GenAI4ELab/papercli-papers huggingface dataset:
LPT: Long-tailed Prompt Tuning  for Image Classification
Authors: Bowen Dong; Pan Zhou; Shuicheng YAN; Wangmeng Zuo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained  model for  adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization capability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning (LPT) method for long-tailed classification tasks. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into the target long-tailed domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with fine-grained discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In the first phase, we train the shared prompt via conventional supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In the second phase, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, and then optimize these prompts with a dual sampling strategy and the asymmetric Gaussian Clouded Logit loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training cost and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only $\sim$1.1\% extra trainable parameters, LPT achieves comparable or higher performance than previous whole model fine-tuning methods, and is more robust to domain-shift.
Self-supervised video pretraining yields strong image representations
Authors: Nikhil Parthasarathy; S. M. Ali Eslami; Joao Carreira; Olivier J Henaff
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Videos contain far more information than still images, and hold the potential for learning rich representations of the visual world. Yet, pretraining on image datasets has remained the dominant paradigm for learning representations that capture spatial information and previous attempts at video pretraining have fallen short on image understanding tasks. In this work we revisit self-supervised learning of image representations from the dynamic evolution of video frames. To that end, we propose a dataset curation procedure that addresses the domain mismatch between video and image datasets, and develop a contrastive learning framework which handles the complex transformations present in natural videos. This simple paradigm for distilling knowledge from videos to image representations, called VITO, performs surprisingly well on a variety of image-based transfer learning tasks. For the first time, our video-pretrained model closes the gap with ImageNet pretraining on semantic segmentation on PASCAL and ADE20k and object detection on COCO and LVIS, raising the possibility of video-pretraining becoming the new default for learning image representations.
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Authors: Lingxiao Wang; Qi Cai; Zhuoran Yang; Zhaoran Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon. (ii) The observation and state spaces are often continuous, which induces a sample complexity that scales exponentially with the extrinsic dimension. Addressing such challenges requires learning a minimal but sufficient representation of the observation and state histories by exploiting the structure of the POMDP.

To this end, we propose a reinforcement learning algorithm named Represent to Control (RTC), which learns the representation at two levels while optimizing the policy.~(i) For each step, RTC learns to represent the state with a low-dimensional feature, which factorizes the transition kernel. (ii) Across multiple steps, RTC learns to represent the full history with a low-dimensional embedding, which assembles the per-step feature. We integrate (i) and (ii) in a unified framework that allows a variety of estimators (including maximum likelihood estimators and generative adversarial networks). For a class of POMDPs with a low-rank structure in the transition kernel, RTC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank). Here $\epsilon$ is the optimality gap. To our best knowledge, RTC is the first sample-efficient algorithm that bridges representation learning and policy optimization in POMDPs with infinite observation and state spaces.
Supervision Complexity and its Role in Knowledge Distillation
Authors: Hrayr Harutyunyan; Ankit Singh Rawat; Aditya Krishna Menon; Seungyeon Kim; Sanjiv Kumar
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite the popularity and efficacy of knowledge distillation, there is limited understanding of why it helps. In order to study the generalization behavior of a distilled student, we propose a new theoretical framework that leverages supervision complexity: a measure of alignment between teacher-provided supervision and the student's neural tangent kernel. The framework highlights a delicate interplay among the teacher's accuracy, the student's margin with respect to the teacher predictions, and the complexity of the teacher predictions. Specifically, it provides a rigorous justification for the utility of various techniques that are prevalent in the context of distillation, such as early stopping and temperature scaling. Our analysis further suggests the use of online distillation, where a student receives increasingly more complex supervision from teachers in different stages of their training. We demonstrate efficacy of online distillation and validate the theoretical findings on a range of image classification benchmarks and model architectures.
Hebbian Deep Learning Without Feedback
Authors: Adrien Journé; Hector Garcia Rodriguez; Qinghai Guo; Timoleon Moraitis
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals – which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.
Generated Graph Detection
Authors: Yihan Ma; Zhikun Zhang; Ning Yu; Xinlei He; Michael Backes; Yun Shen; Yang Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph generative models become increasingly effective for data distribution approximation and data augmentation. Although still in sandboxes, they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. It is never too early to regulate the prevalence of generated graphs. As a preventive response, we pioneer to formulate the generated graph detection problem to distinguish generated graphs from real ones. We propose the first framework to systematically investigate a set of sophisticated models and their performance in four classification scenarios. Each scenario switches between seen and unseen datasets/generators during testing to get closer to real world settings and progressively challenge the classifiers. Extensive experiments evidence that all the models are qualified for generated graph detection, with specific models having advantages in specific scenarios. Resulting from the validated generality and oblivion of the classifiers to unseen datasets/generators, we draw a safe conclusion that our solution can sustain for a decent while to curb generated graph misuses.
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning
Authors: Yang Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: By driving optimizers to converge to flat minima, sharpness-aware learning algorithms (such as SAM) have shown the power to achieve state-of-art performances. However, these algorithms will generally incur one extra forward-backward propagation at each training iteration, which largely burdens the computation especially for scalable models. To this end, we propose an efficient training scheme, called Randomized Sharpness-Aware Training (RST). Optimizers in RST would perform a Bernoulli trial at each iteration to choose randomly from base algorithms (SGD) and sharpness-aware algorithms (SAM) with a probability arranged by a predefined scheduling function. Due to the mixture of base algorithms, the overall count of propagation pairs could be largely reduced. Also, we give theoretical analysis on the convergence of RST. Then, we empirically study the computation cost and effect of various types of scheduling functions, and give directions on setting appropriate scheduling functions. Further, we extend the RST to a general framework (G-RST), where we can adjust regularization degree on sharpness freely for any scheduling function. We show that G-RST can outperform SAM in most cases while saving 50\% extra computation cost.
Multiple sequence alignment as a sequence-to-sequence learning problem
Authors: Edo Dotan; Yonatan Belinkov; Oren Avram; Elya Wygoda; Noa Ecker; Michael Alburquerque; Omri Keren; Gil Loewenthal; Tal Pupko
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.
Confounder Identification-free Causal Visual Feature Learning
Authors: Xin Li; Zhizheng Zhang; Guoqiang Wei; Cuiling Lan; Wenjun Zeng; Xin Jin; Zhibo Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.
GReTo: Remedying dynamic graph topology-task discordance via target homophily
Authors: Zhengyang Zhou; Qihe Huang; Gengyu Lin; Kuo Yang; LEI BAI; Yang Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Dynamic graphs are ubiquitous across disciplines where observations usually change over time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as climate early-warning and traffic controlling. Existing homophily Graph Neural Networks (GNNs) adopt physical connections or feature similarity as adjacent matrix to perform node-level aggregations. However, on dynamic graphs with diverse node-wise relations, exploiting a pre-defined fixed topology for message passing inevitably leads to the aggregations of target-deviated neighbors. We designate such phenomenon as the topology-task discordance, which naturally challenges the homophily assumption. In this work, we revisit node-wise relationships and explore novel homophily measurements on dynamic graphs with both signs and distances, capturing multiple node-level spatial relations and temporal evolutions. We discover that advancing homophily aggregations to signed target-oriented message passing can effectively resolve the discordance and promote aggregation capacity. Therefore, a GReTo is proposed, which performs signed message passing in immediate neighborhood, and exploits both local environments and target awareness to realize high-order message propagation. Empirically, our solution achieves significant improvements against best baselines, notably improving 24.79% on KnowAir and 3.60% on Metr-LA.
Deep Variational Implicit Processes
Authors: Luis A. Ortega; Simon Rodriguez Santana; Daniel Hernández-Lobato
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, resulting in flexible models with well-calibrated prediction uncertainty estimates. Methods based on IPs usually carry out function-space approximate inference, which overcomes some of the difficulties of parameter-space approximate inference. Nevertheless, the approximations employed often limit the expressiveness of the final model, resulting, e.g., in a Gaussian predictive distribution, which can be restrictive. We propose here a multi-layer generalization of IPs called the Deep Variational Implicit process (DVIP). This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions. We describe a scalable variational inference algorithm for training DVIP and show that it outperforms previous IP-based methods and also deep GPs. We support these claims via extensive regression and classification experiments. We also evaluate DVIP on large datasets with up to several million data instances to illustrate its good scalability and performance.
Finding and only finding local Nash equilibria by both pretending to be a follower
Authors: Xuchan Bao; Guodong Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Finding (local) Nash equilibria in two-player differentiable games is a classical problem in game theory with important relevance in machine learning. We propose double Follow-the-Ridge (double-FTR), an algorithm that locally converges to and only to local Nash equilibria in general-sum two-player differentiable games. To our knowledge, double-FTR is the first algorithm with such guarantees for general-sum games. Furthermore, we show that by varying its preconditioner, double-FTR leads to a broader family of algorithms with the same convergence guarantee. In addition, double-FTR avoids oscillation near equilibria due to the real-eigenvalues of its Jacobian at fixed points. 
Empirically, we validate the double-FTR algorithm on a range of simple zero-sum and general sum games, as well as simple Generative Adversarial Network (GAN) tasks.
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
Authors: Rajkumar Ramamurthy; Prithviraj Ammanabrolu; Kianté Brantley; Jack Hessel; Rafet Sifa; Christian Bauckhage; Hannaneh Hajishirzi; Yejin Choi
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP?

To help answer this, we first introduce an open-source modular library, $RL4LMs$ (Reinforcement Learning for Language Models), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the $GRUE$ (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference.GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, $NLPO$ (Natural Language Policy Optimization)} that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluations.
Robust Transfer Learning Based on Minimax Principle
Authors: Xinyi Tong; Xiangxiang Xu; Shao-Lun Huang; Lizhong Zheng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The similarity between target and source tasks is a crucial quantity for theoretical analyses and algorithm designs in transfer learning studies. However, this quantity is often difficult to be precisely captured. To address this issue, we make a boundedness assumption on the task similarity and then propose a mathematical framework based on the minimax principle, which minimizes the worst-case expected population risk under this assumption. Furthermore, our proposed minimax problem can be solved analytically, which provides a  guideline for designing robust transfer learning models. According to the analytical expression, we interpret the influences of sample sizes, task distances, and the model dimensionality in knowledge transferring. Then, practical algorithms are developed based on the theoretical results. Finally, experiments conducted on image classification tasks show that our approaches can achieve robust and competitive accuracies under random selections of training sets.
Diversity Boosted Learning for Domain Generalization with a Large Number of Domains
Authors: XI LENG; Xiaoying Tang; Yatao Bian
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning algorithms minimizing the average training loss typically suffer from poor generalization performance. It inspires various works for domain generalization (DG), among which a series of methods work by $O(n^2)$ pairwise domain operations with n domains, where each one is often costly. Moreover, while a common objective in the DG literature is to learn invariant representations against spurious correlations induced by domains, we point out the insufficiency of it and highlight the importance of alleviating spurious correlations caused by objects. Based on the observation that diversity helps mitigate spurious correlations, we propose a Diversity boosted twO-level saMplIng framework (DOMI) to efficiently sample the most informative ones among a large number of domains and data points. We show that DOMI helps train robust models against spurious correlations from both domain-side and object-side, substantially enhancing the performance of five backbone DG algorithms on Rotated MNIST and Rotated Fashion MNIST.
Causality Compensated Attention for Contextual Biased Visual Recognition
Authors: Ruyang Liu; Jingjia Huang; Thomas H. Li; Ge Li
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC.