GenAI4ELab/papercli-papers huggingface dataset:
Artificial Neuronal Ensembles with Learned Context Dependent Gating
Authors: Matthew James Tilley; Michelle Miller; David Freedman
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.
Learning Multiobjective Program Through Online Learning
Authors: Chaosheng Dong; Yijia Wang; Bo Zeng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We investigate the problem of learning the parameters (i.e., objective functions or constraints) of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization, and prove that this framework converges at a rate of $\mathcal{O}(1/\sqrt{T})$ under certain regularity conditions. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results with both synthetic and real world datasets show that both algorithms can learn the parameters of a multiobjective program with great accuracy and are robust to noise.
Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
Authors: Yu Liu; Mingbo Zhao; Zhao Zhang; Jicong Fan; Yang Lou; Shuicheng YAN
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Deep learning based virtual try-on system has achieved some encouraging progress recently, but there still remain several big challenges that need to be solved, such as trying on arbitrary clothes of all types, trying on the clothes from one category to another and generating image-realistic results with few artifacts. To handle this issue, we propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach includes three modules: 1) Limbs Prediction Module, which is utilized for predicting the human body parts by preserving the characteristics of the reference person. This is especially good for handling cross-category try-on task (e.g., long sleeves \(\leftrightarrow\) short sleeves or long pants \(\leftrightarrow\) skirts, etc.), where the exposed arms or legs with the skin colors and details can be reasonably predicted; 2) Improved Geometric Matching Module, which is designed to warp clothes according to the geometry of the target person. We improve the TPS-based warping method with a compactly supported radial function (Wendland's \(\Psi\)-function); 3) Trade-Off Fusion Module, which is to trade off the characteristics of the warped clothes and the reference person. This module is to make the generated try-on images look more natural and realistic based on a fine-tuning symmetry of the network structure. Extensive simulations are conducted and our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.
On Nullspace of Vision Transformers and What Does it Tell Us?
Authors: Aditya Singh; Haohan Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Nullspace of a linear mapping is the subspace which is mapped to the zero vector. For a linear map, adding an element of the nullspace to its input has no effect on the output of the mapping. We position this work as an exposition towards answering one simple question, ``Does a vision transformer have a non-trivial nullspace?" If TRUE, this would imply that adding elements from this non-trivial nullspace to an input will have no effect on the output of the network. This finding can eventually lead us closer to understanding the generalization properties of vision transformers. In this paper, we first demonstrate that provably a non-trivial nullspace exists for a particular class of vision transformers. This proof is drawn by simply computing the nullspace of the patch embedding matrices. We extend this idea to the non-linear layers of the vision transformer and show that it is possible to learn a non-linear counterpart to the nullspace via simple optimisations for any vision transformer. Subsequently, we perform studies to understand robustness properties of ViTs under nullspace noise. Under robustness, we investigate prediction stability, and (network and interpretation) fooling properties of the noise. Lastly, we provide image watermarking as an application of nullspace noise.
Neural Optimal Transport
Authors: Alexander Korotin; Daniil Selikhanovych; Evgeny Burnaev
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image translation.
Implicit Regularization for Group Sparsity
Authors: Jiangyuan Li; Thanh V Nguyen; Chinmay Hegde; Raymond K. W. Wong
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a diagonally grouped linear neural network. We show the following intriguing property of our reparameterization: gradient descent over the squared regression loss, without any explicit regularization, biases towards solutions with a group sparsity structure. In contrast to many existing works in understanding implicit regularization, we prove that our training trajectory cannot be simulated by mirror descent. We analyze the gradient dynamics of the corresponding regression problem in the general noise setting and obtain minimax-optimal error rates. Compared to existing bounds for implicit sparse regularization using diagonal linear networks, our analysis with the new reparameterization shows improved sample complexity. In the degenerate case of size-one groups, our approach gives rise to a new algorithm for sparse linear regression. Finally, we demonstrate the efficacy of our approach with several numerical experiments.
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer
Authors: Kunchang Li; Yali Wang; Yinan He; Yizhuo Li; Yi Wang; Limin Wang; Yu Qiao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning discriminative spatiotemporal representation is the key problem of video understanding. Recently, Vision Transformers (ViTs) have shown their power in learning long-term video dependency with self-attention. Unfortunately, they exhibit limitations in tackling local video redundancy, due to the blind global comparison among tokens. UniFormer has successfully alleviated this issue, by unifying convolution and self-attention as a relation aggregator in the transformer format. However, this model has to require a tiresome and complicated image-pretraining phrase, before being finetuned on videos. This blocks its wide usage in practice. On the contrary, open-sourced ViTs are readily available and well-pretrained with rich image supervision. Based on these observations, we propose a generic paradigm to build a powerful family of video networks, by arming the pretrained ViTs with efficient UniFormer designs. We call this family UniFormerV2, since it inherits the concise style of the UniFormer block. But it contains brand-new local and global relation aggregators, which allow for preferable accuracy-computation balance by seamlessly integrating advantages from both ViTs and UniFormer. Without any bells and whistles, our UniFormerV2 gets the state-of-the-art recognition performance on 8 popular video benchmarks, including scene-related Kinetics-400/600/700 and Moments in Time, temporal-related Something-Something V1/V2, untrimmed ActivityNet and HACS. In particular, it is the first model to achieve 90% top-1 accuracy on Kinetics-400, to our best knowledge. The models will be released afterward.
Group-Disentangling Conditional Shift
Authors: Dan Andrei Iliescu; Damon Wischik
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a novel group disentanglement method called the Context-Aware Variational Autoencoder (CxVAE). Our model can learn disentangled representations on datasets with conditional shift. This phenomenon occurs when the conditional distribution of the instance-level latent variable $\mathbf{z}$ given the input observation $\mathbf{x}$ changes from one group to another (i.e. $p_i(\mathbf{z}|\mathbf{x}) \neq p_j(\mathbf{z}|\mathbf{x})$, where $i,j$ are two different groups). We show that existing methods fail to learn disentangled representations under this scenario because they infer the group $\mathbf{u}$ and instance $\mathbf{z}$ variables separately. CxVAE overcomes this limitation by conditioning the instance inference on the group variable $q(\mathbf{z}|\mathbf{x},\mathbf{u})$. Our model has the novel ability to disentangle ambiguous observations (those with incomplete information about the generative factors), which we evaluate on the task of fair comparisons between student test scores. Additionally, we demonstrate empirically that conditional shift is the cause of our model's improved performance.
Semi-Variance Reduction for Fair Federated Learning
Authors: Saber Malekmohammadi; Guojun Zhang; Yaoliang Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Ensuring fairness in Federated Learning (FL) systems, i.e. ensuring a satisfactory performance for all of the diverse clients in the systems, is an important and challenging problem. There are multiple fair FL algorithms in the literature, which have been relatively successful in providing fairness. However, these algorithms mostly emphasize on the loss functions of worst-off clients to improve their performance, which often results in the suppression of well-performing ones. As a consequence, the system's overall average performance is usually sacrificed for achieving fairness. Motivated by this and inspired by two well-known risk modeling methods in Finance, Mean-Variance and Mean-Semi-Variance, we propose and study two new fair FL algorithms, Variance Reduction (VRed) and Semi-Variance Reduction (Semi-VRed). VRed encourages equality between clients loss functions by penalizing their variance. In contrast, Semi-VRed penalizes the discrepancy of only the worst-off clients loss functions from the average loss. Through extensive experiments on multiple vision and language datasets, we show that, Semi-VRed achieves SoTA performance in scenarios with highly heterogeneous data distributions by improving both fairness and the system overall average performance at the same time.
Neural Lagrangian Schr\"{o}dinger Bridge: Diffusion Modeling for Population Dynamics
Authors: Takeshi Koshizuka; Issei Sato
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Population dynamics is the study of temporal and spatial variation in the size of populations of organisms and is a major part of population ecology. One of the main difficulties in analyzing population dynamics is that we can only obtain observation data with coarse time intervals from fixed-point observations due to experimental costs or measurement constraints. Recently, modeling population dynamics by using continuous normalizing flows (CNFs) and dynamic optimal transport has been proposed to infer the sample trajectories from a fixed-point observed population. While the sample behavior in CNFs is deterministic, the actual sample in biological systems moves in an essentially random yet directional manner. Moreover, when a sample moves from point A to point B in dynamical systems, its trajectory typically follows the principle of least action in which the corresponding action has the smallest possible value. To satisfy these requirements of the sample trajectories, we formulate the Lagrangian Schrödinger bridge (LSB) problem and propose to solve it approximately by modeling the advection-diffusion process with regularized neural SDE. We also develop a model architecture that enables faster computation of the loss function. Experimental results show that the proposed method can efficiently approximate the population-level dynamics even for high-dimensional data and that using the prior knowledge introduced by the Lagrangian enables us to estimate the sample-level dynamics with stochastic behavior.
Raisin: Residual Algorithms for Versatile Offline Reinforcement Learning
Authors: Braham Snyder; Yuke Zhu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The residual gradient algorithm (RG), gradient descent of the Mean Squared Bellman Error, brings robust convergence guarantees to bootstrapped value estimation. Meanwhile, the far more common semi-gradient algorithm (SG) suffers from well-known instabilities and divergence. Unfortunately, RG often converges slowly in practice. Baird (1995) proposed residual algorithms (RA), weighted averaging of RG and SG, to combine RG's robust convergence and SG's speed. RA works moderately well in the online setting. We find, however, that RA works disproportionately well in the offline setting. Concretely, we find that merely adding a variable residual component to SAC increases its score on D4RL gym tasks by a median factor of 54. We further show that using the minimum of ten critics lets our algorithm match SAC-$N$'s state-of-the-art returns using 50$\times$ less compute and no additional hyperparameters. In contrast, TD3+BC with the same minimum-of-ten-critics trick does not match SAC-$N$'s returns on a handful of environments.
Outlier-Robust Group Inference via Gradient Space Clustering
Authors: Yuchen Zeng; Kristjan Greenewald; Kangwook Lee; Justin Solomon; Mikhail Yurochkin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can improve the worst-group performance, but they can have several limitations: (i) they require group annotations, which are often expensive and sometimes infeasible to obtain, and/or (ii) they are sensitive to outliers. Most related works fail to solve these two issues simultaneously as they focus on conflicting perspectives of minority groups and outliers. We address the problem of learning group annotations in the presence of outliers by clustering the data in the space of gradients of the model parameters. We show that data in the gradient space has a simpler structure while preserving information about minority groups and outliers, making it suitable for standard clustering methods like DBSCAN. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art both in terms of group identification and downstream worst-group performance.
Multi-Agent Multi-Game Entity Transformer
Authors: Rundong Wang; Weixuan Wang; Xianhan Zeng; Liang Wang; Zhenjie Lian; Yiming Gao; Feiyu Liu; Siqin Li; Xianliang Wang; QIANG FU; Yang Wei; Lanxiao Huang; Longtao Zheng; Zinovi Rabinovich; Bo An
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Building large-scale generalist pre-trained models for many tasks is becoming an emerging and potential direction in reinforcement learning (RL). Research such as Gato and Multi-Game Decision Transformer have displayed outstanding performance and generalization capabilities on many games and domains. However, there exists a research blank about developing highly capable and generalist models in multi-agent RL (MARL), which can substantially accelerate progress towards general AI. To fill this gap, we propose Multi-Agent multi-Game ENtity TrAnsformer (MAGENTA) from the entity perspective as an orthogonal research to previous time-sequential modeling. Specifically, to deal with different state/observation spaces in different games, we analogize games as languages, thus training different "tokenizers" for various games. The feature inputs are split according to different entities and tokenized in the same continuous space. Then, two types of transformer-based model are proposed as permutation-invariant architectures to deal with various numbers of entities and capture the attention over different entities. MAGENTA is trained on Honor of Kings, Starcraft II micromanagement, and Neural MMO with a single set of transformer weights. Extensive experiments show that MAGENTA can play games across various categories with arbitrary numbers of agents and increase the efficiency of fine-tuning in new games and scenarios by 50\%-100\%. See our project page at \url{https://sites.google.com/view/rl-magenta}.
Efficiently Controlling Multiple Risks with Pareto Testing
Authors: Bracha Laufer-Goldshtein; Adam Fisch; Regina Barzilay; Tommi S. Jaakkola
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyper-parameter dimensions grow, naively selected settings may lead to sub-optimal and/or unreliable results. We develop an efficient method for calibrating models such that their predictions provably satisfy multiple explicit and simultaneous statistical guarantees (e.g., upper-bounded error rates), while also optimizing any number of additional, unconstrained objectives (e.g., total run-time cost). Building on recent results in distribution-free, finite-sample risk control for general losses, we propose Pareto Testing: a two-stage process which combines multi-objective optimization with multiple hypothesis testing. The optimization stage constructs a set of promising combinations on the Pareto frontier. We then apply statistical testing to this frontier only to identify configurations that have (a) high utility with respect to our objectives, and (b) guaranteed risk levels with respect to our constraints, with specifiably high probability. We demonstrate the effectiveness of our approach to reliably accelerate the execution of large-scale Transformer models in natural language processing (NLP) applications. In particular, we show how Pareto Testing can be used to dynamically configure multiple inter-dependent model attributes—including the number of layers computed before exiting, number of attention heads pruned, or number of text tokens considered—to simultaneously control and optimize various accuracy and cost metrics.
How deep convolutional neural networks lose spatial information with training
Authors: Umberto Maria Tomasini; Leonardo Petrini; Francesco Cagnetta; Matthieu Wyart
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A central question of machine learning is how deep nets  manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information  irrelevant to the task is lost. For image data sets, this view is supported by the observation that after (and not before) training,  the neural representation becomes less and less sensitive to diffeomorphisms acting on images as the signal propagates through the net.  
This loss of sensitivity correlates with performance, and surprisingly  also correlates  with a gain of sensitivity to white noise acquired during training. These facts are unexplained, and as we demonstrate still hold when white noise is added to the images of the training set. Here, we (i) show empirically for various architectures that stability to image diffeomorphisms is achieved by spatial pooling in the first half of the net, and by channel pooling in the second half, (ii) introduce a scale-detection task for a simple model of data where pooling is learnt during training, which captures all empirical observations above and (iii) compute in this model how stability to diffeomorphisms and noise scale with depth. The scalings are found to depend on the presence of strides in the net architecture. We find that the increased sensitivity to noise is due to the perturbing noise piling up during pooling, after a ReLU non-linearity is applied to the noise in the internal layers.
Semi-supervised Community Detection via Structural Similarity Metrics
Authors: Yicong Jiang; Tracy Ke
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Motivated by the interests of social network analysis and network-based recommendation systems, we consider a semi-supervised community detection problem, where the goal is to estimate the community label of a new node by leveraging on the network structure and partially observed community labels of existing nodes. 
We model the network with a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities. 
We propose a fast algorithm that computes a `structural similarity metric' between the new node and each of the $K$ communities, aggregating information in labeled and unlabeled data. The estimated label of the new node is equal to the value of $k$  that maximizes this similarity metric. Our method is computationally fast and compares favorably with existing semi-supervised algorithms on numerical performance. In theory, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. To our best knowledge, our results provide the first semi-supervised community detection algorithm with theoretical guarantees.