GenAI4ELab/papercli-papers huggingface dataset:
Strategic Data Sharing between Competitors
Authors: Nikita Tsoy; Nikola Konstantinov
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Collaborative learning techniques have significantly advanced in recent years, enabling private model training across multiple organizations. Despite this opportunity, firms face a dilemma when considering data sharing with competitors—while collaboration can improve a company’s machine learning model, it may also benefit competitors and hence reduce profits. In this work, we introduce a general framework for analyzing this data-sharing trade-off. The framework consists of three components, representing the firms’ production decisions, the effect of additional data on model quality, and the data-sharing negotiation process, respectively. We then study an instantiation of the framework, based on a conventional market model from economic theory, to identify key factors that affect collaboration incentives. Our findings indicate a profound impact of market conditions on the data-sharing incentives. In particular, we find that reduced competition, in terms of the similarities between the firms’ products, and harder learning tasks foster collaboration.
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
Authors: Shutong Ding; Tianyu Cui; Jingya Wang; Ye Shi
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations (Neural ODEs) are two branches of implicit models that have achieved remarkable success owing to their superior performance and low memory consumption. While both are implicit models, DEQs and Neural ODEs are derived from different mathematical formulations. Inspired by homotopy continuation, we establish a connection between these two models and illustrate that they are actually two sides of the same coin. Homotopy continuation is a classical method of solving nonlinear equations based on a corresponding ODE. Given this connection, we proposed a new implicit model called HomoODE that inherits the property of high accuracy from DEQs and the property of stability from Neural ODEs. Unlike DEQs, which explicitly solve an equilibrium-point-finding problem via Newton's methods in the forward pass, HomoODE solves the equilibrium-point-finding problem implicitly using a modified Neural ODE via homotopy continuation. Further, we developed an acceleration method for HomoODE with a shared learnable initial point. It is worth noting that our model also provides a better understanding of why Augmented Neural ODEs work as long as the augmented part is regarded as the equilibrium point to find. Comprehensive experiments with several image classification tasks demonstrate that HomoODE surpasses existing implicit models in terms of both accuracy and memory consumption.
Reduced Policy Optimization for Continuous Control with Hard Constraints
Authors: Shutong Ding; Jingya Wang; Yali Du; Ye Shi
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Recent advances in constrained reinforcement learning (RL) have endowed reinforcement learning with certain safety guarantees. However, deploying existing constrained RL algorithms in continuous control tasks with general hard constraints remains challenging, particularly in those situations with non-convex hard constraints. Inspired by the generalized reduced gradient (GRG) algorithm, a classical constrained optimization technique, we propose a reduced policy optimization (RPO) algorithm that combines RL with GRG to address general hard constraints. RPO partitions actions into basic actions and nonbasic actions following the GRG method and outputs the basic actions via a policy network. Subsequently, RPO calculates the nonbasic actions by solving equations based on equality constraints using the obtained basic actions. The policy network is then updated by implicitly differentiating nonbasic actions with respect to basic actions. Additionally, we introduce an action projection procedure based on the reduced gradient and apply a modified Lagrangian relaxation technique to ensure inequality constraints are satisfied. To the best of our knowledge, RPO is the first attempt that introduces GRG to RL as a way of efficiently handling both equality and inequality hard constraints. It is worth noting that there is currently a lack of RL environments with complex hard constraints, which motivates us to develop three new benchmarks: two robotics manipulation tasks and a smart grid operation control task. With these benchmarks, RPO achieves better performance than previous constrained RL algorithms in terms of both cumulative reward and constraint violation. We believe RPO, along with the new benchmarks, will open up new opportunities for applying RL to real-world problems with complex constraints.
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Authors: Yi Wu; Ziqiang Li; Chaoyue Wang; Heliang Zheng; Shanshan Zhao; Bin Li; Dacheng Tao
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called $\textbf{Domain Re-Modulation (DoRM)}$. DoRM not only meets the criteria of $\textit{high quality}$, $\textit{large synthesis diversity}$, and $\textit{cross-domain consistency}$, which were achieved by previous research in GDA, but also incorporates $\textit{memory}$ and $\textit{domain association}$, akin to how human brains operate. Specifically, DoRM freezes the source generator and introduces new mapping and affine modules (M\&A modules) to capture the attributes of the target domain during GDA. This process resembles the formation of new synapses in human brains. Consequently, a linearly combinable domain shift occurs in the style space. By incorporating multiple new M\&A modules, the generator gains the capability to perform high-fidelity multi-domain and hybrid-domain generation. Moreover, to maintain cross-domain consistency more effectively, we introduce a similarity-based structure loss. This loss aligns the auto-correlation map of the target image with its corresponding auto-correlation map of the source image during training. Through extensive experiments, we demonstrate the superior performance of our DoRM and similarity-based structure loss in few-shot GDA, both quantitatively and qualitatively. Code will be available at https://github.com/wuyi2020/DoRM.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Authors: Jean-Yves Franceschi; Mike Gartrell; Ludovic Dos Santos; Thibaut Issenhuth; Emmanuel de Bezenac; Mickael Chen; Alain Rakotomamonjy
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions using differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper we challenge this interpretation, and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.
State Regularized Policy Optimization on Data with Dynamics Shift
Authors: Zhenghai Xue; Qingpeng Cai; Shuchang Liu; Dong Zheng; Peng Jiang; Kun Gai; Bo An
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. 
To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings.
Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Authors: Hao Wang; Luxi He; Rui Gao; Flavio Calmon
Venue: NeurIPS 2023 (NeurIPS 2023 spotlight)

Abstract: Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination, which is inherent in the data distribution, and epistemic discrimination, which is due to decisions made during model development. We quantify aleatoric discrimination by determining the performance limits of a model under fairness constraints, assuming perfect knowledge of the data distribution. We demonstrate how to characterize aleatoric discrimination by applying Blackwell's results on comparing statistical experiments. We then quantify epistemic discrimination as the gap between a model's accuracy when fairness constraints are applied and the limit posed by aleatoric discrimination. We apply this approach to benchmark existing fairness interventions and investigate fairness risks in data with missing values. Our results indicate that state-of-the-art fairness interventions are effective at removing epistemic discrimination on standard (overused) tabular datasets. However, when data has missing values, there is still significant room for improvement in handling aleatoric discrimination.
Rank-DETR for High Quality Object Detection
Authors: Yifan Pu; Weicong Liang; Yiduo Hao; Yuhui Yuan; Yukang Yang; Chao Zhang; Han Hu; Gao Huang
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Authors: Qi Xu; Yuyuan Gao; Jiangrong Shen; Yaxin Li; Xuming Ran; Huajin Tang; Gang Pan
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.
Regularization properties of adversarially-trained linear regression
Authors: Antonio H. Ribeiro; Dave Zachariah; Francis Bach; Thomas B. Schön
Venue: NeurIPS 2023 (NeurIPS 2023 spotlight)

Abstract: State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the  minimum-norm  interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial training---as in square-root Lasso---the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples.
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
Authors: Federico Errica
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Researchers have used nearest neighbor graphs to transform classical machine learning problems on tabular data into node classification tasks to solve with graph representation learning methods. Such artificial structures often reflect the homophily assumption, believed to be a key factor in the performances of deep graph networks. In light of recent results demystifying these beliefs, we introduce a theoretical framework to understand the benefits of Nearest Neighbor (NN) graphs when a graph structure is missing. We formally analyze the Cross-Class Neighborhood Similarity (CCNS), used to empirically evaluate the usefulness of structures, in the context of nearest neighbor graphs. Moreover, we study the class separability induced by deep graph networks on a k-NN graph. Motivated by the theory, our quantitative experiments demonstrate that, under full supervision, employing a k-NN graph offers no benefits compared to a structure-agnostic baseline. Qualitative analyses suggest that our framework is good at estimating the CCNS and hint at k-NN graphs never being useful for such classification tasks under full supervision, thus advocating for the study of alternative graph construction techniques in combination with deep graph networks.
Imagine That! Abstract-to-Intricate Text-to-Image Synthesis with Scene Graph Hallucination Diffusion
Authors: Shengqiong Wu; Hao Fei; Hanwang Zhang; Tat-Seng Chua
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: In this work, we investigate the task of text-to-image (T2I) synthesis under the abstract-to-intricate setting, i.e., generating intricate visual content from simple abstract text prompts. Inspired by human imagination intuition, we propose a novel scene-graph hallucination (SGH) mechanism for effective abstract-to-intricate T2I synthesis. SGH carries out scene hallucination by expanding the initial scene graph (SG) of the input prompt with more feasible specific scene structures, in which the structured semantic representation of SG ensures high controllability of the intrinsic scene imagination. To approach the T2I synthesis, we deliberately build an SG-based hallucination diffusion system. First, we implement the SGH module based on the discrete diffusion technique, which evolves the SG structure by iteratively adding new scene elements. Then, we utilize another continuous-state diffusion model as the T2I synthesizer, where the overt image-generating process is navigated by the underlying semantic scene structure induced from the SGH module. On the benchmark COCO dataset, our system outperforms the existing best-performing T2I model by a significant margin, especially improving on the abstract-to-intricate T2I generation. Further in-depth analyses reveal how our methods advance.
Greatness in Simplicity: Unified Self-Cycle Consistency for Parser-Free Virtual Try-On
Authors: Chenghu Du; junyin Wang; Shuqing Liu; Shengwu Xiong
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Image-based virtual try-on tasks remain challenging, primarily due to inherent complexities associated with non-rigid garment deformation modeling and strong feature entanglement of clothing within human body. Recent groundbreaking formulations, such as in-painting, cycle consistency, and knowledge distillation, have facilitated self-supervised generation of try-on images. However, these paradigms necessitate the disentanglement of garment features within human body features through auxiliary tasks, such as leveraging 'teacher knowledge' and dual generators. The potential presence of irresponsible prior knowledge in the auxiliary task can serve as a significant bottleneck for the main generator (e.g., 'student model') in the downstream task. Moreover, existing garment deformation methods lack the ability to perceive the correlation between the garment and the human body in the real world, leading to unrealistic alignment effects. To tackle these limitations, we present a new parser-free virtual try-on network based on unified self-cycle consistency (USC-PFN), which enables robust translation between different garments using just a single generator, faithfully replicating non-rigid geometric deformation of garments in real-life scenarios. Specifically, we first propose a self-cycle consistency architecture with a circular mode. It utilizes real unpaired garment-person images exclusively as input for training, effectively eliminating the impact of irresponsible prior knowledge at the model input end. Additionally, we formulate a Markov Random Field to simulate a more natural and realistic garment deformation. Furthermore, USC-PFN can leverage a general generator for self-supervised cycle training. Experiments demonstrate that our method achieves state-of-the-art performance on a popular virtual try-on benchmark.
Exploring Question Decomposition for Zero-Shot VQA
Authors: Zaid Khan; Vijay Kumar b g; Samuel Schulter; Manmohan Chandraker; Yun Fu
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Visual question answering (VQA) has traditionally been treated as a single-step task where each question receives the same amount of effort, unlike natural human question-answering strategies. We explore a question decomposition strategy for VQA to overcome this limitation. We probe the ability of recently developed large vision-language models to use human-written decompositions and produce their own decompositions of visual questions, finding they are capable of learning both tasks from demonstrations alone.
However, we show that naive application of model-written decompositions can hurt performance.
We introduce a model-driven selective decomposition approach for second-guessing predictions and correcting errors, and validate its effectiveness on eight VQA tasks across three domains, showing consistent improvements in accuracy, including improvements of >20% on medical VQA datasets and boosting the zero-shot performance of BLIP-2 above chance on a VQA reformulation of the challenging Winoground task. Project Site: https://zaidkhan.me/decomposition-0shot-vqa/
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.
Authors: Mingjia Shi; Yuhao Zhou; Kai Wang; Huaizheng Zhang; Shudong Huang; Qing Ye; Jiancheng Lv
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the performance of the localized model. Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data. Such a global model may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL. At the heart of our proposed approach is a framework, the $\textit{PFL with Bregman Divergence}$ (pFedBreD), decoupling the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized scenarios. We also relax the mirror descent (RMD) to extract the prior explicitly to provide optional strategies. Additionally, our pFedBreD is backed up by a convergence analysis. Sufficient experiments demonstrate that our method reaches the $\textit{state-of-the-art}$ performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks. Extensive analyses verify the robustness and necessity of proposed designs. The code will be made public.