GenAI4ELab/papercli-papers huggingface dataset:
Behavior Proximal Policy Optimization 
Authors: Zifeng Zhuang; Kun LEI; Jinxin Liu; Donglin Wang; Yilang Guo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to overestimating of out-of-distribution state-action pairs. Thus, various additional augmentations are proposed to keep the learned policy close to the offline dataset (or the behavior policy). In this work, starting from the analysis of offline monotonic policy improvement, we reach a surprising conclusion that online on-policy algorithms are naturally able to solve offline RL. Specifically, the inherent conservatism of these on-policy algorithms is exactly what the offline RL method needs to overcome the overestimation. Based on this, we propose Behavior Proximal Policy Optimization (BPPO), which solves offline RL without any extra constraint or regularization introduced compared to PPO. Extensive experiments on the D4RL benchmark empirically show this extremely succinct method outperforms state-of-the-art offline RL algorithms. Our implementation is available at https://github.com/Dragon-Zhuang/BPPO.
How Much Data Are Augmentations Worth?  An Investigation into Scaling Laws, Invariance, and Implicit Regularization
Authors: Jonas Geiping; Micah Goldblum; Gowthami Somepalli; Ravid Shwartz-Ziv; Tom Goldstein; Andrew Gordon Wilson
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite the clear performance benefits of data augmentations, little is known about why they are so effective. In this paper, we disentangle several key mechanisms through which data augmentations operate. Establishing an exchange rate between augmented and additional real data, we find that in out-of-distribution testing scenarios, augmentations which yield samples that are diverse, but inconsistent with the data distribution can be even more valuable than additional training data. Moreover, we find that data augmentations which encourage invariances can be more valuable than invariance alone, especially on small and medium sized training sets. Following this observation, we show that augmentations induce additional stochasticity during training, effectively flattening the loss landscape.
Test-Time Robust Personalization for Federated Learning
Authors: Liangze Jiang; Tao Lin
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalization on FL models additionally adapts the global model to different clients, achieving promising results on consistent local training & test distributions. However, for real-world personalized FL applications, it is crucial to go one step further: robustifying FL models under the evolving local test set during deployment, where various types of distribution shifts can arise. In this work, we identify the pitfalls of existing works under test-time distribution shifts and propose Federated Test-time Head Ensemble plus tuning (FedTHE+), which personalizes FL models with robustness to various test-time distribution shifts. We illustrate the advancement of FedTHE+ (and its degraded computationally efficient variant FedTHE) over strong competitors, for training various neural architectures (CNN, ResNet, and Transformer) on CIFAR10 and ImageNet and evaluating on diverse test distributions. Along with this, we build a benchmark for assessing the performance and robustness of personalized FL methods during deployment. Code: \url{https://github.com/LINs-lab/FedTHE}.
Unified neural representation model for physical and conceptual spaces
Authors: Tatsuya Haga; Yohei Oseki; Tomoki Fukai
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The spatial processing system of the brain uses grid-like neural representations (grid cells) for supporting vector-based navigation. Experiments also suggest that neural representations for concepts (concept cells) exist in the human brain, and conceptual inference relies on navigation in conceptual spaces. We propose a unified model called ``disentangled successor information (DSI)'' that explains neural representations for both physical and conceptual spaces. DSI generates grid-like representations in a 2-dimensional space that highly resemble those observed in the brain. Moreover, the same model creates concept-specific representations from linguistic inputs, corresponding to concept cells. Mathematically, DSI vectors approximate value functions for navigation and word vectors obtained by word embedding methods, thus enabling both spatial navigation and conceptual inference based on vector-based calculation. Our results suggest that a single principle can explain computation of physical and conceptual spaces in the human brain.
Approximate Vanishing Ideal Computations at Scale
Authors: Elias Samuel Wirth; Hiroshi Kera; Sebastian Pokutta
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The vanishing ideal of a set of points $X = \{\mathbf{x}_1, \ldots, \mathbf{x}_m\}\subseteq \mathbb{R}^n$ is the set of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and admits an efficient representation by a finite subset of generators. In practice, to accommodate noise in the data, algorithms that construct generators of the approximate vanishing ideal are widely studied but their computational complexities remain expensive. In this paper, we scale up the oracle approximate vanishing ideal algorithm (OAVI), the only generator-constructing algorithm with known learning guarantees. We prove that the computational complexity of OAVI is not superlinear, as previously claimed, but linear in the number of samples $m$. In addition, we propose two modifications that accelerate OAVI's training time: Our analysis reveals that replacing the pairwise conditional gradients algorithm, one of the solvers used in OAVI, with the faster blended pairwise conditional gradients algorithm leads to an exponential speed-up in the number of features $n$. Finally, using a new inverse Hessian boosting approach, intermediate convex optimization problems can be solved almost instantly, improving OAVI's training time by multiple orders of magnitude in a variety of numerical experiments.
Invariant Aggregator for Defending against Federated Backdoor Attacks
Authors: Xiaoyang Wang; Dimitrios Dimitriadis; Oluwasanmi O Koyejo; Shruti Tople
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning is gaining popularity as it enables training of high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vulnerable to various adversarial attacks in the presence of malicious clients. Specifically, an adversary can perform backdoor attacks to control model predictions via poisoning the training dataset with a trigger. In this work, we propose a mitigation for backdoor attacks in a federated learning setup. Our solution forces the model optimization trajectory to focus on the invariant directions that are generally useful for utility and avoid selecting directions that favor few and possibly malicious clients. Concretely, we consider the sign consistency of the pseudo-gradient (the client update) as an estimation of the invariance. Following this, our approach performs dimension-wise filtering to remove pseudo-gradient elements with low sign consistency. Then, a robust mean estimator eliminates outliers among the remaining dimensions. Our theoretical analysis further shows the necessity of the defense combination and illustrates how our proposed solution defends the federated learning model. Empirical results on three datasets with different modalities and varying number of clients show that our approach mitigates backdoor attacks with a negligible cost on the model utility.
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
Authors: Andrea Gesmundo; Jeff Dean
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of tasks. Also, continual learning, that adds the temporal aspect to multitask, is often focused to the study of common pitfalls such as catastrophic forgetting instead of being studied at a large scale as a critical component to build the next generation artificial intelligence.
We propose an evolutionary method capable of generating large scale multitask models that support the dynamic addition of new tasks. The generated multitask models are sparsely activated and integrates a task-based routing that guarantees bounded compute cost and fewer added parameters per task as the model expands. The proposed method relies on a knowledge compartmentalization technique to achieve immunity against catastrophic forgetting and other common pitfalls such as gradient interference and negative transfer. We demonstrate empirically that the  proposed  method  can  jointly  solve  and  achieve  competitive  results  on  69 public image classification tasks, for example improving the state of the art on a competitive benchmark such as cifar10 by achieving a 15% relative error reduction compared to the best model trained on public data.
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Authors: Kedar Karhadkar; Pradeep Kr. Banerjee; Guido Montufar
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the curvature and spectral gap of the graph. On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing. We propose a computationally efficient algorithm that prevents oversquashing by systematically adding edges to the graph based on spectral expansion. We combine this with a relational architecture, which lets the GNN preserve the original graph structure and provably prevents oversmoothing. We find experimentally that our algorithm outperforms existing graph rewiring methods in several graph classification tasks.
Unicom: Universal and Compact Representation Learning for Image Retrieval
Authors: Xiang An; Jiankang Deng; Kaicheng Yang; Jaiwei Li; Ziyong Feng; Jia Guo; Jing Yang; Tongliang Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors.
However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trained feature representation is therefore not universal enough to generalize well to the diverse open-world classes. 
In this paper, we first cluster the large-scale \laion{} into one million pseudo classes based on the joint textual and visual features extracted by the CLIP model. Due to the confusion of label granularity, the automatically clustered dataset inevitably contains heavy inter-class conflict. To alleviate such conflict, we randomly select partial inter-class prototypes to construct the margin-based softmax loss. To further enhance the low-dimensional feature representation, we randomly select partial feature dimensions when calculating the similarities between embeddings and class-wise prototypes. The dual random partial selections are with respect to the class dimension and the feature dimension of the prototype matrix, making the classification conflict-robust and the feature embedding compact. Our method significantly outperforms state-of-the-art unsupervised and supervised image retrieval approaches on multiple benchmarks. The code and pre-trained models are released to facilitate future research \url{https://github.com/deepglint/unicom}.
No Reason for No Supervision: Improved Generalization in Supervised Models
Authors: Mert Bülent Sarıyıldız; Yannis Kalantidis; Karteek Alahari; Diane Larlus
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model’s generalization and maintaining its performance on the original task. Models trained with self-supervised learning tend to generalize better than their supervised counterparts for transfer learning; yet, they still lag behind supervised models on IN1K. In this paper, we propose a supervised learning setup that leverages the best of both worlds. We extensively analyze supervised training using multi-scale crops for data augmentation and an expendable projector head, and reveal that the design of the projector allows us to control the trade-off between performance on the training task and transferability. We further replace the last layer of class weights with class prototypes computed on the fly using a memory bank and derive two models: t-ReX that achieves a new state of the art for transfer learning and outperforms top methods such as DINO and PAWS on IN1K, and t-ReX* that matches the highly optimized RSB-A1 model on IN1K while performing better on transfer tasks.
Code and pretrained models: https://europe.naverlabs.com/t-rex
Towards scalable and non-IID robust Hierarchical Federated Learning via Label-driven Knowledge Aggregator
Authors: Duong Minh Nguyen; Viet Quoc Pham; Hoang Thai Dinh; Diep Nguyen; Long Tran-Thanh; Won-Joo Hwang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks, and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
Authors: Haoxuan Li; Chunyuan Zheng; Peng Wu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities. Moreover, the fact that DR relies more on extrapolation will lead to suboptimal performance. To address the above limitations while retaining double robustness, we propose a stabilized doubly robust (StableDR) learning approach with a weaker reliance on extrapolation. Theoretical analysis shows that StableDR has bounded bias, variance, and generalization error bound simultaneously under inaccurate imputed errors and arbitrarily small propensities. In addition, we propose a novel learning approach for StableDR that updates the imputation, propensity, and prediction models cyclically, achieving more stable and accurate predictions. Extensive experiments show that our approaches significantly outperform the existing methods.
Gradient Boosting Performs Gaussian Process Inference
Authors: Aleksei Ustimenko; Artem Beliakov; Liudmila Prokhorenkova
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection.
Variational Latent Branching Model for Off-Policy Evaluation
Authors: Qitong Gao; Ge Gao; Min Chi; Miroslav Pajic
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled. Specifically, VLBM leverages and extends the variational inference framework with the recurrent state alignment (RSA), which is designed to capture as much information underlying the limited training data, by smoothing out the information flow between the variational (encoding) and generative (decoding) part of VLBM. Moreover, we also introduce the branching architecture to improve the model’s robustness against randomly initialized model weights. The effectiveness of the VLBM is evaluated on the deep OPE (DOPE) benchmark, from which the training trajectories are designed to result in varied coverage of the state-action space. We show that the VLBM outperforms existing state-of-the-art OPE methods in general.
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
Authors: Xin-Qiang Cai; Yao-Xiang Ding; Zixuan Chen; Yuan Jiang; Masashi Sugiyama; Zhi-Hua Zhou
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existing imitation learning approaches, since most of them learn policies under homogeneous observation spaces. On the other hand, previous studies under different observation spaces have strong assumptions that these two observation spaces coexist during the entire learning process. However, in reality, the observation coexistence will be limited due to the high cost of acquiring expert observations. In this work, we study this challenging problem with limited observation coexistence under heterogeneous observations: Heterogeneously Observable Imitation Learning (HOIL). We identify two underlying issues in HOIL: the dynamics mismatch and the support mismatch, and further propose the Importance Weighting with REjection (IWRE) algorithm based on importance weighting and learning with rejection to solve HOIL problems. Experimental results show that IWRE can solve various HOIL tasks, including the challenging tasks of transforming the vision-based demonstrations to random access memory (RAM)-based policies in the Atari domain, even with limited visual observations.
Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model
Authors: Jeremiah Blocki; Seunghoon Lee; Tamalika Mukherjee; Samson Zhou
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: The data management of large companies often prioritize more recent data, as a source of higher accuracy prediction than outdated data. For example, the Facebook data policy retains user search histories for $6$ months while the Google data retention policy states that browser information may be stored for up to $9$ months. These policies are captured by the sliding window model, in which only the most recent $W$ statistics form the underlying dataset. In this paper, we consider the problem of privately releasing the $L_2$-heavy hitters in the sliding window model, which include $L_p$-heavy hitters for $p\le 2$ and in some sense are the strongest possible guarantees that can be achieved using polylogarithmic space, but cannot be handled by existing techniques due to the sub-additivity of the $L_2$ norm. Moreover, existing non-private sliding window algorithms use the smooth histogram framework, which has high sensitivity. To overcome these barriers, we introduce the first differentially private algorithm for $L_2$-heavy hitters in the sliding window model by initiating a number of $L_2$-heavy hitter algorithms across the stream with significantly lower threshold. Similarly, we augment the algorithms with an approximate frequency tracking algorithm with significantly higher accuracy. We then use smooth sensitivity and statistical distance arguments to show that we can add noise proportional to an estimation of the $L_2$ norm. To the best of our knowledge, our techniques are the first to privately release statistics that are related to a sub-additive function in the sliding window model, and may be of independent interest to future differentially private algorithmic design in the sliding window model.
On a Built-in Conflict between Deep Learning and Systematic Generalization
Authors: Yuanpeng Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Out-of-distribution or systematic generalization is a desirable property that most deep learning algorithms lack. In this paper, we hypothesize that internal function sharing is one of the reasons to weaken systematic generalization in deep learning for classification tasks. Under equivalent prediction, a model partitions an input space into multiple parts separated by boundaries. The function sharing prefers to reuse boundaries, leading to fewer parts for new outputs, which conflicts with systematic generalization. We show such phenomena in standard deep learning models, such as fully connected, convolutional, residual networks, LSTMs, and (Vision) Transformers. We hope this study provides novel insights and forms a basis for new research directions to improve systematic generalization.