GenAI4ELab/papercli-papers huggingface dataset:
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
Authors: Jinjin Gu; Xianzheng Ma; Xiangtao Kong; Yu Qiao; Chao Dong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep low-level networks are successful in laboratory benchmarks, but still suffer from severe generalization problems in real-world applications, especially for the deraining task. An ``acknowledgement'' of deep learning drives us to use the training data with higher complexity, expecting the network to learn richer knowledge to overcome generalization problems. Through extensive systematic experiments, we show that this approach fails to improve their generalization ability but instead makes the networks overfit to degradations even more. Our experiments establish that it is capable of training a deraining network with better generalization by reducing the training data complexity. Because the networks are slacking off during training, i.e. learn the less complex element in the image content and degradation to reduce the training loss. When the background image is less complex than the rain streak, the network will focus on the reconstruction of the background without overfitting the rain patterns, thus achieving a good generalization effect. Our research demonstrates excellent application potential and provides an indispensable perspective and research methodology for understanding the generalization problem of low-level vision.
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
Authors: Abulhair Saparov; He He
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these models obtain the answers and whether they rely on simple heuristics rather than the generated chain-of-thought. To enable systematic exploration of the reasoning ability of LLMs, we present a new synthetic question-answering dataset called PrOntoQA, where each example is generated from a synthetic world model represented in first-order logic. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis. Our analysis on InstructGPT and GPT-3 shows that LLMs are quite capable of making correct individual deduction steps, and so are generally capable of reasoning, even in fictional contexts. However, they have difficulty with proof planning: When multiple valid deduction steps are available, they are not able to systematically explore the different options.
Collaborative Symmetricity Exploitation for Offline Learning of Hardware Design Solver
Authors: Haeyeon Kim; Minsu Kim; Joungho Kim; Jinkyoo Park
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper proposes \textit{collaborative symmetricity exploitation} (CSE) framework to train a solver for the decoupling capacitor placement problem (DPP) benchmark, one of the significant hardware design problems. Due to the sequentially coupled multi-level property of the hardware design process, the design condition of DPP changes depending on the design of higher-level problems. Also, the online evaluation of real-world electrical performance through simulation is extremely costly. Thus, a data-efficient offline learning method to train a solver (i.e., contextualized policy) with high generalization capability over changing task conditions is necessary. In this paper, we apply the CSE framework to train a DPP solver using a limited number of offline expert data. Leveraging the symmetricity for offline learning of hardware design solver has two major advantages: it increases data-efficiency by reducing the solution space and improves generalization capability by capturing the invariant nature present regardless of changing conditions. The proposed CSE is composed of two learning schemes: expert exploitation and self-exploitation. Expert exploitation induces symmetricity during the imitation learning process with offline expert data and self-exploitation induces symmetricity during the consistency learning process with self-generated data. Extensive experiments verified that CSE with zero-shot inference outperforms the neural baselines and iterative conventional design methods on the DPP benchmark. Furthermore, CSE showed promising extrapolation capability as it greatly outperforms the expert method used to generate the offline data for training. Scalability and flexibility of the proposed method were also verified for practical use of CSE in industry.
Post-mortem on a deep learning contest: a Simpson’s paradox and the complementary roles of scale metrics versus shape metrics
Authors: Michael W. Mahoney; charles h martin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: To understand better good generalization performance in state-of-the-art neural network (NN) models, and in particular the success of the AlphaHat metric based on Heavy-Tailed Self-Regularization (HT-SR) theory, we analyze of a corpus of models that was made publicly-available for a contest to predict the generalization accuracy of NNs. These models include a wide range of qualities and were trained with a range of architectures and regularization hyperparameters. We break AlphaHat into its two subcomponent metrics: a scale-based metric; and a shape-based metric. We identify what amounts to a Simpson’s paradox: where “scale” metrics (from traditional statistical learning theory) perform well in aggregate, but can perform poorly on subpartitions of the data of a given depth, when regularization hyperparameters are varied; and where “shape” metrics (from HT-SR theory) perform well on each subpartition of the data, when hyperparameters are varied for models of a given depth, but can perform poorly overall when models with varying depths are aggregated. Our results highlight the subtlety of comparing models when both architectures and hyperparameters are varied; the complementary role of implicit scale versus implicit shape parameters in understanding NN model quality; and the need to go beyond one-size-fits-all metrics based on upper bounds from generalization theory to describe the performance of NN models. Our results also clarify further why the AlphaHat metric from HT-SR theory works so well at predicting generalization across a broad range of CV and NLP models.
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
Authors: Yujia Zheng; Ignavier Ng; Yewen Fan; Kun Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables. Existing work focuses on specific families of distributions (e.g., exponential families) and/or certain structures of graphs, and most of them can only handle variables of a single data type (continuous or discrete). In this work, we characterize the conditional independence structure in general distributions for all data types (i.e., continuous, discrete, and mixed-type) with a Generalized Precision Matrix (GPM). Besides, we also allow general functional relations among variables, thus giving rise to a Markov network structure learning algorithm in one of the most general settings. To deal with the computational challenge of the problem, especially for large graphs, we unify all cases under the same umbrella of a regularized score matching framework. We validate the theoretical results and demonstrate the scalability empirically in various settings.
Matching receptor to odorant with protein language and graph neural networks
Authors: Matej Hladiš; Maxence Lalis; Sebastien Fiorucci; Jérémie Topin
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Odor perception in mammals is triggered by interactions between volatile organic compounds and a subset of hundreds of proteins called olfactory receptors (ORs). Molecules activate these receptors in a complex combinatorial coding allowing mammals to discriminate a vast number of chemical stimuli. Recently, ORs have gained attention as new therapeutic targets following the discovery of their involvement in other physiological processes and diseases. To date, predicting molecule-induced activation for ORs is highly challenging since $43\%$ of ORs have no identified active compound. In this work, we combine [CLS] token from protBERT with a molecular graph and propose a tailored GNN architecture incorporating inductive biases from the protein-molecule binding. We abstract the biological process of protein-molecule activation as the injection of a molecule into a protein-specific environment. On a newly gathered dataset of $46$ $700$ OR-molecule pairs, this model outperforms state-of-the-art models on drug-target interaction prediction as well as standard GNN baselines. Moreover, by incorporating non-bonded interactions the model is able to work with mixtures of compounds. Finally, our predictions reveal a similar activation pattern for molecules within a given odor family, which is in agreement with the theory of combinatorial coding in olfaction.
DYNAMIC BATCH NORM STATISTICS UPDATE FOR NATURAL ROBUSTNESS
Authors: Shahbaz Rezaei; Mohammad Sadegh Norouzzadeh
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: DNNs trained on natural clean samples have been shown to perform poorly on corrupted samples,
such as noisy or blurry images. Various data augmentation methods have been recently proposed
to improve DNN’s robustness against common corruptions. Despite their success, they require
computationally expensive training and cannot be applied to off-the-shelf trained models. Recently,
updating only BatchNorm(BN) statistics of a model on a single corruption has been shown to improve
its accuracy on that corruption significantly. However, adopting the idea at inference time when the
type of corruption changes decreases the effectiveness of this method. In this paper, we harness the
Fourier domain to detect the corruption type, a challenging task in the image domain. We propose
a unified framework consisting of a corruption-detection model and BN statistics update that can
improve the corruption accuracy of any off-the-shelf trained model. We benchmark our framework
on different models and datasets. Our results demonstrate about 8% and 4% accuracy improvement
on CIFAR10-C and ImageNet-C, respectively. Furthermore, our framework can further improve the
accuracy of state-of-the-art robust models, such as AugMix and DeepAug.
When to Make and Break Commitments?
Authors: Alihan Hüyük; Zhaozhi Qian; Mihaela van der Schaar
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In many scenarios, decision-makers must commit to long-term actions until their resolution before receiving the payoff of said actions, and usually, staying committed to such actions incurs continual costs. For instance, in healthcare, a newly-discovered treatment cannot be marketed to patients until a clinical trial is conducted, which both requires time and is also costly. Of course in such scenarios, not all commitments eventually pay off. For instance, a clinical trial might end up failing to show efficacy. Given the time pressure created by the continual cost of keeping a commitment, we aim to answer: When should a decision-maker break a commitment that is likely to fail—either to make an alternative commitment or to make no further commitments at all? First, we formulate this question as a new type of optimal stopping/switching problem called the optimal commitment problem (OCP). Then, we theoretically analyze OCP, and based on the insights we gain, propose a practical algorithm for solving it. Finally, we empirically evaluate the performance of our algorithm in running clinical trials with subpopulation selection.
D-CIPHER: Discovery of Closed-form Partial Differential Equations
Authors: Krzysztof Kacprzyk; Zhaozhi Qian; Mihaela van der Schaar
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these equations directly from data is challenging because it requires modeling relationships between various derivatives that are not observed in the data (equation-data mismatch) and it involves searching across a huge space of possible equations. Current approaches make strong assumptions about the form of the equation and thus fail to discover many well-known systems. Moreover, many of them resolve the equation-data mismatch by estimating the derivatives, which makes them inadequate for noisy and infrequently sampled systems. To this end, we propose D-CIPHER, which is robust to measurement artifacts and can uncover a new and very general class of differential equations. We further design a novel optimization procedure, CoLLie, to help D-CIPHER search through this class efficiently. Finally, we demonstrate empirically that it can discover many well-known equations that are beyond the capabilities of current methods.
Property Inference Attacks Against t-SNE Plots
Authors: Jingcan Chen; Xinlei He; Boyang Zhang; Yang Chen; Yang Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: With the prevailing of machine learning (ML), researchers have shown that ML models are also vulnerable to various privacy and security attacks. As one of the representative attacks, the property inference attack aims to infer the private/sensitive properties of the training data (e.g., race distribution) given the output of ML models. In this paper, we present a new side channel for property inference attacks, i.e., t-SNE plots, which are widely used to show feature distribution or demonstrate model performance. We show for the first time that the private/sensitive properties of the data that are used to generate the plot can be successfully predicted. Briefly, we leverage the publicly available model as the shadow model to generate t-SNE plots with different properties. We use those plots to train an attack model, which is a simple image classifier, to infer the specific property of a given t-SNE plot. Extensive evaluation on four datasets shows that our proposed attack can effectively infer the undisclosed property of the data presented in the t-SNE plots, even when the shadow model is different from the target model used to generate the t-SNE plots. We also reveal that the attacks are robust in various scenarios, such as constructing the attack with fewer t-SNE plots/different density settings and attacking t-SNE plots generated by fine-tuned target models. The simplicity of our attack method indicates that the potential risk of leaking sensitive properties in t-SNE plots is largely underestimated. As possible defenses, we observe that adding noise to the image embeddings or t-SNE coordinates effectively mitigates attacks but can be bypassed by adaptive attacks, which prompts the need for more effective defenses.
MARLlib: Extending RLlib for Multi-agent Reinforcement Learning
Authors: Siyi Hu; Yifan Zhong; Minquan Gao; Weixun Wang; Hao Dong; Zhihui Li; Xiaodan Liang; Yaodong Yang; Xiaojun Chang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite the fast development of multi-agent reinforcement learning (MARL) methods, there is a lack of commonly-acknowledged baseline implementation and evaluation platforms. As a result, an urgent need for MARL researchers is to develop an integrated library suite, similar to the role of RLlib in single-agent RL, that delivers reliable MARL implementation and replicable evaluation in various bechmarks. To fill such a research gap, in this paper, we propose Multi-Agent RLlib (MARLlib), a comprehensive MARL algorithm library that facilitates RLlib for solving multi-agent problems. With a novel design of agent-level distributed dataflow, MARLlib manages to unify tens of algorithms, including different types of independent learning, centralized critic, and value decomposition methods; this leads to a highly composable integration of MARL algorithms that are not possible to unify before. Furthermore, MARLlib goes beyond current work by integrating diverse environment interfaces and providing flexible parameter sharing strategies; this allows to create versatile solutions to  cooperative, competitive, and mixed tasks with minimal code modifications for end users. A plethora of experiments  are conducted to substantiate the correctness of our implementation, based on which we further derive new insights on the relationship between the  performance and the design of algorithmic components. With MARLlib, we expect researchers to be able to tackle broader real-world multi-agent problems with trustworthy solutions. Our code\footnote{\url{https://github.com/ICLR2023Paper4242/MARLlib}} and documentation\footnote{\url{https://iclr2023marllib.readthedocs.io/}} are released for reference.
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
Authors: Yiming Gao; Feiyu Liu; Liang Wang; Zhenjie Lian; Weixuan Wang; Siqin Li; Xianliang Wang; Xianhan Zeng; Rundong Wang; jiawei wang; QIANG FU; Yang Wei; Lanxiao Huang; Wei Liu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far. However, these AI systems mainly focus on how to compete with humans, less on exploring how to collaborate with humans. To this end, this paper makes the first attempt to investigate human-agent collaboration in MOBA games. In this paper, we propose to enable humans and agents to collaborate through explicit communication by designing an efficient and interpretable Meta-Command Communication-based framework, dubbed MCC, for accomplishing effective human-agent collaboration in MOBA games. The MCC framework consists of two pivotal modules: 1) an interpretable communication protocol, i.e., the Meta-Command, to bridge the communication gap between humans and agents; 2) a meta-command value estimator, i.e., the Meta-Command Selector, to select a valuable meta-command for each agent to achieve effective human-agent collaboration. Experimental results in Honor of Kings demonstrate that MCC agents can collaborate reasonably well with human teammates and even generalize to collaborate with different levels and numbers of human teammates. Videos are available at https://sites.google.com/view/mcc-demo.
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning
Authors: Tung Nguyen; Qinqing Zheng; Aditya Grover
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The goal of offline reinforcement learning (RL) is to learn near-optimal policies from static logged datasets, thus sidestepping expensive online interactions. Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances~\cite{chen2021decision, janner2021offline, emmons2021rvs} have shown that by conditioning on desired future returns, BC can perform competitively to their value-based counterparts, while enjoying much more simplicity and training stability. However, the distribution of returns in the offline dataset can be arbitrarily skewed and suboptimal, which poses a unique challenge for conditioning BC on expert returns at test-time. We propose ConserWeightive Behavioral Cloning (\name), a simple and effective method for improving the performance of conditional BC for offline RL with two key components: trajectory weighting and conservative regularization. Trajectory weighting addresses the bias-variance tradeoff in conditional BC and provides a principled mechanism to learn from both low return trajectories (typically plentiful) and high return trajectories (typically few). Further, we analyze the notion of conservatism in existing BC methods, and propose a novel conservative regularizer that explicitly encourages the policy to stay close to the data distribution. The regularizer helps achieve more reliable performance, and removes the need for ad-hoc tuning of the conditioning value during evaluation. We instantiate \name{} in the context of Reinforcement Learning via Supervised Learning (RvS)~\cite{emmons2021rvs} and Decision Transformer (DT)~\citep{chen2021decision}, and empirically show that it significantly boosts the performance and stability of prior methods on various offline RL benchmarks.
Learning on Large-scale Text-attributed Graphs via Variational Inference
Authors: Jianan Zhao; Meng Qu; Chaozhuo Li; Hao Yan; Qian Liu; Rui Li; Xing Xie; Jian Tang
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and  GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM. Instead of simultaneously training large language models and GNNs on big graphs, GLEM proposes to alternatively update the two modules in the E-step and M-step. Such a procedure allows training the two modules separately while simultaneously allowing the two modules to interact and mutually enhance each other. Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach.
Learning Fast and Slow for Online Time Series Forecasting
Authors: Quang Pham; Chenghao Liu; Doyen Sahoo; Steven Hoi
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite the recent success of deep learning for time series forecasting, these methods are not scalable for many real-world applications where data arrives sequentially. Training deep neural forecasters on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and remember old knowledge. We argue that the fast adaptation capability of deep neural networks is critical and successful solutions require handling changes to both new and recurring patterns effectively. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning Network (FSNet) as a novel framework to address the challenges of online forecasting. Particularly, FSNet improves the slowly-learned backbone by dynamically balancing fast adaptation to recent changes and retrieving similar old knowledge. FSNet achieves this mechanism via an interaction between two novel complementary components: (i) a per-layer adapter to support fast learning from individual layers, and (ii) an associative memory to support remembering, updating, and recalling repeating events. Extensive experiments on real and synthetic datasets validate FSNet's efficacy and robustness to both new and recurring patterns. Our code is publicly available at: \url{https://github.com/salesforce/fsnet/}.