GenAI4ELab/papercli-papers huggingface dataset:
Target-Free Ligand Scoring via One-Shot Learning
Authors: Peter Eckmann; Jake Anderson; Michael K Gilson; Rose Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Scoring ligands in a library based on their structural similarity to a known hit compound is widely used in drug discovery following high-throughput screening. However, such "similarity search" relies on the assumption that structurally similar compounds have similar activities, and will therefore only retrieve ligands with hit-like affinity, requiring resource-intensive tweaking by medicinal chemists to reach a more active lead compound. We propose a novel approach, One-Shot Ligand Scoring (OSLS), that is much more capable of directly retrieving lead-like compounds from a library using a novel one-shot learning technique. For this new task, we design a Siamese-inspired neural architecture using two Transformer encoders without tied weights, a novel positional encoding-like mechanism, and a final prediction head. OSLS is able to score ligands by activity against a target without any target-specific knowledge beyond a single known activity value, a cost-effective approach to ligand-based or phenotypic drug discovery. We show that OSLS surpasses traditional similarity search as well as modern deep learning baselines on a simulated ligand retrieval task. Furthermore, we demonstrate the applicability of our approach on various drug discovery tasks that also involve ligand scoring, including drug repositioning, precision patient-level drug efficacy prediction, and even molecular generative modeling.
Any-scale Balanced Samplers for Discrete Space
Authors: Haoran Sun; Bo Dai; Charles Sutton; Dale Schuurmans; Hanjun Dai
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The locally balanced informed proposal has proved to be highly effective for sampling from discrete spaces. However, its success relies on the "local'' factor, which ensures that whenever the proposal distribution is restricted to be near the current state, the locally balanced weight functions are asymptotically optimal and the gradient approximations are accurate.  In seeking a more efficient sampling algorithm, many recent works have considered increasing the scale of the proposal distributions, but this causes the "local'' factor to no longer hold. Instead, we propose any-scale balanced samplers to repair the gap in non-local proposals. In particular, we substitute the locally balanced function with an any-scale balanced function that can self-adjust to achieve better efficiency for proposal distributions at any scale. We also use quadratic approximations to capture curvature of the target distribution and reduce the error in the gradient approximation, while employing a Gaussian integral trick with a special estimated diagonal to efficiently sample from the quadratic proposal distribution. On various synthetic and real distributions, the proposed sampler substantially outperforms existing approaches.
Group-level Brain Decoding with Deep Learning
Authors: Richard Csaky; Mats W.J. Van Es; Oiwi Parker Jones; Mark Woolrich
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Decoding experimental variables from brain imaging data is gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects. Here, we propose a method that uses subject embedding, analogous to word embedding in Natural Language Processing, to learn and exploit the structure in between subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (but impair high-accuracy subjects) and can be helpful for initialising subject models. The potential of such group modelling is even higher with bigger datasets. To better enable physiological interpretation at the group level we demonstrate the use of permutation feature importance developing insights into the spatio-temporal and spectral information encoded in the models. All code is available on GitHub.
FedMT: Federated Learning with Mixed-type Labels
Authors: Qiong Zhang; Aline Talhouk; Gang Niu; Xiaoxiao Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple centers without exchanging data across them, and thus improves sample efficiency. In the classical setting of FL, the same labeling criterion is usually employed across all centers being involved in training. This constraint greatly limits the applicability of FL. For example, standards used for disease diagnosis are more likely to be different across clinical centers, which mismatches the classical FL setting. In this paper, we consider an important yet under-explored setting of FL, namely FL with mixed-type labels where different labeling criteria can be employed by various centers, leading to inter-center label space differences and challenging existing FL methods designed for the classical setting. To effectively and efficiently train models with mixed-type labels, we propose a theory-guided and model-agnostic approach that can make use of the underlying correspondence between those label spaces and can be easily combined with various FL methods such as FedAvg. We present convergence analysis based on over-parameterized ReLU networks. We show that the proposed method can achieve linear convergence in label projection, and demonstrate the impact of the parameters of our new setting on the convergence rate. The proposed method is evaluated and the theoretical findings are validated on benchmark and medical datasets.
Non-Gaussian Process Regression
Authors: Yaman Kindap; Simon J. Godsill
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outliers and shocks routinely observed. In these cases GPs can fail to model uncertainty adequately and may over-smooth inferences. Here we extend the GP framework into a new class of time-changed GPs that allow for straightforward modelling of heavy-tailed non-Gaussian behaviours, while retaining a tractable conditional GP structure through an infinite mixture of non-homogeneous GPs representation. The conditional GP structure is obtained by conditioning the observations on a latent transformed input space and the random evolution of the latent transformation is modelled using a Lévy process which allows Bayesian inference in both the posterior predictive density and the latent transformation function. We present  Markov chain Monte Carlo inference procedures for this model and demonstrate the potential benefits compared to a standard GP.
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
Authors: Chongjian GE; Jiangliu Wang; Zhan Tong; Shoufa Chen; Yibing Song; Ping Luo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Contrastive learning methods train visual encoders by comparing views (e.g., often created via a group of data augmentations on the same instance) from one instance to others. Typically, the views created from one instance are set as positive, while views from other instances are negative. This binary instance discrimination is studied extensively to improve feature representations in self-supervised learning. In this paper, we rethink the instance discrimination framework and find the binary instance labeling insufficient to measure correlations between different samples. For an intuitive example, given a random image instance, there may exist other images in a mini-batch whose content meanings are the same (i.e., belonging to the same category) or partially related (i.e., belonging to a similar category). How to treat the images that correlate similarly to the current image instance leaves an unexplored problem. We thus propose to support the current image by exploring other correlated instances (i.e., soft neighbors). We first carefully cultivate a candidate neighbor set, which will be further utilized to explore the highly-correlated instances. A cross-attention module is then introduced to predict the correlation score (denoted as positiveness) of other correlated instances with respect to the current one. The positiveness score quantitatively measures the positive support from each correlated instance, and is encoded into the objective for pretext training. To this end, our proposed method benefits in discriminating uncorrelated instances while absorbing correlated instances for SSL. We evaluate our soft neighbor contrastive learning method (SNCLR) on standard visual recognition benchmarks, including image classification, object detection, and instance segmentation. The state-of-the-art recognition performance shows that SNCLR is effective in improving feature representations from both ViT and CNN encoders.
Modeling content creator incentives on algorithm-curated platforms
Authors: Jiri Hron; Karl Krauth; Michael Jordan; Niki Kilbertus; Sarah Dean
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by modern algorithms including factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices—e.g., non-negative vs. unconstrained factorization—significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models like ours for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups.
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation
Authors: Utkarsh Nath; Yancheng Wang; Yingzhen Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep Neural Networks are vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the driving tools of deep neural networks, demonstrates superior performance in prediction accuracy in various machine learning applications. However, it is unclear how it performs against adversarial attacks. Given the presence of a robust teacher, it would be interesting to investigate if NAS would produce robust neural architecture by inheriting robustness from the teacher. In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation. Unlike previous knowledge distillation methods that encourage close student/teacher output only in the last layer, RNAS-CL automatically searches for the best teacher layer to supervise each student layer. Experimental result evidences the effectiveness of RNAS-CL and shows that RNAS-CL produces small and robust neural architecture.
Label-distribution-agnostic Ensemble Learning on Federated Long-tailed Data
Authors: Yaopei Zeng; Lei Liu; Baoyuan Wu; ShaoGuo Liu; Li Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables devices to collaboratively train a shared model. However, the long-tailed distribution in nature deteriorates the performance of the global model, which is difficult to address due to data heterogeneity, e.g., local clients may exhibit diverse imbalanced class distributions. Moreover, existing re-balance strategies generally utilize label distribution as the class prior, which may conflict with the privacy requirement of FL. To this end, we propose a Label-Distribution-Agnostic Ensemble (LDAE) learning framework to integrate heterogeneous data distributions using multiple experts, which targets to optimize a balanced global objective under privacy protection. In particular, we derive a privacy-preserving proxy from the model updates of clients to guide the grouping and updating of multiple experts. Knowledge from clients can be aggregated via implicit interactions among different expert groups. We theoretically and experimentally demonstrate that (1) there is a global objective gap between global and local re-balance strategies\footnote{The local re-balance strategy means that each client utilizes re-balance methods based on the local label distribution, while the global re-balance strategy applies re-balance methods using global label distribution as the class-wise prior.} and (2) with protecting data privacy, the proxy can be used as an alternative to label distribution for existing class prior based re-balance strategies. Extensive experiments on long-tailed decentralized datasets demonstrate the effectiveness of our method, showing superior performance to state-of-the-art methods.
One-Step Estimator for Permuted Sparse Recovery
Authors: Hang Zhang; Ping Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper considers the unlabeled sparse recovery under multiple measurements, i.e., ${\mathbf{Y}} = {\mathbf{\Pi}}^{\natural}{\mathbf{X}} {\mathbf{B}}^{\natural} + {\mathbf{W}}$, where ${\mathbf{Y}} \in \mathbb{R}^{n\times m}, {\mathbf{\Pi}}^{\natural}\in \mathbb{R}^{n\times n}, {\mathbf{X}} \in \mathbb{R}^{n\times p}, {\mathbf{B}}^{\natural}\in \mathbb{R}^{p\times m}, {\mathbf{W}}\in \mathbb{R}^{n\times m}$ represents the observations, missing (or incomplete) correspondence information, sensing matrix, sparse signals, and additive sensing noise, respectively. Different from the previous works on multiple measurements ($m > 1$) which all focus on the sufficient samples regime, namely, $n > p$, we consider a sparse matrix $\mathbf{B}^{\natural}$ and investigate the insufficient samples regime (i.e., $n \ll p$) for the first time. To begin with, we establish the lower bound on the sample number and \emph{signal-to-noise ratio} (${\mathsf{SNR}}$) for the correct permutation recovery. Moreover, we present a simple yet effective estimator. Under mild conditions, we show that our estimator can restore the correct correspondence information with high probability.  Numerical experiments are presented to corroborate our theoretical claims.
Explainability of deep reinforcement learning algorithms in robotic domains by using Layer-wise Relevance Propagation
Authors: Mehran Taghian Jazi; Shotaro Miwa; Yoshihiro Mitsuka; Johannes Günther; Osmar Zaiane
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A key component to the recent success of reinforcement learning is the introduction of neural networks for representation learning. Doing so allows for solving challenging problems in several domains, one of which is robotics. However, a major criticism of deep reinforcement learning (DRL) algorithms is their lack of explainability and interpretability. This problem is even exacerbated in robotics as they oftentimes cohabitate space with humans, making it imperative to be able to reason about their behaviour.
In this paper, we propose to analyze the learned representation in a robotic setting by utilizing graph neural networks. Using the graphical neural networks and Layer-wise Relevance Propagation (LRP), we represent the observations as an entity-relationship to allow us to interpret the learned policy. We evaluate our approach in two environments in MuJoCo. These two environments were delicately designed to effectively measure the value of knowledge gained by our approach to analyzing learned representations. This approach allows us to analyze not only how different parts of the observation space contribute to the decision-making process but also differentiate between policies and their differences in performance. This difference in performance also allows for reasoning about the agent's recovery from faults. These insights are key contributions to explainable deep reinforcement learning in robotic settings.
Towards Fair Classification against Poisoning Attacks
Authors: Han Xu; Xiaorui Liu; Yuxuan Wan; Jiliang Tang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classification can be under the risk of poisoning attacks which deliberately insert malicious training samples to manipulate the trained classifiers' performance. In this work, we study the poisoning scenario where the attacker can insert a small fraction of samples into training data, with arbitrary sensitive attributes as well as other predictive features. We demonstrate that the fairly trained classifiers can be greatly vulnerable to such poisoning attacks, with much worse accuracy & fairness trade-off, even when we apply some of the most effective defenses (originally proposed to defend traditional classification tasks). As countermeasures to defend fair classification tasks, we propose a general and theoretically guaranteed framework which accommodates traditional defense methods to fair classification against poisoning attacks. Through extensive experiments, the results validate that the proposed defense framework obtains better robustness in terms of accuracy and fairness than baseline methods.
Provable Adaptivity in Adam
Authors: Bohan Wang; Yushun Zhang; Huishuai Zhang; Qi Meng; Zhi-Ming Ma; Tie-Yan Liu; Wei Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adaptive Moment Estimation (Adam) has been observed to converge faster than stochastic gradient descent (SGD) in practice. However, such an advantage has not been theoretically characterized -- the existing convergence rate of Adam is no better than SGD. We attribute this mismatch between theory and practice to a commonly used assumption: the gradient is globally Lipschitz continuous (called $L$-smooth condition). Specifically, compared to SGD, Adam adaptively chooses a learning rate better suited to the local gradient Lipschitz constant (called local smoothness). This effect becomes 
prominent when the local smoothness varies drastically across the domain.
In this paper, we analyze the convergence of Adam under a condition called $(L_0,L_1)$-smooth condition, which allows the gradient Lipschitz constant to change with the gradient norm. This condition has been empirically verified to be more realistic for deep neural networks \citep{zhang2019gradient}  than the $L$-smooth condition. Under $(L_0,L_1)$-smooth condition, we establish the convergence for  Adam with practical hyperparameters. As such, we argue that Adam can adapt to this local smoothness condition, justifying Adam's \emph{adaptivity}. In contrast, SGD can be arbitrarily slow under this condition.   Our result can shed light on the benefit of adaptive gradient methods over non-adaptive ones.
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Authors: XIZHE XUE; Dongdong Yu; Lingqiao Liu; Yu Liu; Satoshi Tsutsui; Ying Li; Zehuan Yuan; Ping Song; Mike Zheng Shou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locates the candidate object bounding boxes and then performs instance segmentation. In this work, we instead promote a single-stage framework for OWIS. We argue that the end-to-end training process in the single-stage framework can be more convenient for directly regularizing the localization of class-agnostic object pixels. Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss. We show that such a consistency loss could alleviate the problem of incomplete instance annotation -- a common problem in the existing OWIS datasets. We also show that the proposed loss lends itself to an effective solution to semi-supervised OWIS that could be considered an extreme case that all object annotations are absent for some images. Our extensive experiments demonstrate that the proposed method achieves impressive results in both fully-supervised and semi-supervised settings. Compared to SOTA methods, the proposed method significantly improves the $AP_{100}$ score by 4.75\% in UVO$\rightarrow$UVO setting and 4.05\% in COCO$\rightarrow$UVO setting. In the case of semi-supervised learning, our model learned with only 30\% labeled data, even outperforms its fully-supervised counterpart with 50\% labeled data. The code will be released soon.
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Authors: Gilad Kutiel; Regev Cohen; Michael Elad; Daniel Freedman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms.  In this paper, we introduce a new approach to this problem based on masking.  Given an existing image-to-image network, our approach computes a mask such that the distance between the masked reconstructed image and the masked true image is guaranteed to be less than a specified threshold, with high probability.  The mask thus identifies the more certain regions of the reconstructed image.  Our approach is agnostic to the underlying image-to-image network, and only requires triples of the input (degraded), reconstructed and true images for training.  Furthermore, our method is agnostic to the distance metric used.  As a result, one can use $L_p$-style distances or perceptual distances like LPIPS, which contrasts with interval-based approaches to uncertainty.  Our theoretical guarantees derive from a conformal calibration procedure.  We evaluate our mask-based approach to uncertainty on image colorization, image completion, and super-resolution tasks, demonstrating high quality performance on each.