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Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.

AAAI 2023

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification

ml: multi-class/multi-label learning & extreme classification peai: bias

ml: bias and fairness

fairness & equity

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to leverage kernel-level information for image deblurring networks by inserting Fourier transform, ReLU operation, and inverse Fourier transform to the standard ResBlock. 1 $\times$ 1 convolution is further added to let the network modulate flexible thresholds for frequency selection. We term our newly built block as Res FFT-ReLU Block, which takes advantages of both kernel-level and pixel-level features via learning frequency-spatial dual-domain representations. Extensive experiments are conducted to acquire a thorough analysis on the insights of the method. Moreover, after plugging the proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset. Our method noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity. Code is available at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.

Intriguing Findings of Frequency Selection for Image Deblurring

We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training. To address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN) in this paper, which is able to realize where and how to edit the image regions of interest. Specifically, the image editor in cManiGAN learns to identify and complete the input image, while cross-modal interpreter and reasoner are deployed to verify the semantic correctness of the output image based on the input instruction. While the former utilizes factual/counterfactual description learning for authenticating the image semantics, the latter predicts the "undo" instruction and provides pixel-level supervision for the training of cManiGAN. With the above operational cycle-consistency, our cManiGAN can be trained in the above weakly supervised setting. We conduct extensive experiments on the datasets of CLEVR and COCO datasets, and the effectiveness and generalizability of our proposed method can be successfully verified.

Target-free Text-guided Image Manipulation

Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of actions, and noise level. Thus, the data-driven estimator selection problem is becoming increasingly important and can have a significant impact on the accuracy of OPE. However, identifying the most accurate estimator using only the logged data is quite challenging because the ground-truth estimation accuracy of estimators is generally unavailable. This paper thus studies this challenging problem of estimator selection for OPE for the first time. In particular, we enable an estimator selection that is adaptive to a given OPE task, by appropriately subsampling available logged data and constructing pseudo policies useful for the underlying estimator selection task. Comprehensive experiments on both synthetic and real-world company data demonstrate that the proposed procedure substantially improves the estimator selection compared to a non-adaptive heuristic.

Policy-Adaptive Estimator Selection for Off-Policy Evaluation

Occlusion and motion blur make it challenging to interpolate video frame, since estimating complex motions between two frames is hard and unreliable, especially in highly dynamic scenes. This paper aims to address these issues by exploiting spike stream as auxiliary visual information between frames to synthesize target frames. Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). It extracts the scene structure and objects' outline feature maps of the target frames from spike stream. Those feature maps are fused with the color and texture feature maps extracted from RGB frames to synthesize target frames. Benefited by the spike stream that contains consecutive information between two frames, SVFI can directly extract the information in occlusion and motion blur areas of target frames from spike stream, thus it is more robust than previous optical flow-based methods. Experiments show SVFI outperforms the SOTA methods on wide variety of datasets. For instance, in 7 and 15 frame skip evaluations, it shows up to 5.58 dB and 6.56 dB improvements in terms of PSNR over the corresponding second best methods BMBC and DAIN. SVFI also shows visually impressive performance in real-world scenes.

SVFI: Spiking-based Video Frame Interpolation for High-Speed Motion

The study of implicit regularization induced by gradient-based optimization in deep learning is a long-standing pursuit. In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) in the continuous-time view, so-called momentum gradient flow (MGF).   We show that the components of weight vector are learned for a depth-$N$ linear neural network at  the different evolution rates, and this evolution gap increases with the depth $N$. Firstly, we show that if $N=1$, the evolution gap between the weight components is linear, which is consistent with the performance of ridge. In particular, we establish a tight coupling between MGF and ridge which for least squares regression. In details, we prove that, under the calibration $t=\sqrt{2/\lambda}$, where $t$ is the time  parameter in MGF and $\lambda$ is the tuning parameter in ridge, the risk of MGF is no more than 1.54 times that of ridge and their relative Bayesian risk is hardly different. Secondly,  if the model becomes deeper, i.e. $N\geq2$, the evolution gap becomes more significant, which implies an implicit bias towards sparse solutions. The numerical experiments strongly support our theoretical results.

The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate
the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.

ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges

By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer has to go through every pre-placed exiting layer until it exits. In addition, it is also hard to adjust the configurations of the computing platforms alongside the inference proceeds. By incorporating a low-cost prediction engine, we propose a Predictive Exit framework for computation- and energy-efficient deep learning applications. Predictive Exit can forecast where the network will exit (i.e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer. Moreover, according to the number of remaining layers, proper computing configurations (i.e., frequency and voltage) are selected to execute the network to further save energy. Extensive experimental results demonstrate that Predictive Exit achieves up to 96.2% computation reduction and 72.9% energy-saving compared with classic deep learning networks; and 12.8% computation reduction and 37.6% energy-saving compared with the early exit under state-of-the-art exiting strategies, given the same inference accuracy and latency.

Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference

Choreography refers to creation of dance motions according to both music and dance knowledge, where the created dances should be style-specific and consistent. However, most of the existing methods generate dances using the given music as the only reference, lacking the stylized dancing knowledge, namely, the flag motion patterns contained in different styles. Without the stylized prior knowledge, these approaches are not promising to generate controllable style or diverse moves for each dance style, nor new dances complying with stylized knowledge. To address this issue, we propose a novel music-to-dance generation framework guided by style embedding, considering both input music and stylized dancing knowledge. These style embeddings are learnt representations of style-consistent kinematic abstraction of reference dance videos, which can act as controllable factors to impose style constraints on dance generation in a latent manner. Hence, we can make the style embedding fit into any given style while allowing the flexibility to generate new compatible dance moves by modifying the style embedding according to the learnt representations of a certain style. We are the first to achieve knowledge-driven style control in dance generation tasks. To support this study, we build a large multi-style music-to-dance dataset referred to as I-Dance. The qualitative and quantitative evaluations demonstrate the advantage of the proposed framework, as well as the ability to synthesize diverse moves under a dance style directed by style embedding.

Mining and Applying Composition Knowledge of Dance Moves for Style-concentrated Dance Generation

Graph convolutional networks (GCNs) have been grabbing widespread attentions due to their encouraging performance and powerful generalizations. However, few works provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centered optimization framework, in which by building appropriate regularizers we could interpret most existing GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture both topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we borrow the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

Vaccine delivery in under-resourced locations with security
risks is not just challenging but also life threatening. The
COVID pandemic and the need to vaccinate added even more
urgency to this issue. Motivated by this problem, we propose
a general framework to set-up limited temporary (vaccination)
centers that balance physical security and desired (vaccine)
service coverage with limited resources. We set-up the
problem as a Stackelberg game between the centers operator
(defender) and an adversary, where the set of centers is not
fixed a priori but is part of the decision output. This results in
a mixed combinatorial and continuous optimization problem.
As part of our scalable approximation solution, we provide a
fundamental contribution by identifying general duality conditions
of switching max and min when both discrete and continuous
variables are involved. Via detailed experiments, we
show that the solution proposed is scalable in practice.

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Intriguing Findings of Frequency Selection for Image Deblurring

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SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models

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Next from AAAI 2023

Intriguing Findings of Frequency Selection for Image Deblurring

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SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models

Stay up to date with the latest Underline news!

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