United States

We study the price of anarchy (PoA) of simultaneous 2nd price auctions (S2PA) under a new natural condition of no underbidding, meaning that agents never bid on items less than their marginal values.
We establish improved (mostly tight) bounds on the PoA of S2PA under no underbidding for different valuation classes (including unit demand, submodular, XOS, subadditive, and general monotone valuations), in both full information and incomplete information settings. 

To derive our results, we introduce a new parameterized property of auctions, termed (gamma,delta) revenue guaranteed, which implies a PoA of at least gamma/(1+delta). 
Via extension theorems, this guarantee extends to coarse correlated equilibria (CCE) in full information settings, and to Bayesian PoA (BPoA) in settings with incomplete information and arbitrary (correlated) distributions.
We then show that S2PA are (1,1) revenue guaranteed with respect to bids satisfying no underbidding. 
This implies a PoA of at least 1/2 for general monotone valuation, which extends to BPOA with arbitrary correlated distributions.
Moreover, we show that (lambda,mu) smoothness combined with (gamma,delta) revenue guaranteed guarantees a PoA of at least (gamma+lambda)/(1+delta+mu).
This implies a host of results, such as a tight PoA of 2/3 for S2PA with submodular (or XOS) valuations, under no overbidding and no underbidding.
Beyond establishing improved bounds for S2PA, the no underbidding assumption sheds new light on the performance of S2PA relative to 
simultaneous 1st price auctions.

AAAI 2021

Simultaneous 2nd Price Item Auctions with No-Underbidding

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

technical paper

Weakly-supervised semantic segmentation (WSSS) using image-level labels has recently attracted much attention for reducing annotation costs.
Existing WSSS methods utilize localization maps from the classification network to generate pseudo segmentation labels.
However, since localization maps obtained from the classifier focus only on sparse discriminative object regions, it is difficult to generate high-quality segmentation labels.
To address this issue, we introduce discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions.
DRS suppresses the attention on discriminative regions and spreads it to adjacent non-discriminative regions, generating dense localization maps.
DRS requires few or no additional parameters and can be plugged into any network.
Furthermore, we introduce an additional learning strategy to give a self-enhancement of localization maps, named localization map refinement learning.
Benefiting from this refinement learning, localization maps are refined and enhanced by recovering some missing parts or removing noise itself.
Due to its simplicity and effectiveness, our approach achieves mIoU 71.4% on the PASCAL VOC 2012 segmentation benchmark using only image-level labels.
Extensive experiments demonstrate the effectiveness of our approach.

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-curriculum learning for domain adaptation in NMT. During meta-training, the NMT first learns the similar curricula from each domain to avoid falling into a bad local optimum early, and finally learns the curricula of individualities to improve the model robustness for learning domain-specific knowledge. Experimental results on 10 different low-resource domains show that meta-curriculum learning can improve the translation performance of both familiar and unfamiliar domains. All the codes and data are freely available at https://github.com/NLP2CT/Meta-Curriculum.

Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation

Ternary neural networks (TNNs) are potential for network acceleration by reducing the full-precision weights in network to ternary ones, e.g., {-1,0,1}.
However, existing TNNs are mostly calculated based on rule-of-thumb quantization methods by simply thresholding operations, which  causes a significant accuracy loss. In this paper, we introduce a stem-residual framework which provides new  insight into Ternary quantization, termed Residual Quantization (TRQ), to achieve more powerful TNNs. Rather than directly thresholding operations, TRQ recursively performs quantization on full-precision weights for a  refined reconstruction by combining the binarized stem and residual parts. With such a unique quantization process, TRQ  endows the quantizer with high flexibility and precision. Our TRQ is generic, which can be easily extended to multiple bits through recursively encoded residual for  a better recognition accuracy. Extensive experimental results demonstrate that the proposed method yields great recognition accuracy while being accelerated.

TRQ: Ternary Neural Networks with Residual Quantization

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. As previous text GANs suffer from high variance of gradients, we apply contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.

TextGAIL: Generative Adversarial Imitation Learning for Text Generation

Low-shot (one/few-shot) segmentation has attracted increasing attention as it works well with limited annotation. State-of-the-art low-shot segmentation methods on natural images usually focus on implicit representation learning for each novel class, such as learning prototypes, deriving guidance features via masked average pooling, and segmenting using cosine similarity in feature space. We argue that low-shot segmentation on medical images should step further to explicitly learn dense correspondences between images to utilize the anatomical similarity. The core ideas are inspired by the classical practice of multi-atlas segmentation, where the indispensable parts of atlas-based segmentation, i.e., registration, label propagation, and label fusion are unified into a single framework in our work. Specifically, we propose two alternative baselines, i.e., the Siamese-Baseline and Individual-Difference-Aware Baseline, where the former is targeted at anatomically stable structures (such as brain tissues), and the latter possesses a strong generalization ability to organs suffering large morphological variations (such as abdominal organs). In summary, this work sets up a benchmark for low-shot 3D medical image segmentation and sheds light on further understanding of atlas-based few-shot segmentation.

Alternative Baselines for Low-Shot 3D Medical Image Segmentation---An Atlas Perspective

Generally, the existing graph-based multi-view clustering models consists of two steps: (1) graph construction; (2) eigen-decomposition on the graph Laplacian matrix to compute a continuous cluster assignment matrix, followed by a post-processing algorithm to get the discrete one. However, both the graph construction and eigen-decomposition are time-consuming, and the two-stage process may deviate from directly solving the primal problem. To this end, we propose Fast Multi-view Discrete Clustering (FMDC) with anchor graphs, focusing on directly solving the spectral clustering problem with a small time cost. We efficiently generate representative anchors and construct anchor graphs on different views. The discrete cluster assignment matrix is directly obtained by performing clustering on the automatically aggregated graph. FMDC has a linear computational complexity with respect to the data scale, which is a significant improvement compared to the quadratic one. Extensive experiments on benchmark datasets demonstrate its efficiency and effectiveness.

Fast Multi-View Discrete Clustering with Anchor Graphs

Review generation, aiming to automatically generate review text according to the given information, is proposed to assist in the unappealing review writing. However, most of existing methods only consider the overall sentiments of reviews and cannot achieve aspect-level sentiment control. Even though some previous studies attempt to generate aspect-level sentiment-controllable reviews, they usually require large-scale human annotations which are unavailable in the real world. To address this issue, we propose a mutual learning framework to take advantage of unlabeled data to assist the aspect-level sentiment-controllable review generation. The framework consists of a generator and a classifier which utilize confidence mechanism and reconstruction reward to enhance each other. Experimental results show our model can achieve aspect-sentiment control accuracy up to 88% without losing generation quality.

Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework

In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. The framework incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable. Experimental results on a new dataset across four domains show that our framework yields distractors outperforming previous methods both by automatic and human evaluation. The dataset can also be used as a benchmark for distractor generation research in the future.

Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions

Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achieved good results in ST. However, wait-k policy faces two weaknesses: low training speed caused by the recalculation of hidden states and lack of future source information to guide training. For the low training speed, we propose an incremental Transformer with an average embedding layer (AEL) to accelerate the speed of calculation of the hidden states during training. For future-guided training, we propose a conventional Transformer as the teacher of the incremental Transformer, and try to invisibly embed some future information in the model through knowledge distillation. We conducted experiments on Chinese-English and German-English simultaneous translation tasks and compared with the wait-k policy to evaluate the proposed method. Our method can effectively increase the training speed by about 28 times on average at different k and implicitly embed some predictive abilities in the model, achieving better translation quality than wait-k baseline.

Future-Guided Incremental Transformer for Simultaneous Translation

Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results. Prediction fusion is therefore important to achieve accurate panoptic segmentation. In this paper, we present REFINE, pREdiction FusIon NEtwork for panoptic segmentation, to achieve high-quality panoptic segmentation by improving cross-task prediction fusion, and within-task prediction fusion. Our single-model ResNeXt-101 with DCN achieves PQ=51.5 on the COCO dataset, surpassing state-of-the-art performance by a convincing margin and is comparable with ensembled models. Our smaller model with a ResNet-50 backbone achieves PQ=44.9, which is comparable with state-of-the-art methods with larger backbones.

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