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This paper presents a multi-task learning method to fine-tune pre-trained LMs (e.g., BERT, BART) and evaluate on in-distribution and out-of-distribution data. In addition to the MLM loss, the proposed training process also applies a mask LM to replace randomly sampled words in an input sentence and uses the sentence's ...	Recommendation: 3: reject, not good enough	Area: Applications (eg, speech processing, computer vision, NLP)	Review: This paper presents a multi-task learning method to fine-tune pre-trained LMs (e.g., BERT, BART) and evaluate on in-distribution and out-of-distribution data. In addition to the MLM loss, the proposed training process also applies a mask LM to replace randomly sampled words in an input sentence and uses the sen...
This paper studies deployment-efficient reward-free exploration problems in the linear MDP setting. The authors propose an algorithm that can achieve optimal deployment efficiency. Strength: The theoretical results seem to be sound although I did not check all the details. The writing is clear and the discussion about...	Recommendation: 6: marginally above the acceptance threshold	Area: Theory (eg, control theory, learning theory, algorithmic game theory)	Review: This paper studies deployment-efficient reward-free exploration problems in the linear MDP setting. The authors propose an algorithm that can achieve optimal deployment efficiency. Strength: The theoretical results seem to be sound although I did not check all the details. The writing is clear and the discussi...
This work developed a novel meta-learnt method to transfer learning knowledge between neural operators. Different from typical final-layer transfer in existing meta-learning methods, this paper adapted the first layer of the neural operator model to capture hidden parameter field. Evaluation on both synthetic and real ...	Recommendation: 6: marginally above the acceptance threshold	Area: Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability )	Review: This work developed a novel meta-learnt method to transfer learning knowledge between neural operators. Different from typical final-layer transfer in existing meta-learning methods, this paper adapted the first layer of the neural operator model to capture hidden parameter field. Evaluation on both synthetic a...
High level motivation: While the multimodality of human perception is shown to help with analogical reasoning, there isn't a standardized task adopted by the machine learning community to drive progress in this area. Contributions: * New task and dataset: The authors propose a new multimodal analogical re...	Recommendation: 8: accept, good paper	Area: Applications (eg, speech processing, computer vision, NLP)	Review: High level motivation: While the multimodality of human perception is shown to help with analogical reasoning, there isn't a standardized task adopted by the machine learning community to drive progress in this area. Contributions: * New task and dataset: The authors propose a new multimodal analo...
This paper defines a new task: matching unbalanced points cloud pairs in terms of spatial extent and density. Though the task is somewhat similar with registering low-overlap point cloud pairs for spatial extent, this paper adds a factor - density. This paper proposes three steps/modules to solve the problem: 1) Subm...	Recommendation: 6: marginally above the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: This paper defines a new task: matching unbalanced points cloud pairs in terms of spatial extent and density. Though the task is somewhat similar with registering low-overlap point cloud pairs for spatial extent, this paper adds a factor - density. This paper proposes three steps/modules to solve the problem:...
The paper proposes a novel design of physical loss to find a trade-off between sample density and solution accuracy of PDEs. The formulation of the proposed loss tackles the challenges of approximating the boundary layer by modeling and adding a term in the loss function that takes into consideration the highly complex...	Recommendation: 5: marginally below the acceptance threshold	Area: Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability )	Review: The paper proposes a novel design of physical loss to find a trade-off between sample density and solution accuracy of PDEs. The formulation of the proposed loss tackles the challenges of approximating the boundary layer by modeling and adding a term in the loss function that takes into consideration the highly...
This paper proposes a new benchmark MBXP for 10+ programming languages to facilitate the evaluation of execution-based code completion task. By the design of the conversion framework to translate prompts and test cases from the MBPP dataset, MBXP can evaluate code generation models in a multi-lingual manner. Furthermor...	Recommendation: 6: marginally above the acceptance threshold	Area: Deep Learning and representational learning	Review: This paper proposes a new benchmark MBXP for 10+ programming languages to facilitate the evaluation of execution-based code completion task. By the design of the conversion framework to translate prompts and test cases from the MBPP dataset, MBXP can evaluate code generation models in a multi-lingual manner. Fu...
The paper introduces introduces a technique to mitigate indirect bias. To use an example from the paper, after debiasing sentences [ "he is a handsome engineer", "she is a sensitive engineer" ] and removing the gendered pronoun, the indirect biases ("handsome engineer" and "sensitive engineer") still persist. Empirical...	Recommendation: 5: marginally below the acceptance threshold	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: The paper introduces introduces a technique to mitigate indirect bias. To use an example from the paper, after debiasing sentences [ "he is a handsome engineer", "she is a sensitive engineer" ] and removing the gendered pronoun, the indirect biases ("handsome engineer" and "sensitive engineer") still persist. E...
The paper proposes a framework that combines expert knowledge and AI for improved experimental design. The principle is to let humans drive the discovery/optimization process while the AI provides additional data to overcome the strong exploitation bias of humans. The AI model consists of a GP that is fit using data fr...	Recommendation: 6: marginally above the acceptance threshold	Area: Optimization (eg, convex and non-convex optimization)	Review: The paper proposes a framework that combines expert knowledge and AI for improved experimental design. The principle is to let humans drive the discovery/optimization process while the AI provides additional data to overcome the strong exploitation bias of humans. The AI model consists of a GP that is fit using...
This paper addresses task-specific diversity problem, which exhibits in policies with similar expected return (what the authors called quality). The task specificity is framed by a set of scalar functions of a trajectory defined by the user, which are called Behavior Descriptors (BDs). A BD captures some property of a ...	Recommendation: 6: marginally above the acceptance threshold	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: This paper addresses task-specific diversity problem, which exhibits in policies with similar expected return (what the authors called quality). The task specificity is framed by a set of scalar functions of a trajectory defined by the user, which are called Behavior Descriptors (BDs). A BD captures some proper...
This paper presents a theoretical and empirical analysis with the main goal showing the similarity between two main categories of algorithms, one-step methods and critic regularization-based methods. Specifically, the paper shows that, under some conditions, the two types of methods are theoretically equivalent; it als...	Recommendation: 5: marginally below the acceptance threshold	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: This paper presents a theoretical and empirical analysis with the main goal showing the similarity between two main categories of algorithms, one-step methods and critic regularization-based methods. Specifically, the paper shows that, under some conditions, the two types of methods are theoretically equivalent...
This paper introduces a way to create a concept bottleneck paper without having to provide the concept datasets (which is usually user-specified) (this can be seen as a bug or a feature in the community). The way they achieve this is to first use GPT-3 to query related concepts given an output class. Using these GPT-cr...	Recommendation: 8: accept, good paper	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: This paper introduces a way to create a concept bottleneck paper without having to provide the concept datasets (which is usually user-specified) (this can be seen as a bug or a feature in the community). The way they achieve this is to first use GPT-3 to query related concepts given an output class. Using thes...
The paper propose a federated recommender system which guarantees fairness and communication efficiency while trying to minimize the accuracy drop. Strength: 1. The authors derive the first sample complexity for federated recommender, despite under strong assumptions. 2. The authors evaluate the proposed the method on ...	Recommendation: 5: marginally below the acceptance threshold	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: The paper propose a federated recommender system which guarantees fairness and communication efficiency while trying to minimize the accuracy drop. Strength: 1. The authors derive the first sample complexity for federated recommender, despite under strong assumptions. 2. The authors evaluate the proposed the me...
This paper shows that under suitable assumptions, the Bayesian optimal robust estimator requires test-time adaptation, and such adaptation can lead to a significant performance boost over standard adversarial training. It then proposes self-supervised test-time fine-tuning on adversarially-trained models to improve the...	Recommendation: 6: marginally above the acceptance threshold	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: This paper shows that under suitable assumptions, the Bayesian optimal robust estimator requires test-time adaptation, and such adaptation can lead to a significant performance boost over standard adversarial training. It then proposes self-supervised test-time fine-tuning on adversarially-trained models to imp...
The paper evaluates a set of known model-theft attacks to vision and NLP transformers. For vision, adaptation of MixMatch [Berthelot et al., 2019] to semi-supervised scenarios is the key novelty. Then the authors adapt the Dataset Inference (DI) [Dziedzic et. al. 2022b] technique for transformer models that get trained...	Recommendation: 3: reject, not good enough	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: The paper evaluates a set of known model-theft attacks to vision and NLP transformers. For vision, adaptation of MixMatch [Berthelot et al., 2019] to semi-supervised scenarios is the key novelty. Then the authors adapt the Dataset Inference (DI) [Dziedzic et. al. 2022b] technique for transformer models that get...
The paper proposes a novel setting for anomaly detection where the visual data also has a set of attributes that describe the environmental parameters. The proposed method focuses on a setting where the user can define one or more nuisance attributes such that the algorithm should ignore the impact of nuisance attribut...	Recommendation: 6: marginally above the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: The paper proposes a novel setting for anomaly detection where the visual data also has a set of attributes that describe the environmental parameters. The proposed method focuses on a setting where the user can define one or more nuisance attributes such that the algorithm should ignore the impact of nuisance ...
This paper introduces a method to learn invariant listwise representations for ranking. A theoretical generalization bound is presented by analyzing domain adaptation for learning to listwise rank. Experimental results demonstrate the effectiveness of the proposed method on unsupervised domain adaptation for passage re...	Recommendation: 5: marginally below the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: This paper introduces a method to learn invariant listwise representations for ranking. A theoretical generalization bound is presented by analyzing domain adaptation for learning to listwise rank. Experimental results demonstrate the effectiveness of the proposed method on unsupervised domain adaptation for pa...
The authors proposed a new modal for training single-spike SNNs, which offers a 13.98x training speedup compared to a multi-spike counterpart. The speedup is achieved by avoiding all sequential dependence on time and exclusively relies on GPU parallelizable non-sequential operations. The effectiveness of the proposed m...	Recommendation: 5: marginally below the acceptance threshold	Area: General Machine Learning	Review: The authors proposed a new modal for training single-spike SNNs, which offers a 13.98x training speedup compared to a multi-spike counterpart. The speedup is achieved by avoiding all sequential dependence on time and exclusively relies on GPU parallelizable non-sequential operations. The effectiveness of the pr...
The paper proposes QAID, Question Answering inspired Intent Detection system, which models the intention detection classification as a question-answering task. The model uses two stages of training: a pretraining for better query representation and finetuning on few-shot labels of query and answers (name of intents). C...	Recommendation: 6: marginally above the acceptance threshold	Area: Deep Learning and representational learning	Review: The paper proposes QAID, Question Answering inspired Intent Detection system, which models the intention detection classification as a question-answering task. The model uses two stages of training: a pretraining for better query representation and finetuning on few-shot labels of query and answers (name of int...
This paper studies how to approximate Lipschitz functions in high dimensions for signals with low-dimensional structures. By assuming the existence of a linear Johnson-Linderstrauss embedding on the signals, the appropriation bounds of Lipschitz functions based on neural networks are given. It allows for better explana...	Recommendation: 6: marginally above the acceptance threshold	Area: Deep Learning and representational learning	Review: This paper studies how to approximate Lipschitz functions in high dimensions for signals with low-dimensional structures. By assuming the existence of a linear Johnson-Linderstrauss embedding on the signals, the appropriation bounds of Lipschitz functions based on neural networks are given. It allows for better...
The paper presents a method for learning a GAN for tabular data that performs conditional generation with semantic constraints for the structural properties in the tabular dataset. This model is used to generate perturbed samples that are, it is argued, intuitively performing a similar role to the perturbations in dist...	Recommendation: 3: reject, not good enough	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: The paper presents a method for learning a GAN for tabular data that performs conditional generation with semantic constraints for the structural properties in the tabular dataset. This model is used to generate perturbed samples that are, it is argued, intuitively performing a similar role to the perturbations...
The authors propose a benchmark to solely focus on benchmarking Deep Reinforcement Learning agents' memory and ability to generalise. In addition to open source accessibility, simulation step speed and flexibility (level hardness, noise etc) the authors claim their benchmark is better suited to distinctly evaluate the ...	Recommendation: 5: marginally below the acceptance threshold	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: The authors propose a benchmark to solely focus on benchmarking Deep Reinforcement Learning agents' memory and ability to generalise. In addition to open source accessibility, simulation step speed and flexibility (level hardness, noise etc) the authors claim their benchmark is better suited to distinctly evalu...
This paper has reformulated soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), and proposed a unified theoretical framework for soft threshold pruning. Experiments have shown that the proposed method outperforms SOTA soft threshold pruning algo...	Recommendation: 6: marginally above the acceptance threshold	Area: Deep Learning and representational learning	Review: This paper has reformulated soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), and proposed a unified theoretical framework for soft threshold pruning. Experiments have shown that the proposed method outperforms SOTA soft threshold prun...
This paper illustrates the desired IRM solution $f_{\text{IRM}}$ does not belong to the Pareto front of the IRMv1/IRMs loss included MOO problem in the environment (Kamath et al., 2021) described. This paper includes the VERx objective by (Krueger et al., 2021) into the MOO problem (IRMX) and shows in Proposition 1 tha...	Recommendation: 5: marginally below the acceptance threshold	Area: Deep Learning and representational learning	Review: This paper illustrates the desired IRM solution $f_{\text{IRM}}$ does not belong to the Pareto front of the IRMv1/IRMs loss included MOO problem in the environment (Kamath et al., 2021) described. This paper includes the VERx objective by (Krueger et al., 2021) into the MOO problem (IRMX) and shows in Propositi...
This paper raises a bar against an adversary who aims to reverse-engineer efficient models deployed to edge devices. A naive deployment practice allows the attacker to just extract the model's architecture and parameters which work as a prior for other attacks (such as adversarial attacks). To address this issue, the p...	Recommendation: 5: marginally below the acceptance threshold	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: This paper raises a bar against an adversary who aims to reverse-engineer efficient models deployed to edge devices. A naive deployment practice allows the attacker to just extract the model's architecture and parameters which work as a prior for other attacks (such as adversarial attacks). To address this issu...
The authors utilize the pre-trained conditional generation model as a data augmentation technique to facilitate transfer learning. Extensive experience done prove the effectiveness of the proposed method. Strength: Based on my knowledge, the authors are the first to apply the pre-trained GAN to transfer learning. It's ...	Recommendation: 6: marginally above the acceptance threshold	Area: Deep Learning and representational learning	Review: The authors utilize the pre-trained conditional generation model as a data augmentation technique to facilitate transfer learning. Extensive experience done prove the effectiveness of the proposed method. Strength: Based on my knowledge, the authors are the first to apply the pre-trained GAN to transfer learnin...
In this paper, the authors provide new differentially private (DP) mechanisms for gradient-based machine learning (ML) training involving multiple passes (epochs) of a dataset, substantially improving the achievable privacy-utility-computation tradeoff. They propose a framework for computing the sensitivity of matrix m...	Recommendation: 5: marginally below the acceptance threshold	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: In this paper, the authors provide new differentially private (DP) mechanisms for gradient-based machine learning (ML) training involving multiple passes (epochs) of a dataset, substantially improving the achievable privacy-utility-computation tradeoff. They propose a framework for computing the sensitivity of ...
Existing exploration methods tend to explore at the frontier of the currently seen states. The authors of this paper propose to direct the goal-conditioned policy in a way that induces higher exploration value trajectories for the agent. Specifically they first use the goal-conditioned policy $\pi^G$ to reach a such a ...	Recommendation: 8: accept, good paper	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: Existing exploration methods tend to explore at the frontier of the currently seen states. The authors of this paper propose to direct the goal-conditioned policy in a way that induces higher exploration value trajectories for the agent. Specifically they first use the goal-conditioned policy $\pi^G$ to reach a...
The paper introduces a method for seq2seq tasks which incorporates the target sequence itself to learn a latent variable that informs the target prediction. This is done by using an EM-network that conditions on the target, with cross-attention to the regular seq2seq encoder representations, yielding the latent represe...	Recommendation: 6: marginally above the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: The paper introduces a method for seq2seq tasks which incorporates the target sequence itself to learn a latent variable that informs the target prediction. This is done by using an EM-network that conditions on the target, with cross-attention to the regular seq2seq encoder representations, yielding the latent...
This paper addresses the problem of systematic generalization in VQA. It proposes a new model that takes advantage of the learning capability of Transformers and the compositional modeling of Module Networks. The proposed model achieves state-of-the-art performance on three different VQA datasets including standard tes...	Recommendation: 5: marginally below the acceptance threshold	Area: Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)	Review: This paper addresses the problem of systematic generalization in VQA. It proposes a new model that takes advantage of the learning capability of Transformers and the compositional modeling of Module Networks. The proposed model achieves state-of-the-art performance on three different VQA datasets including stan...
This paper starts from a PCN and expands around the limit where the output is not tethered to the target. Perturbing in the parameter $\lambda$, which may be interpreted as the precision parameter deciding how tightly would the target be tied to the output, builds a useful bridge to many other approaches to training ne...	Recommendation: 8: accept, good paper	Area: Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)	Review: This paper starts from a PCN and expands around the limit where the output is not tethered to the target. Perturbing in the parameter $\lambda$, which may be interpreted as the precision parameter deciding how tightly would the target be tied to the output, builds a useful bridge to many other approaches to tra...
The paper proposes a generative framework for synthesizing multi-view consistent portrait videos. Towards this, the authors employed EG3D architecture in NeRF and made the network conditioned on motion features. 2 discriminators are used to maintain spatial and temporal consistency. While it’s claimed in the paper that...	Recommendation: 8: accept, good paper	Area: Generative models	Review: The paper proposes a generative framework for synthesizing multi-view consistent portrait videos. Towards this, the authors employed EG3D architecture in NeRF and made the network conditioned on motion features. 2 discriminators are used to maintain spatial and temporal consistency. While it’s claimed in the pa...
This paper analyzes the ability to identify the source model that generated the audio files. The authors explore generating samples from seven models different models. Specifically, the authors trained a classification network to distinguish between the samples. Results suggest that such classifiers can almost perfectl...	Recommendation: 3: reject, not good enough	Area: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)	Review: This paper analyzes the ability to identify the source model that generated the audio files. The authors explore generating samples from seven models different models. Specifically, the authors trained a classification network to distinguish between the samples. Results suggest that such classifiers can almost ...
The paper studies federated learning with heterogeneous label noise, and they propose a dual structure to solve the problem: they introduce a local and personalized denoising model to each client, which helps the global model select filter noisy samples, and then update. The paper also provides some favorable experimen...	Recommendation: 5: marginally below the acceptance threshold	Area: General Machine Learning	Review: The paper studies federated learning with heterogeneous label noise, and they propose a dual structure to solve the problem: they introduce a local and personalized denoising model to each client, which helps the global model select filter noisy samples, and then update. The paper also provides some favorable e...
As the title suggests, this work studies bags of tricks for FGSM AT. Strength 1: This work performs a comprehensive analysis on FGSM AT Strength 2: Competitive performance. Weakness1: The main concern is that this work mainly relies on empirical results on showing effectiveness. For example, it is fully unclear why ...	Recommendation: 5: marginally below the acceptance threshold	Area: Deep Learning and representational learning	Review: As the title suggests, this work studies bags of tricks for FGSM AT. Strength 1: This work performs a comprehensive analysis on FGSM AT Strength 2: Competitive performance. Weakness1: The main concern is that this work mainly relies on empirical results on showing effectiveness. For example, it is fully uncl...
This paper presents a benchmark and a set of studies about the sensitivity of trajectory prediction models to the removal of non-causal agents. The authors had human labelers label the agents that influence the ego behavior in the WOMD validation set. They then used those labels to perturb the dataset. They considered...	Recommendation: 5: marginally below the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: This paper presents a benchmark and a set of studies about the sensitivity of trajectory prediction models to the removal of non-causal agents. The authors had human labelers label the agents that influence the ego behavior in the WOMD validation set. They then used those labels to perturb the dataset. They co...
This paper studied the generalization problem in RL and used Bayesian regret as a performance measure. The authors considered if the agent is allowed to use the data from interaction with the testing environment. I feel this is an interesting paper. Considering testing time interaction is the right way to think about...	Recommendation: 5: marginally below the acceptance threshold	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: This paper studied the generalization problem in RL and used Bayesian regret as a performance measure. The authors considered if the agent is allowed to use the data from interaction with the testing environment. I feel this is an interesting paper. Considering testing time interaction is the right way to thi...
The paper proposes an image-goal navigation (ImageNav) method on top of VGM (Kwon et al. 2021) which builds a topological graph. The proposed method, MemoNav considers VGM as the short-term memory (STM) and removes some nodes based on thresholding on attention score (selective forgetting). The modified STMs are fed int...	Recommendation: 6: marginally above the acceptance threshold	Area: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)	Review: The paper proposes an image-goal navigation (ImageNav) method on top of VGM (Kwon et al. 2021) which builds a topological graph. The proposed method, MemoNav considers VGM as the short-term memory (STM) and removes some nodes based on thresholding on attention score (selective forgetting). The modified STMs are...
The paper leads a discussion on multi-vector document retrieval by introducing a sparse alignment perspective that can include serval previous document retrieval scheme. On top of that, the paper proposes an entropy-regularized sparse relaxation of the novel alignment scheme by separating the query-document token-wise ...	Recommendation: 6: marginally above the acceptance threshold	Area: Applications (eg, speech processing, computer vision, NLP)	Review: The paper leads a discussion on multi-vector document retrieval by introducing a sparse alignment perspective that can include serval previous document retrieval scheme. On top of that, the paper proposes an entropy-regularized sparse relaxation of the novel alignment scheme by separating the query-document tok...
The paper studies scaling of performance of deep learning-based methods for different image reconstruction tasks with respect to available training set sizes. Tasks investigated are U-net and SwinIR transformer for image denoising (and super-resolution in the appendix), as well as U-net for MRI compressive sensing. Exp...	Recommendation: 8: accept, good paper	Area: Deep Learning and representational learning	Review: The paper studies scaling of performance of deep learning-based methods for different image reconstruction tasks with respect to available training set sizes. Tasks investigated are U-net and SwinIR transformer for image denoising (and super-resolution in the appendix), as well as U-net for MRI compressive sens...