pwc-archive/papers-with-abstracts huggingface dataset:
Provably Calibrated Regression Under Distribution Drift
Authors: Shengjia Zhao, Yusuke Tashiro, Danny Tse, Stefano Ermon
Date: 2021-09-29 00:00:00
Tasks: Prediction, regression, Time Series, Time Series Analysis, Time Series Prediction, Uncertainty Quantification

Accurate uncertainty quantification is a key building block of trustworthy machine learning systems. Uncertainty is typically represented by probability distributions over the possible outcomes, and these probabilities should be calibrated, \textit{e.g}. the 90\% credible interval should contain the true outcome 90\% of the times. In the online prediction setup, existing conformal methods can provably achieve calibration assuming no distribution shift; however, the assumption is difficult to verify, and unlikely to hold in many applications such as time series prediction. Inspired by control theory, we propose a prediction algorithm that guarantees calibration even under distribution shift, and achieves strong performance on metrics such as sharpness and proper scores. We compare our method with baselines on 19 time-series and regression datasets, and our method achieves approximately 2x reduction in calibration error, comparable sharpness, and improved downstream decision utility.
Curriculum Discovery through an Encompassing Curriculum Learning Framework
Authors: Mohamed Elgaar, Hadi Amiri
Date: 2021-09-29 00:00:00
Tasks: Natural Language Inference, text-classification, Text Classification

We describe a curriculum learning framework capable of discovering optimal curricula in addition to performing standard curriculum learning. We show that this framework encompasses existing curriculum learning approaches such as difficulty-based data sub-sampling, data pruning, and loss re-weighting. We employ the proposed framework to address the following key questions in curriculum learning research: (a) What is the best curriculum to train a given model on a given dataset? (b) What are the characteristics of optimal curricula for different datasets and different difficulty scoring functions? We show that our framework outperforms competing state-of-the-art curriculum learning approaches in natural language inference and other text classification tasks. In addition, exhaustive experiments illustrate the generalizability of the discovered curricula across the three datasets and two difficulty scoring functions.
Rethinking Rehearsal in Lifelong Learning: Does An Example Contribute the Plasticity or Stability?
Authors: Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan
Date: 2021-09-29 00:00:00
Tasks: Lifelong learning, Multi-Task Learning

Lifelong Learning (LL) is the sequential transformation of Multi-Task Learning, which learns new tasks in order like human-beings.
Traditionally, the primary goal of LL is to achieve the trade-off between the Stability (remembering past tasks) and Plasticity (adapting to new tasks). Rehearsal, seeking to remind the model by storing examples from old tasks in LL, is one of the most effective ways to get such trade-off. However, the Stability and Plasticity (SP) are only evaluated when a model is trained well, and it is still unknown what leads to the final SP in rehearsal-based LL. In this paper, we study the cause of SP from the perspective of example difference. First, we theoretically analyze the example-level SP via the influence function and deduce the influence of each example on the final SP. Moreover, to avoid the calculation burden of Hessian for each example, we propose a simple yet effective MetaSP algorithm to simulate the acquisition of example-level SP. Last but not least, we find that by adjusting the weights of each training example, a solution on the SP Pareto front can be obtained, resulting in a better SP trade-off for LL. Empirical results show that our algorithm significantly outperforms state-of-the-art methods on benchmark LL datasets.
Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints
Authors: Rajshekhar Das, Jonathan Francis, Sanket Vaibhav Mehta, Jean Oh, Emma Strubell, Jose Moura
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Object, Pseudo Label, Segmentation, Semantic Segmentation, Superpixels, Unsupervised Domain Adaptation

Pseudo label self-training has emerged as a dominant approach to unsupervised domain adaptation (UDA) for semantic segmentation. Despite recent advances, this approach is susceptible to erroneous pseudo labels arising from  confirmation bias that ultimately leads to sub-optimal segmentation. To mitigate the effect of noisy pseudo-labels, we propose regularising conventional self-training objectives with constraints that are derived from structure-preserving modalities, such as depth. Towards this end, we introduce a contrastive image-level objectness constraint that pulls the pixel representations of the same object instance closer while pushing those from different object categories apart. To identify pixels within an object, we subscribe to a notion of objectness derived from depth maps, that are robust to photometric  variations, as well as superpixels, that are obtained via unsupervised clustering over the raw image space. Crucially, the objectness constraint is agnostic to the ground-truth semantic segmentation labels and, therefore, remains appropriate for unsupervised adaptation settings. In this paper, we show that our approach of leveraging multi-modal constraint improves top performing self-training methods in various UDA benchmarks for semantic segmentation. We make our code and data-splits available in the supplementary material.
Optimizing Few-Step Diffusion Samplers by Gradient Descent
Authors: Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Denoising, Image Generation, Unconditional Image Generation

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a flexible family of generative models rivaling GANs and autoregressive models in sample quality and likelihoods. DDPMs however typically require hundreds of inference steps to generate a high-fidelity image, despite recent progress on speeding up diffusion model sampling. We introduce Differentiable Diffusion Sampler Search (DDSS): a method that learns few-step samplers for any pre-trained DDPM by using gradient descent. We propose Generalized Gaussian Diffusion Processes (GGDP), a family of non-Markovian samplers for diffusion models, and we show how to improve the generated samples of pre-trained DDPMs by optimizing the degrees of freedom of the GGDP sampler family with respect to a perceptual loss. Our optimization procedure backpropagates through the sampling process using the reparameterization trick. Searching our novel GGDP family with DDSS, we achieve strong results on unconditional image generation on both CIFAR and ImageNet 64x64 (e.g., 7.59 on CIFAR10 with only 10 inference steps, and 4.67 with 25 steps, compared to 13.62 and 6.56 with the strongest respective DDIM($\eta=0$) baselines). Our method is compatible with any pre-trained DDPM without re-training, only needs to be applied once, and does not finetune the parameters of the pre-trained DDPM.
Contrastive Quant: Quantization Makes Stronger Contrastive Learning
Authors: Yonggan Fu, Qixuan Yu, Meng Li, Xu Ouyang, Vikas Chandra, Yingyan Lin
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Quantization

Contrastive learning, which learns visual representations by enforcing feature consistency under different augmented views, has emerged as one of the most effective unsupervised learning methods. In this work, we explore contrastive learning from a new perspective, inspired by the recent works showing that properly designed weight perturbations or quantization help the models learn a smoother loss landscape. Interestingly, we find that quantization, when properly engineered, can enhance the effectiveness of contrastive learning. To this end, we propose a novel contrastive learning framework, dubbed Contrastive Quant, to encourage the feature consistency under both (1) differently augmented inputs via various data transformations and (2) differently augmented weights/activations via various quantization levels, where the feature consistency under injected noises via quantization can be viewed as augmentations on both model weights and intermediate activations as a complement to the input augmentations. Extensive experiments, built on top of two state-of-the-art contrastive learning methods SimCLR and BYOL, show that Contrastive Quant consistently improves the learned visual representation, especially with limited labeled data under semi-supervised scenarios. For example, our Contrastive Quant achieves a 8.69% and 10.27% higher accuracy on ResNet-18 and ResNet-34, respectively, on ImageNet when fine-tuning with 10% labeled data. We believe this work has opened up a new perspective for future contrastive learning innovations. All codes will be released upon acceptance.
Offline Pre-trained Multi-Agent Decision Transformer
Authors: Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xi yun Li, Haifeng Zhang, Ying Wen, Weinan Zhang, Jun Wang, Bo Xu
Date: 2021-09-29 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Offline reinforcement learning leverages static datasets to learn optimal policies with no necessity to access the environment. This is desirable for multi-agent systems due to the expensiveness of agents' online interactions and the demand for sample numbers. Yet,  in multi-agent reinforcement learning (MARL), the paradigm of offline pre-training with online fine-tuning has never been reported, nor datasets or benchmarks for offline MARL research are available. In this paper, we intend to investigate whether offline training is able to learn policy representations that elevate performance on downstream MARL tasks. We introduce the first offline dataset based on StarCraftII with diverse quality levels and propose a multi-agent decision transformer (MADT) for effective offline learning. MADT integrates the powerful temporal representation learning ability of Transformer into both offline and online multi-agent learning, which promotes generalisation across agents and scenarios. The proposed method demonstrates superior performance than the state-of-the-art algorithms in offline MARL. Furthermore, when applied to online tasks, the pre-trained MADT largely improves sample efficiency, even in zero-shot task transfer. To our best knowledge, this is the first work to demonstrate the effectiveness of pre-trained models in terms of sample efficiency and generalisability enhancement in MARL.
k-Median Clustering via Metric Embedding: Towards Better Initialization with Privacy
Authors: Chenglin Fan, Ping Li, Xiaoyun Li
Date: 2021-09-29 00:00:00
Tasks: Clustering

In clustering algorithms, the choice of initial centers is crucial for the quality of the learned clusters. We propose a new initialization scheme for the $k$-median problem in the general metric space (e.g., discrete space induced by graphs), based on the construction of metric embedding tree structure of the data. From the tree, we can extract good initial centers that can be used subsequently for the local search algorithm. Our method, named the HST initialization, can also be easily extended to the setting of differential privacy (DP) to generate private initial centers. Theoretically, the initial centers from HST initialization can achieve lower error than those from another popular initialization method, $k$-median++, in the non-DP setting. Moreover, with privacy constraint, we show that the error of applying DP local search followed by our private HST initialization improves previous results, and approaches the known lower bound within a small factor. Empirically, experiments are conducted to demonstrate the effectiveness of our methods.
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles
Authors: Ruqi Bai, David I. Inouye, Saurabh Bagchi
Date: 2021-09-29 00:00:00
Tasks: Adversarial Robustness

While ensemble methods have been widely used for robustness against random perturbations (\ie the average case), ensemble approaches for robustness against adversarial perturbations (\ie the worst case) have remained elusive despite multiple prior attempts. We show that ensemble methods can improve adversarial robustness to multiple attacks if the ensemble is \emph{adversarially diverse}, which is defined by two properties: 1) the sub-models are adversarially robust themselves and yet 2) adversarial attacks do not transfer easily between sub-models. While at first glance, creating such an ensemble would seem computationally expensive, we demonstrate that an adversarially diverse ensemble can be trained with minimal computational overhead via a Multiple-Input Multiple-Output (MIMO) model. Specifically, we propose to train a MIMO model with adversarial training ({\emph{MAT}}), where each sub-model can be trained on a different attack type. When computing gradients for generating adversarial examples during training, we use the gradient with respect to the ensemble objective. This has a two-fold benefit: 1) it only requires 1 backward pass and 2) the cross-gradient information between the models promotes robustness against transferable attacks. We empirically demonstrate that {\emph{MAT}} produces an ensemble of models that is adversarially diverse and significantly improves performance over single models or vanilla ensembles while being comparable to previous state-of-the-art methods. On MNIST, we obtain $99.5\%$ clean accuracy and ($88.6\%, 57.1\%,71.6\%$) against $(\ell_\infty, \ell_2, \ell_1)$ attacks, and on CIFAR10, we achieve $79.7\%$ clean accuracy and ($47.9\%, 61.8\%,47.6\%$) against $(\ell_\infty, \ell_2, \ell_1)$ attacks, which are comparable to previous state-of-the-art methods.
Meta Learning with Minimax Regularization
Authors: Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Wenpeng Zhang, Heng Chang, Wenwu Zhu
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Even though meta-learning has attracted research wide attention in recent years, the generalization problem of meta-learning is still not well addressed. Existing works focus on meta-generalization to unseen tasks at the meta-level, while ignoring that adapted-models may not be generalized to the tasks domain at the adaptation-level, which can not be solved trivially. To this end, we propose a new regularization mechanism for meta-learning -- Minimax-Meta Regularization. Especially, we maximize the regularizer in the inner-loop to encourage the adapted-model to be more sensitive to the new task, and minimize the regularizer in the outer-loop to resist overfitting of the meta-model. This adversarial regularization forces the meta-algorithm to maintain generality at the meta-level while it is easy to learn specific assumptions at the task-specific level, thereby improving the generalization of meta-learning. We conduct extensive experiments on the representative meta-learning scenarios to verify our proposed method, including few-shot learning and robust reweighting. The results show that our method consistently improves the performance of the meta-learning algorithms and demonstrates the effectiveness of Minimax-Meta Regularization.
Learning Temporally Latent Causal Processes from General Temporal Data
Authors: Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Causal Discovery, Disentanglement, Representation Learning, Video Understanding

Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables. Estimating latent causal variable graphs from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametetric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified. We propose LEAP, a theoretically-grounded architecture that extends Variational Autoencoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. We evaluate LEAP on a number of datasets including video and motion capture data. Experiments demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structure and our approach considerably outperforms those existing methods without leveraging history or nonstationarity information. This is one of the first works that successfully recover time-delayed latent processes from nonlinear mixtures without using sparsity or minimality assumptions.
Improving and Assessing Anomaly Detectors for Large-Scale Settings
Authors: Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt
Date: 2021-09-29 00:00:00
Tasks: Anomaly Segmentation, Out-of-Distribution Detection, Segmentation, Self-Driving Cars

Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, existing research mainly focuses on simple small-scale settings. To set the stage for more realistic out-of-distribution detection, we depart from small-scale settings and explore large-scale multiclass and multi-label settings with high-resolution images and thousands of classes. To make future work in real-world settings possible, we create new benchmarks for three large-scale settings. To test ImageNet multiclass anomaly detectors, we introduce a new dataset of anomalous species. We leverage ImageNet-22K to evaluate PASCAL VOC and COCO multilabel anomaly detectors. Third, we introduce a new benchmark for anomaly segmentation by introducing a segmentation benchmark with road anomalies. We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
Density Estimation for Conservative Q-Learning
Authors: Paul Daoudi, Merwan Barlier, Ludovic Dos Santos, Aladin Virmaux
Date: 2021-09-29 00:00:00
Tasks: Density Estimation, Q-Learning

Batch Reinforcement Learning algorithms aim at learning the best policy from a batch of data without interacting with the environment. Within this setting, one difficulty is to correctly assess the value of state-action pairs that are far from the dataset. Indeed, the lack of information may provoke an overestimation of the value function, leading to non-desirable behaviors. A compromise between enhancing the behaviour policy's performance and staying close to it must be found. To alleviate this issue, most existing approaches introduce a regularization term to favor state-action pairs from the dataset. In this paper, we refine this idea by estimating the density of these state-action pairs to distinguish neighbourhoods. The resulting regularization guides the policy toward meaningful unseen regions, improving the learning process. We hence introduce Density Conservative Q-Learning (D-CQL), a batch-RL algorithm with strong theoretical guarantees that carefully penalizes the value function based on the amount of information collected in the state-action space. The performance of our approach is outlined on many classical benchmark in batch-RL.
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning
Authors: Jun Luo, Shandong Wu
Date: 2021-09-29 00:00:00
Tasks: Federated Learning

The goal of conventional federated learning (FL) is to train a global model for a federation of clients with decentralized data, reducing the systemic privacy risk of centralized training. The distribution shift across non-IID datasets, also known as the data heterogeneity, often poses a challenge for this one-global-model-fits-all solution. In this work, we propose APPLE, a personalized cross-silo FL framework that adaptively learns how much each client can benefit from other clients’ models. We also introduce a method to flexibly control the focus of training APPLE between global and local objectives. We empirically evaluate our method's convergence and generalization behavior and performed extensive experiments on two benchmark datasets and two medical imaging datasets under two non-IID settings. The results show that the proposed personalized FL framework, APPLE, achieves state-of-the-art performance compared to several other personalized FL approaches in the literature.
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
Authors: Prashant Khanduri, Haibo Yang, Mingyi Hong, Jia Liu, Hoi To Wai, Sijia Liu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Benchmarking

This work develops a novel framework for communication-efficient distributed learning where the models to be learned are overparameterized. We focus on a class of kernel learning problems (which includes the popular neural tangent kernel (NTK) learning as a special case) and propose a novel {\it multi-agent kernel approximation} technique that allows the agents to distributedly estimate the full kernel function, and subsequently perform decentralized optimization, without directly exchanging any local data or parameters. The proposed framework is a significant departure from the classical consensus-based approaches, because the agents do not exchange problem parameters, and no consensus is required. We analyze the optimization and the generalization performance of the proposed framework for the $\ell_2$ loss. We show that with $M$ agents and $N$ total samples when certain generalized inner-product kernels (resp. the random features kernel) are used, each agent needs to communicate $\mathcal{O}\big({N^2}/{M}\big)$ bits (resp. $\mathcal{O}\big(N \sqrt{N}/M \big)$ real values) to achieve minimax optimal generalization performance. We validate the theoretical results on 90 UCI benchmarking datasets (with average data size $N \approx 1000$) and show that each agent needs to share a total of $200N/M$ bits (resp. $3N/M$ real values) to closely match the performance of the centralized algorithms, and these numbers are independent of parameter and feature dimensions.