United States

With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider a general, but realistic, scenario in which a predictive model is learned from (potentially biased) training data, and model predictions are assessed post-hoc for fairness by some auditing method. We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. Unlike prior work, we evaluate the downstream impacts of data biases quantitatively rather than qualitatively and prove theoretical guarantees for detection. Under reasonable assumptions, we quantify how the amount of bias in the model predictions varies as a function of the amount of differential sampling bias in the data, and at what point this bias becomes provably detectable by the auditor. Through experiments on two criminal justice datasets-- the well-known COMPAS dataset and historical data from NYPD&#39;s stop and frisk policy-- we demonstrate that the theoretical results hold in practice even when our assumptions are relaxed.

AAAI 2023

Provable Detection of Propagating Sampling Bias in Prediction Models

machine-learning

fairness

bias

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider a general, but realistic, scenario in which a predictive model is learned from (potentially biased) training data, and model predictions are assessed post-hoc for fairness by some auditing method. We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. Unlike prior work, we evaluate the downstream impacts of data biases quantitatively rather than qualitatively and prove theoretical guarantees for detection. Under reasonable assumptions, we quantify how the amount of bias in the model predictions varies as a function of the amount of differential sampling bias in the data, and at what point this bias becomes provably detectable by the auditor. Through experiments on two criminal justice datasets-- the well-known COMPAS dataset and historical data from NYPD's stop and frisk policy-- we demonstrate that the theoretical results hold in practice even when our assumptions are relaxed.

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. 
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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.

In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones. This issue is addressed in recent methods by storing the important gradient spaces for old tasks and updating the model orthogonally during new tasks. However, such restrictive orthogonal gradient updates hamper the learning capability of the new tasks resulting in sub-optimal performance. To improve new learning while minimizing forgetting, in this paper we propose a Scaled Gradient Projection (SGP) method, where we combine the orthogonal gradient projections with scaled gradient steps along the important gradient spaces for the past tasks. The degree of gradient scaling along these spaces depends on the importance of the bases spanning them. We propose an efficient method for computing and accumulating importance of these bases using the singular value decomposition of the input representations for each task. We conduct extensive experiments ranging from continual image classification to reinforcement learning tasks and report better performance with less training overhead than the state-of-the-art approaches.

Continual Learning with Scaled Gradient Projection

Data-driven medium-range weather forecasting has attracted much attention in recent years. However, the forecasting accuracy at high resolution is unsatisfactory currently. Pursuing high-resolution and high-quality weather forecasting, we develop a data-driven model SwinRDM which integrates an improved version of SwinRNN with a diffusion model. SwinRDM performs predictions at $0.25^\circ$ resolution and achieves superior forecasting accuracy to IFS (Integrated Forecast System), the state-of-the-art operational NWP model, on representative atmospheric variables including 500 hPa geopotential (Z500), 850 hPa temperature (T850), 2-m temperature (T2M), and total precipitation (TP), at lead times of up to 5 days. We propose to leverage a two-step strategy to achieve high-resolution predictions at $0.25^\circ$ considering the trade-off between computation memory and forecasting accuracy. Recurrent predictions for future atmospheric fields are firstly performed at $1.40625^\circ$ resolution, and then a diffusion-based super-resolution model is leveraged to recover the high spatial resolution and finer-scale atmospheric details. SwinRDM pushes forward the performance and potential of data-driven models for a large margin towards operational applications.

SwinRDM: Integrate SwinRNN with Diffusion Model Towards High-Resolution and High-Quality Weather Forecasting

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid \textit{spatial frequency collapse} and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data are publicly available\footnote{\url{https://github.com/seanywang0408/RadianceMapping}}.

Boosting Point Clouds Rendering via Radiance Mapping

It is assumed that pre-training provides the feature extractor with strong class transferability and that high novel class generalization can be achieved by simply reusing the transferable feature extractor. In this work, our motivation is to explicitly learn some fine-grained and transferable meta-knowledge so that feature reusability can be further improved. Concretely, inspired by the fact that humans can use learned concepts or components to help them recognize novel classes, we propose Compositional Prototypical Networks (CPN) to learn a transferable prototype for each human-annotated attribute, which we call a component prototype. We empirically demonstrate that the learned component prototypes have good class transferability and can be reused to construct compositional prototypes for novel classes. Then a learnable weight generator is utilized to adaptively fuse the compositional and visual prototypes. Extensive experiments demonstrate that our method can achieve state-of-the-art results on different datasets and settings. The performance gains are especially remarkable in the 5-way 1-shot setting. The code is available at https://github.com/fikry102/CPN.

Compositional Prototypical Networks for Few-Shot Classification

Raven’s Progressive Matrices (RPMs) have been widely used to evaluate the visual reasoning ability of humans. To tackle the challenges of visual perception and logic reasoning on RPMs, we propose a Hierarchical ConViT with Attention-based Relational Reasoner (HCV-ARR). Traditional solution methods often apply relatively shallow convolution networks to visually perceive shape patterns in RPM images, which may not fully model the long-range dependencies of complex pattern combinations in RPMs. The proposed ConViT consists of a convolutional block to capture the low-level attributes of visual patterns, and a transformer block to capture the high-level image semantics such as pattern formations. Furthermore, the proposed hierarchical ConViT captures visual features from multiple receptive fields, where the shallow layers focus on the image fine details while the deeper layers focus on the image semantics. To better model the underlying reasoning rules embedded in RPM images, an Attention-based Relational Reasoner (ARR) is proposed to establish the underlying relations among images. The proposed ARR well exploits the hidden relations among question images through the developed element-wise attentive reasoner. Experimental results on three RPM datasets demonstrate that the proposed HCV-ARR achieves a significant performance gain compared with the state-of-the-art models.

Hierarchical ConViT with Attention-based Relational Reasoner for Visual Analogical Reasoning

Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields. However, most previous works only discuss the SINGLE task setting which is not aligned with real clinical setting, where pathologists often conduct multiple diagnosis tasks simultaneously. Also, it is commonly recognized that the multi-task learning paradigm can improve learning efficiency by exploiting commonalities and differences across multiple tasks. To this end, we present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer equipped with Task-aware Knowledge Injection and Domain Knowledge-driven Graph Pooling modules. Basically, with the Graph Neural Network and Transformer as the building commons, our framework is able to learn task-agnostic low-level local information as well as task-specific high-level global representation. Considering that different tasks in WSI analysis depend on different features and properties, we also design a novel Task-aware Knowledge Injection module to transfer the task-shared graph embedding into task-specific feature spaces to learn more accurate representation for different tasks. Further, we elaborately design a novel Domain Knowledge-driven Graph Pooling module for each task to improve both the accuracy and robustness of different tasks by leveraging different diagnosis patterns of multiple tasks. We evaluated our method on two public WSI datasets from TCGA projects, i.e., esophageal carcinoma and kidney carcinoma. Experimental results show that our method outperforms single-task counterparts and the state-of-theart methods on both tumor typing and staging tasks.

MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis

Spike camera is a kind of neuromorphic sensor that uses a novel ``integrate-and-fire'' mechanism to generate a continuous spike stream to record the dynamic light intensity at extremely high temporal resolution. However, as a trade-off for high temporal resolution, its spatial resolution is limited, resulting in inferior reconstruction details. To address this issue, this paper develops a network (SpikeSR-Net) to super-resolve a high-resolution image sequence from the low-resolution binary spike streams. SpikeSR-Net is designed based on the observation model of spike camera and exploits both the merits of model-based and learning-based methods. To deal with the limited representation capacity of binary data, a pixel-adaptive spike encoder is proposed to convert spikes to latent representation to infer clues on intensity and motion. Then, a motion-aligned super resolver is employed to exploit long-term correlation, so that the dense sampling in temporal domain can be exploited to enhance the spatial resolution without introducing motion blur. Experimental results show that SpikeSR-Net is  promising in super-resolving higher-quality images for spike camera.

Learning to Super-Resolve Dynamic Scenes for Neuromorphic Spike Camera

We study the properties of elections that have a given position matrix (in such elections each candidate is ranked on each position by a given number of voters).  We show that counting elections that generate a given position matrix is #P-complete. Consequently, sampling such elections uniformly at random is challenging and we propose a simple algorithm that does not offer guarantees on the distribution. Next, we consider the problem of testing if a given matrix can be implemented by an election with certain structure (such as single-peakedness or group-separability). Finally, we consider the problem of testing if a given position matrix can be implemented by an election with a Condorcet winner.  We complement our theoretical findings with experiments.

Properties of Position Matrices and Their Elections

Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. By exploring domain-invariant 3D geometric characteristics and motion patterns, we present an unsupervised domain adaptation method that overcomes above difficulties. First, we propose the Spatial Geometry Alignment module to extract similar 3D shape geometric features of the same object class to align two domains, while eliminating the effect of distinct point distributions. Second, we present Temporal Motion Alignment module to utilize motion features in sequential frames of data to match two domains. Prototypes generated from two modules are incorporated into the pseudo-label reweighting procedure and contribute to our effective self-training framework for the target domain. Extensive experiments show that our method achieves state-of-the-art performance on cross-device datasets, especially for the datasets with large gaps captured by mechanical scanning LiDARs and solid-state LiDARs in various scenes. 

CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection

In dialogue state tracking (DST), the exploitation of dialogue history is a crucial research direction, and the existing DST models can be divided into two categories: full-history models and partial-history models. Since the “select first, use later” mechanism explicitly filters the distracting information being passed to the downstream state prediction, the partial-history models have recently achieved a performance advantage over the full-history models. However, besides the redundant information, some critical dialogue context information was inevitably filtered out by the partial-history models simultaneously. To reconcile the contextual consideration with avoiding the introduction of redundant information, we propose DICE-DST, a model-agnostic module widely applicable to the partial-history DST models, which aims to strengthen the ability of context exploitation for the encoder of each DST model. Specifically, we first construct a teacher encoder and devise two contextual reasoning tasks to train it to acquire extensive dialogue contextual knowledge. Then we transfer the contextual knowledge from the teacher encoder to the student encoder via a novel turn-level attention-alignment distillation. Experimental results show that our approach extensively improves the performance of partial-history DST models and thereby achieves new state-of-the-art performance on multiple mainstream datasets while keeping high efficiency.

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Continual Learning with Scaled Gradient Projection

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