GenAI4ELab/papercli-papers huggingface dataset:
Improving Object-centric Learning with Query Optimization
Authors: Baoxiong Jia; Yu Liu; Siyuan Huang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-Attention module has played an important role with its simple yet effective design and fostered many powerful variants. These methods, however, have been exceedingly difficult to train
without supervision and are ambiguous in the notion of object, especially for complex natural scenes. In this paper, we propose to address these issues by investigating the potential of learnable queries as initializations for Slot-Attention learning, uniting it with efforts from existing attempts on improving Slot-Attention learning with bi-level optimization. With simple code adjustments on Slot-Attention, our model, Bi-level Optimized Query Slot Attention, achieves state-of-the-art results on 3 challenging synthetic and 7 complex real-world datasets in unsupervised image segmentation and reconstruction, outperforming previous baselines by a large margin. We provide thorough ablative studies to validate the necessity and effectiveness of our design. Additionally, our model exhibits great potential for concept binding and zero-shot learning. Our work is made publicly available at https://bo-qsa.github.io.
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
Authors: Avrajit Ghosh; He Lyu; Xitong Zhang; Rongrong Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: It is well known that the finite step-size ($h$) in Gradient descent (GD) implicitly regularizes solutions to flatter minimas. A natural question to ask is \textit{Does the momentum parameter $\beta$ (say) play a role in implicit regularization in Heavy-ball (H.B) momentum accelerated gradient descent (GD+M)?}. To answer this question, first, we show that  the trajectory traced by discrete H.B momentum update (GD+M) is $O(h^2)$ close to a continuous trajectory induced by a modified loss, which consists of an original loss and an implicit regularizer. This implicit regularizer for (GD+M) is indeed stronger than that of (GD) by factor of $(\frac{1+\beta}{1-\beta})$, thus explaining why (GD+M) shows better generalization performance and higher test accuracy than (GD). Furthermore, we extend our analysis to stochastic version of gradient descent with momentum (SGD+M) and propose a deterministic continuous trajectory that is $O(h^2)$ close to the discrete update of (SGD+M) in a strong approximation sense. We explore the implicit regularization in (SGD+M) and (GD+M) through a series of experiments validating our theory.
Continual Learning with Soft-Masking of Parameter-Level Gradient Flow
Authors: Tatsuya Konishi; Mori Kurokawa; Chihiro Ono; Zixuan Ke; Gyuhak Kim; Bing Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Several techniques have achieved learning with no CF. However, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i.e., as more tasks are learned, the performance deteriorates. The goal of this paper is threefold: (1) overcoming CF, (2) encouraging KT, and (3) tackling the capacity problem. A novel and simple technique (called SPG) is proposed that soft-masks (partially blocks) parameter updating in training based on the importance of each parameter to old tasks. Each task still uses the full network, i.e., no monopoly of any part of the network by any task, which enables maximum KT and reduction of capacity usage. Extensive experiments demonstrate the effectiveness of SPG in achieving all three objectives. More notably, it attains significant transfer of knowledge not only among similar tasks (with shared knowledge) but also among dissimilar tasks (with little shared knowledge) while preventing CF.
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution
Authors: Shuang Ao; Tianyi Zhou; Guodong Long; Jing Jiang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes. In contrast, current reinforcement learning (RL) studies mainly focus on training an agent with a fixed morphology (e.g., skeletal structure and joint attributes) in a fixed environment, which can hardly generalize to changing environments or new tasks. In this paper, we optimize an RL agent and its morphology through ''morphology-environment co-evolution (MECE)'', in which the morphology keeps being updated to adapt to the changing environment, while the environment is modified progressively to bring new challenges and stimulate the improvement of the morphology. This leads to a curriculum to train generalizable RL, whose morphology and policy are optimized for different environments. Instead of hand-crafting the curriculum, we train two policies to automatically change the morphology and the environment. To this end, (1) we develop two novel and effective rewards for the two policies, which are solely based on the learning dynamics of the RL agent; (2) we design a scheduler to automatically determine when to change the environment and the morphology. We find these two designs are critical to the success of MECE, as  verified by extensive ablation studies. In experiments on two classes of tasks, the morphology and RL policies trained via MECE exhibit significantly better generalization performance in unseen test environments than SOTA morphology optimization methods. Our ablation studies on the two MECE policies further show that the co-evolution between the morphology and environment is the key to the success.
Continual evaluation for lifelong learning: Identifying the stability gap
Authors: Matthias De Lange; Gido M van de Ven; Tinne Tuytelaars
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery. We refer to this intriguing but potentially problematic phenomenon as the stability gap. The stability gap had likely remained under the radar due to standard practice in the field of evaluating continual learning models only after each task. Instead, we establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance. Empirically we show that experience replay, constraint-based replay, knowledge-distillation, and parameter regularization methods are all prone to the stability gap; and that the stability gap can be observed in class-, task-, and domain-incremental learning benchmarks. Additionally, a controlled experiment shows that the stability gap increases when tasks are more dissimilar. Finally, by disentangling gradients into plasticity and stability components, we propose a conceptual explanation for the stability gap.
Model-Based Decentralized Policy Optimization 
Authors: Hao Luo; Jiechuan Jiang; Zongqing Lu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Decentralized policy optimization has been commonly used in cooperative multi-agent tasks. However, since all agents are updating their policies simultaneously, from the perspective of individual agents, the environment is non-stationary, resulting in it being hard to guarantee monotonic policy improvement. To help the policy improvement be stable and monotonic, we propose model-based decentralized policy optimization (MDPO), which incorporates a latent variable function to help construct the transition and reward function from an individual perspective. We theoretically analyze that the policy optimization of MDPO is more stable than model-free decentralized policy optimization. Moreover, due to non-stationarity, the latent variable function is varying and hard to be modeled. We further propose a latent variable prediction method to reduce the error of latent variable function, which theoretically contributes to the monotonic policy improvement. Empirically, MDPO can indeed obtain superior performance than model-free decentralized policy optimization in a variety of cooperative multi-agent tasks.
BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging
Authors: Yuchen Liu; Ziyu Jia
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Sleep staging is helpful in assessing sleep quality and diagnosing sleep disorders. However, how to adequately capture the temporal and spatial relations of the brain during sleep remains a challenge. In particular, existing methods cannot adaptively infer spatial-temporal relations of the brain under different sleep stages. In this paper, we propose a novel Bayesian spatial-temporal relation inference neural network, named Bayesian spatial-temporal transformer (BSTT), for sleep staging. Our model is able to adaptively infer brain spatial-temporal relations during sleep for spatial-temporal feature modeling through a well-designed Bayesian relation inference component. Meanwhile, our model also includes a spatial transformer for extracting brain spatial features and a temporal transformer for capturing temporal features. Experiments show that our BSTT outperforms state-of-the-art baselines on ISRUC and MASS datasets. In addition, the visual analysis shows that the spatial-temporal relations obtained by BSTT inference have certain interpretability for sleep staging.
Kernel Neural Optimal Transport
Authors: Alexander Korotin; Daniil Selikhanovych; Evgeny Burnaev
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost may learn fake plans which are not optimal. To resolve this issue, we introduce kernel weak quadratic costs. We show that they provide improved theoretical guarantees and practical performance. We test NOT with kernel costs on the unpaired image-to-image translation task.
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Authors: Paria Rashidinejad; Hanlin Zhu; Kunhe Yang; Stuart Russell; Jiantao Jiao
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Offline reinforcement learning (RL), which aims at learning good policies from historical data, has received significant attention over the past years. Much effort has focused on improving offline RL practicality by addressing the prevalent issue of partial data coverage through various forms of conservative policy learning. While the majority of algorithms do not have finite-
sample guarantees, several provable conservative offline RL algorithms are designed and analyzed within the single-policy concentrability framework that handles partial coverage. Yet, in the nonlinear function approximation setting where confidence intervals are difficult to obtain, existing provable algorithms suffer from computational intractability, prohibitively strong assumptions, and suboptimal statistical rates. In this paper, we leverage the marginalized importance sampling (MIS) formulation of RL and present the first set of offline RL algorithms that are statistically optimal and practical under general function approximation and single-policy concentrability, bypassing the need for uncertainty quantification. We identify that the key to successfully solving the sample-based approximation of the MIS problem is ensuring that certain occupancy validity constraints are nearly satisfied. We enforce these constraints by a novel application of the augmented Lagrangian method and prove the following result: with the MIS formulation, augmented Lagrangian is enough for statistically optimal offline RL. In stark contrast to prior algorithms that induce additional conservatism through methods such as behavior regularization, our approach provably eliminates this need and reinterprets regularizers as "enforcers of occupancy validity" than "promoters of conservatism."
A Curriculum Perspective to Robust Loss Functions
Authors: Zebin Ou; Yue Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning with noisy labels is a fundamental problem in machine learning. Much work has been done in designing loss functions that are theoretically robust against label noise. However, it remains unclear why robust loss functions can underfit and why loss functions deviating from theoretical robustness conditions can appear robust. To elucidate these questions, we show that most robust loss functions differ only in the sample-weighting curriculums they implicitly define. The curriculum perspective enables straightforward analysis of the training dynamics with each loss function, which has not been considered in existing theoretical approaches. We show that underfitting can be attributed to marginal sample weights during training, and noise robustness can be attributed to larger weights for clean samples than noisy samples. With a simple fix to the curriculums, robust loss functions that severely underfit can become competitive with the state-of-the-art.
On the Universality of Langevin Diffusion for Private Euclidean (Convex) Optimization
Authors: Arun Ganesh; Abhradeep Guha Thakurta; Jalaj Kumar Upadhyay
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we revisit the problem of differentially private empirical risk minimization (DP-ERM) and differentially private stochastic convex optimization (DP-SCO). We show that a well-studied continuous time algorithm from statistical physics, called Langevin diffusion (LD), simultaneously provides optimal privacy/utility trade-offs for both DP-ERM and DP-SCO, under $\epsilon$-DP, and $(\epsilon,\delta)$-DP both for convex and strongly convex loss functions. We provide new time and dimension independent uniform stability properties of LD, with which we provide the corresponding optimal excess population risk guarantees for $\epsilon$-DP.  An important attribute of our DP-SCO guarantees for $\epsilon$-DP is that they match the non-private optimal bounds as $\epsilon\to\infty$.
Scaling Laws for a Multi-Agent Reinforcement Learning Model
Authors: Oren Neumann; Claudius Gros
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The recent observation of neural power-law scaling relations has made a significant impact in the field of deep learning. A substantial amount of attention has been dedicated as a consequence to the description of scaling laws, although mostly for supervised learning and only to a reduced extent for reinforcement learning frameworks. In this paper we present an extensive study of performance scaling for a cornerstone reinforcement learning algorithm, AlphaZero. On the basis of a relationship between Elo rating, playing strength and power-law scaling, we train AlphaZero agents on the games Connect Four and Pentago and analyze their performance. We find that player strength scales as a power law in neural network parameter count when not bottlenecked by available compute, and as a power of compute when training optimally sized agents. We observe nearly identical scaling exponents for both games. Combining the two observed scaling laws we obtain a power law relating optimal size to compute similar to the ones observed for language models. We find that the predicted scaling of optimal neural network size fits our data for both games. This scaling law implies that previously published state-of-the-art game-playing models are significantly smaller than their optimal size, given the respective compute budgets. We also show that large AlphaZero models are more sample efficient, performing better than smaller models with the same amount of training data.
Mask-tuning: Towards  Improving  Pre-trained Language Models' Generalization
Authors: Somayeh Ghanbarzadeh; Hamid Palangi; Yan Huang; Radames Cruz Moreno; Hamed Khanpour
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Pre-trained language models have the known generalization problem. This issue emerges from the pre-trained language models' learning process that heavily relies on spurious correlations, which work for the majority of training examples but do not hold in general. As a consequence, the models' performance drops substantially on out-of-distribution datasets. Previous studies proposed various solutions, including data augmentation and learning process improvement. In this paper, we present Mask-tuning, an approach that alleviates the impact of spurious correlations on the fine-tuning learning process. To achieve this goal, Mask-tuning integrates masked language training into the fine-tuning learning process. In this case, Mask-tuning perturbs the linguistic relation of downstream tasks' training examples and computes masked language training loss. Then, the perturbed examples are fed into fine-tuning process to be classified based on their ground-truth label and compute the fine-tuning training loss. Afterward, Mask-tuning loss-- a weighted aggregation of masked language model training loss and fine-tuning loss-- updates the masked language model and fine-tuning through training iterations. Extensive experiments show that Mask-tuning consistently improves the pre-trained language models' generalization on out-of-distribution datasets and enhances their performance on in-distribution datasets. The source code and pre-trained models will be available on the author's GitHub page.
Bi-Stride Multi-Scale Graph Neural Network for Mesh-Based Physical Simulation
Authors: Yadi Cao; Menglei Chai; Minchen Li; Chenfanfu Jiang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning physical systems on unstructured meshes by flat Graph neural networks (GNNs) faces the challenge of modeling the long-range interactions due to the scaling complexity w.r.t. the number of nodes, limiting the generalization under mesh refinement. On regular grids, the convolutional neural networks (CNNs) with a U-net structure can resolve this challenge by efficient stride, pooling, and upsampling operations. Nonetheless, these tools are much less developed for graph neural networks (GNNs), especially when GNNs are employed for learning large-scale mesh-based physics. The challenges arise from the highly irregular meshes and the lack of effective ways to construct the multi-level structure without losing connectivity. Inspired by the bipartite graph determination algorithm, we introduce Bi-Stride Multi-Scale Graph Neural Network (BSMS-GNN) by proposing \textit{bi-stride} as a simple pooling strategy for building the multi-level GNN. \textit{Bi-stride} pools nodes by striding every other BFS frontier; it 1) works robustly on any challenging mesh in the wild, 2) avoids using a mesh generator at coarser levels, 3) avoids the spatial proximity for building coarser levels, and 4) uses non-parametrized aggregating/returning instead of MLPs during pooling and unpooling. Experiments show that our framework significantly outperforms the state-of-the-art method's computational efficiency in representative physics-based simulation cases.
Toward Effective Deep Reinforcement Learning for 3D Robotic Manipulation: End-to-End Learning from Multimodal Raw Sensory Data
Authors: Samyeul Noh; Hyun Myung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Sample-efficient reinforcement learning (RL) methods capable of learning directly from raw sensory data without the use of human-crafted representations would open up real-world applications in robotics and control. Recent advances in visual RL have shown that learning a latent representation together with existing RL algorithms closes the gap between state-based and image-based training. However, image-based training is still significantly sample-inefficient with respect to learning in 3D continuous control problems (for example, robotic manipulation) compared to state-based training. In this study, we propose an effective model-free off-policy RL method for 3D robotic manipulation that can be trained in an end-to-end manner from multimodal raw sensory data obtained from a vision camera and a robot's joint encoders, without the need for human-crafted representations. Notably, our method is capable of learning a latent multimodal representation and a policy in an efficient, joint, and end-to-end manner from multimodal raw sensory data. Our method, which we dub MERL: Multimodal End-to-end Reinforcement Learning, results in a simple but effective approach capable of significantly outperforming both current state-of-the-art visual RL and state-based RL methods with respect to sample efficiency, learning performance, and training stability in relation to 3D robotic manipulation tasks from DeepMind Control.
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Authors: Zhihai Wang; Xijun Li; Jie Wang; Yufei Kuang; Mingxuan Yuan; Jia Zeng; Yongdong Zhang; Feng Wu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection---which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs---heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected. Although many modern MILP solvers tackle (P1)-(P2) by manually designed heuristics, machine learning offers a promising approach to learn more effective heuristics from MILPs collected from specific applications. However, many existing learning-based methods focus on learning which cuts should be preferred, neglecting the importance of learning the number of cuts that should be selected. Moreover, we observe from extensive empirical results that (P3) what order of selected cuts should be preferred has a significant impact on the efficiency of solving MILPs as well. To address this challenge, we propose a novel hierarchical sequence model (HEM) to learn cut selection policies via reinforcement learning. Specifically, HEM consists of a two-level model: (1) a higher-level model to learn the number of cuts that should be selected, (2) and a lower-level model---that formulates the cut selection task as a sequence to sequence learning problem---to learn policies selecting an ordered subset with the size determined by the higher-level model. To the best of our knowledge, HEM is the first method that can tackle (P1)-(P3) in cut selection simultaneously from a data-driven perspective. Experiments show that HEM significantly improves the efficiency of solving MILPs compared to human-designed and learning-based baselines on both synthetic and large-scale real-world MILPs, including MIPLIB 2017. Moreover, experiments demonstrate that HEM well generalizes to MILPs that are significantly larger than those seen during training.