities in task configurations. For example, we can train a model to predict the object category from the listing text, or the mass and price from the product listing image. EMMa offers a new benchmark for multi-task learning in computer vision and NLP, and allows practitioners to efficiently add new tasks and object attributes at scale.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Authors: Alan Jeffares; Tennison Liu; Jonathan Crabbé; Fergus Imrie; Mihaela van der Schaar
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on various forms of regularization to prevent overfitting and provide strong generalization performance. Existing regularization techniques include broad modelling decisions such as choice of architecture, loss functions, and optimization methods. In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions. The gradient attribution of an activation with respect to a given input feature suggests how the neuron attends to that feature, and is often employed to interpret the predictions of deep networks. In TANGOS, we take a different approach and incorporate neuron attributions directly into training to encourage orthogonalization and specialization of latent attributions in a fully-connected network. Our regularizer encourages neurons to focus on sparse, non-overlapping input features and results in a set of diverse and specialized latent units. In the tabular domain, we demonstrate that our approach can lead to improved out-of-sample generalization performance, outperforming other popular regularization methods. We provide insight into why our regularizer is effective and demonstrate that TANGOS can be applied jointly with existing methods to achieve even greater generalization performance.
Learning Efficient Hybrid Particle-continuum Representations of Non-equilibrium N-body Systems
Authors: Tailin Wu; Michael Sun; H.G. Jason Chou; Pranay Samala; Sithipont Cholsaipant; Sophia Kivelson; Jacqueline Yau; Zhitao Ying; E. Paulo Alves; Jure Leskovec; Frederico Fiuza
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: An important class of multi-scale, non-equilibrium, N-body physical systems deals with an interplay between particle and continuum phenomena. These include hypersonic flow and plasma dynamics, materials science, and astrophysics. Hybrid solvers that combine particle and continuum representations could provide an efficient framework to model these systems. However, the coupling between these two representations has been a key challenge, which is often limited to inaccurate or incomplete prescriptions. In this work, we introduce a method for Learning Hybrid Particle-Continuum (LHPC) models from the data of first-principles particle simulations. LHPC analyzes the local velocity-space particle distribution function and separates it into near-equilibrium (thermal) and far-from-equilibrium (non-thermal) components. The most computationally-intensive particle solver is used to advance the non-thermal particles, whereas a neural network solver is used to efficiently advance the thermal component using a continuum representation. Most importantly, an additional neural network learns the particle-continuum coupling: the dynamical exchange of mass, momentum, and energy between the particle and continuum representations. Training of the different neural network components is done in an integrated manner to ensure global consistency and stability of the LHPC model. We demonstrate our method in an intense laser-plasma interaction problem involving highly nonlinear, far-from-equilibrium dynamics associated with the coupling between electromagnetic fields and multiple particle species. More efficient modeling of these interactions is critical for the design and optimization of compact accelerators for material science and medical applications. Our method achieves an important balance between accuracy and speed: LHPC is 8 times faster than a classical particle solver and achieves up to 6.8-fold reduction of long-term prediction error for key quantities of interest compared to deep-learning baselines using uniform representations.
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation
Authors: Yao Shu; Zhongxiang Dai; Weicong Sng; Arun Verma; Patrick Jaillet; Bryan Kian Hsiang Low
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Zeroth-order (ZO) optimization, in which the derivative is unavailable, has recently succeeded in many important machine learning applications. Existing algorithms rely on finite difference (FD) methods for derivative estimation and gradient descent (GD)-based approaches for optimization. However, these algorithms suffer from query inefficiency because many additional function queries are required for derivative estimation in their every GD update, which typically hinders their deployment in real-world applications where every function query is expensive. To this end, we propose a trajectory-informed derivative estimation method which only employs the optimization trajectory (i.e., the history of function queries during optimization) and hence can eliminate the need for additional function queries to estimate a derivative. Moreover, based on our derivative estimation, we propose the technique of dynamic virtual updates, which allows us to reliably perform multiple steps of GD updates without reapplying derivative estimation. Based on these two contributions, we introduce the zeroth-order optimization with trajectory-informed derivative estimation (ZoRD) algorithm for query-efficient ZO optimization. We theoretically demonstrate that our trajectory-informed derivative estimation and our ZoRD algorithm improve over existing approaches, which is then supported by our real-world experiments such as black-box adversarial attack, non-differentiable metric optimization, and derivative-free reinforcement learning.
Catastrophic overfitting is a bug but it is caused by features
Authors: Guillermo Ortiz-Jimenez; Pau de Jorge; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Grégory Rogez; Philip Torr
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial training (AT) is the de facto method to build robust neural networks, but it is computationally expensive. To overcome this, fast single-step attacks can be used, but doing so is prone to catastrophic overfitting (CO). This is when networks gain non-trivial robustness during the first stages of AT, but then reach a breaking point where they become vulnerable in just a few iterations. Although some works have succeeded at preventing CO, the different mechanisms that lead to this failure mode are still poorly understood. In this work, we study the onset of CO in single-step AT methods through controlled modifications of typical datasets of natural images. In particular, we show that CO can be induced when injecting the images with seemingly innocuous features that are very useful for non-robust classification but need to be combined with other features to obtain a robust classifier. This new perspective provides important insights into the mechanisms that lead to CO and improves our understanding of the general dynamics of adversarial training.
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Authors: Xuyang Zhao; Tianqi Du; Yisen Wang; Jun Yao; Weiran Huang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shift scenarios, where the downstream and training distributions differ. However, the theoretical understanding of its transferability remains limited. In this paper, we develop a theoretical framework to analyze the transferability of self-supervised contrastive learning, by investigating the impact of data augmentation on it. Our results reveal that the downstream performance of contrastive learning depends largely on the choice of data augmentation.  Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability. Based on these theoretical insights, we propose a novel method called Augmentation-robust Contrastive Learning (ArCL), which guarantees to learn domain-invariant features and can be easily integrated with existing contrastive learning algorithms. We conduct experiments on several datasets and show that ArCL significantly improves the transferability of contrastive learning.
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Authors: Fan Chen; Yu Bai; Song Mei
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Partial Observability---where agents can only observe partial information about the true underlying state of the system---is ubiquitous in real-world applications of Reinforcement Learning (RL). Theoretically, learning a near-optimal policy under partial observability is known to be hard in the worst case due to an exponential sample complexity lower bound. Recent work has identified several tractable subclasses that are learnable with polynomial samples, such as Partially Observable Markov Decision Processes (POMDPs) with certain revealing or decodability conditions. However, this line of research is still in its infancy, where (1) unified structural conditions enabling sample-efficient learning are lacking; (2) existing sample complexities for known tractable subclasses are far from sharp; and (3) fewer sample-efficient algorithms are available than in fully observable RL.

This paper advances all three aspects above for Partially Observable RL in the general setting of Predictive State Representations (PSRs). First, we propose a natural and unified structural condition for PSRs called \emph{B-stability}. B-stable PSRs encompasses the vast majority of known tractable subclasses such as weakly revealing POMDPs, low-rank future-sufficient POMDPs, decodable POMDPs, and regular PSRs. Next, we show that any B-stable PSR can be learned with polynomial samples in relevant problem parameters. When instantiated in the aforementioned subclasses, our sample complexities improve substantially over the current best ones. Finally, our results are achieved by three algorithms simultaneously: Optimistic Maximum Likelihood Estimation, Estimation-to-Decisions, and Model-Based Optimistic Posterior Sampling. The latter two algorithms are new for sample-efficient learning of POMDPs/PSRs.
We additionally design a variant of the Estimation-to-Decisions algorithm to perform sample-efficient \emph{all-policy model estimation} for B-stable PSRs, which also yields guarantees for reward-free learning as an implication.
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence
Authors: Margalit Glasgow; Colin Wei; Mary Wootters; Tengyu Ma
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A major challenge in modern machine learning is theoretically understanding the generalization properties of overparameterized models. Many existing tools rely on uniform convergence  (UC), a property that, when it holds, guarantees that the test loss will be close to the training loss, uniformly over a class of candidate models. Nagarajan and Kolter (2019) show that in certain simple linear and neural-network settings, any uniform convergence bound will be vacuous, leaving open the question of how to prove generalization in settings where UC fails. Our main contribution is proving novel generalization bounds in two such settings, one linear, and one non-linear. We study the linear classification setting of Nagarajan and Kolter (2019), and a quadratic ground truth function learned via a two-layer neural network in the non-linear regime. We prove a new type of margin bound showing that above a certain signal-to-noise threshold, any near-max-margin classifier will achieve almost no test loss in these two settings. Our results show that near-max-margin is important: while any model that achieves at least a $(1 - \epsilon)$-fraction of the max-margin generalizes well, a classifier achieving half of the max-margin may fail terribly. Our analysis provides insight on why memorization can coexist with generalization: we show that in this challenging regime where generalization occurs but UC fails, near-max-margin classifiers simultaneously contain some generalizable components and some overfitting components that memorize the data. The presence of the overfitting components is enough to preclude UC, but the near-extremal margin guarantees that sufficient generalizable components are present.
CCIL: Context-conditioned imitation learning for urban driving
Authors: Ke Guo; Wei Jing; Wenxi Liu; Junbo Chen; Jia Pan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Imitation learning is a promising solution to the challenging autonomous urban driving task as experienced human drivers can effortlessly tackle highly complex driving scenarios. Behavior cloning is the most widely applied imitation learning approach in autonomous driving due to its exemption from potentially risky online interactions, but it suffers from the covariate shift issue. To mitigate this problem, we propose a context-conditioned imitation learning approach that learns a policy to map the context state into the ego vehicle's state instead of the typical formulation from both ego and context state to the ego action.  Besides, to make full use of the spatial and temporal relations in the context to infer the ego future states, we design a novel policy network based on the Transformer, whose attention mechanism has demonstrated excellent performance in capturing relations. Finally, during evaluation, a linear quadratic controller is employed to produce smooth planning based on the predicted states from the policy network. Experiments on the real-world large-scale Lyft and nuPlan datasets demonstrate that our method can surpass the state-of-the-art method significantly.
EA-HAS-Bench: Energy-aware Hyperparameter and Architecture Search Benchmark
Authors: Shuguang Dou; XINYANG JIANG; Cai Rong Zhao; Dongsheng Li
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: The energy consumption for training deep learning models is increasing at an alarming rate due to the growth of training data and model scale, resulting in a negative impact on carbon neutrality. Energy consumption is an especially pressing issue for AutoML algorithms because it usually requires repeatedly training large numbers of computationally intensive deep models to search for optimal configurations. This paper takes one of the most essential steps in developing energy-aware (EA) NAS methods, by providing a benchmark that makes EA-NAS research more reproducible and accessible. Specifically, we present the first large-scale energy-aware benchmark that allows studying AutoML methods to achieve better trade-offs between performance and search energy consumption, named EA-HAS-Bench. EA-HAS-Bench provides a large-scale architecture/hyperparameter joint search space, covering diversified configurations related to energy consumption. Furthermore, we propose a novel surrogate model specially designed for large joint search space, which proposes a Bezier curve-based model to predict learning curves with unlimited shape and length. Based on the proposed dataset, we new energy-aware AutoML method that arms existing AutoML algorithms to consider the search energy consumption, and our experiments show that the modified energy-aware AutoML methods achieve a better trade-off between energy consumption and model performance.
Revisiting Populations in multi-agent Communication
Authors: Paul Michel; Mathieu Rita; Kory Wallace Mathewson; Olivier Tieleman; Angeliki Lazaridou
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite evidence from cognitive sciences that larger groups of speakers tend to develop more structured languages in human communication, scaling up to populations has failed to yield significant benefits in emergent multi-agent communication. In this paper we advocate for an alternate population-level training paradigm for referential games based on the idea of "partitioning" the agents into sender-receiver pairs and limiting co-adaptation across pairs. We show that this results in optimizing a different objective at the population level, where agents maximize (1) their respective "internal" communication accuracy and (2) some measure of alignment between agents. In experiments, we find that this leads to the emergence of languages that are significantly more compositional. Moreover, when agents are trained in populations that are not fully connected (ie. not all agent pairs interact at training time), this approach reduces multi-linguality and improves zero-shot communication with new agents (ie. agents are able to communicate successfully with other agents outside their training partners).
Dynamical Isometry for Residual Networks
Authors: Advait Harshal Gadhikar; Rebekka Burkholz
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The training success, training speed and generalization ability of neural networks rely crucially on the choice of random parameter initialization. It has been shown for multiple architectures that initial dynamical isometry is particularly advantageous. Known initialization schemes for residual blocks, however, miss this property and suffer from degrading separability of different inputs for increasing depth and instability without Batch Normalization or lack feature diversity. We propose a random initialization scheme, Risotto, that achieves perfect dynamical isometry for residual networks with ReLU activation functions even for finite depth and width. It balances the contributions of the residual and skip branches unlike other schemes, which initially bias towards the skip connections. In experiments, we demonstrate that in most cases our approach outperforms initialization schemes proposed to make Batch Normalization obsolete, including Fixup and SkipInit, and facilitates stable training. Also in combination with Batch Normalization, we find that Risotto often achieves the overall best result.
Using the Training History to Detect and Prevent Overfitting in Deep Learning Models
Authors: Hao Li; Gopi Krishnan Rajbahadur; Dayi Lin; Cor-Paul Bezemer; Zhen Jiang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Overfitting of deep learning models on training data leads to poor generalizability on unseen data. Overfitting can be (1) prevented (e.g., using dropout or early stopping) or (2) detected in a trained model (e.g., using correlation-based methods). We propose a method that can both detect and prevent overfitting based on the training history (i.e., validation losses). Our method first trains a time series classifier on training histories of overfit models. This classifier is then used to detect if a trained model is overfit. In addition, our trained classifier can be used to prevent overfitting by identifying the optimal point to stop a model's training. We evaluate our method on its ability to identify and prevent overfitting in real-world samples (collected from papers published in the last 5 years at top AI venues). We compare our method against correlation-based detection methods and the most commonly used prevention method (i.e., early stopping). Our method achieves an F1 score of 0.91 which is at least 5% higher than the current best-performing non-intrusive overfitting detection method. In addition, our method can find the optimal stopping point and avoid overfitting at least 32% earlier than early stopping and achieve at least the same accuracy (often better) as early stopping.