GenAI4ELab/papercli-papers huggingface dataset:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair
Authors: Keller Jordan; Hanie Sedghi; Olga Saukh; Rahim Entezari; Behnam Neyshabur
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this paper we empirically investigate the conjecture from Entezari et al. (2021) which states that if permutation invariance is taken into account, then there should be no loss barrier to the linear interpolation between SGD solutions. We conduct our investigation using standard computer vision architectures trained on CIFAR-10 and ImageNet.  First, we observe a general phenomenon in which interpolated deep networks suffer a collapse in the variance of their activations. We demonstrate that an appropriate rescaling of the pre-activations of the interpolated networks ameliorates this problem and significantly reduces the barrier. Second, by combining this with an algorithm for finding permutations based on maximizing correlations between the activations of matched neurons, we are able to reduce the interpolation barrier for a standard ResNet18 trained on CIFAR-10 to 1.5% absolute test error. We explore the interaction between our method and the choice of normalization layer, and demonstrate its robustness across a variety of architectures and training sets.
Pseudometric guided online query and update for offline reinforcement learning
Authors: Haoran Li; Yang Weng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Offline Reinforcement Learning (RL) extracts effective policies from historical data without the need to interact with the environment. However, the learned policy often suffers large generalization errors in the online environment due to the distributional shift. While existing work mostly focuses on learning a generalizable policy, we propose to adapt the learned policy to fit the online environment with limited queries. The goals include querying reasonable actions with limited chances and efficiently modifying the policy. Our insight is to unify these two goals via a proper pseudometric. Intuitively, the metric can compare online and offline states to infer optimal query actions. Additionally, efficient policy updates require good knowledge of the similarity between query results and historical data. Therefore, we propose a unified framework, denoted Pseudometric Guided Offline-to-Online RL (PGO2). Specifically, in deep Q learning, PGO2 has a structural design between the Q-neural network and the Siamese network, which guarantees simultaneous Q-network updating and pseudometric learning, promoting Q-network fine-tuning. In the inference phase, PGO2 solves convex optimizations to identify optimal query actions. We also show that PGO2 training converges to the so-called bisimulation metric with strong theoretical guarantees. Finally, we demonstrate the superiority of PGO2 on diversified datasets.
TEMPERA: Test-Time Prompt Editing via Reinforcement Learning
Authors: Tianjun Zhang; Xuezhi Wang; Denny Zhou; Dale Schuurmans; Joseph E. Gonzalez
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning.  As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we propose Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge, is adaptive to different queries and provides an interpretable prompt for every query. To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers. The proposed method achieves significant gains compared with recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a variety of tasks including sentiment analysis, topic classification, natural language inference, and reading comprehension. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
ELBO-ing Stein Mixtures
Authors: Ola Rønning; Christophe Ley; Ahmad Salim Al-Sibahi; Thomas Hamelryck
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Stein variational gradient descent (SVGD) \citep{DBLP:conf/nips/LiuW16} is a particle-based technique for Bayesian inference. SVGD has recently gained popularity because it combines the ability of variational inference to handle tall data with the modeling power of non-parametric inference. Unfortunately, the number of particles required to represent a model adequately grows exponentially with the dimensionality of the model. Stein mixtures \citep{nalisnick2017variational} alleviate the exponential growth in particles by letting each particle parameterize a distribution. However, the inference algorithm proposed by \cite{nalisnick2017variational} can be numerically unstable.  We show that their algorithm corresponds to inference with the R\'enyi $\alpha$-divergence for $\alpha=0$ and that using other values for $\alpha$ can lead to more stable inference. We empirically study the performance of Stein mixtures inferred with different $\alpha$ values on various real-world problems, demonstrating significantly improved results when using $\alpha=1$, which coincides with using the evidence lower bound (ELBO). We call this instance of our algorithm ELBO-within-Stein. A black-box version of the inference algorithm (for arbitrary $\alpha\in \sR$) is available in the deep probabilistic programming language NumPyro \citep{phan2019}.
Receding Neuron Importances for Structured Pruning
Authors: Mihai Suteu; Yike Guo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. While this can be elegantly achieved by applying sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would shrink all scaling factors rather than just those of superfluous neurons. To tackle this issue, we introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance. Under our method, the weights of unnecessary neurons effectively recede, producing a polarised bimodal distribution of importances. We show that neural networks trained this way can be pruned to a larger extent and with less deterioration. We one-shot prune VGG and ResNet architectures at different ratios on CIFAR and ImagenNet datasets. In the case of VGG-style networks, our method significantly outperforms existing approaches particularly under severe pruning.
Does Continual Learning Equally Forget All Parameters?
Authors: Haiyan Zhao; Tianyi Zhou; Guodong Long; Jing Jiang; Chengqi Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedly replaying buffered data, the every-step replay is time-consuming and the memory to store historical data is usually too small for retraining all parameters. In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL. Our proposed metrics show that only a few modules are more task-specific and sensitively alters between tasks, while others can be shared across tasks as common knowledge. Hence, we attribute forgetting mainly to the former and find that finetuning them only on a small buffer at the end of any CL method can bring non-trivial improvement. Due to the small number of finetuned parameters, such ``Forgetting Prioritized Finetuning (FPF)'' is efficient on both the computation and buffer size required. We further propose a more efficient and simpler method that entirely removes the every-step replay and replaces them by only $k$-times of FPF periodically triggered during CL. Surprisingly, this ``$k$-FPF'' performs comparably to FPF and outperforms the SOTA CL methods but significantly reduces their computational overhead and cost. In experiments on several benchmarks of class- and domain-incremental CL, FPF consistently improves existing CL methods by a large margin and $k$-FPF further excels on the efficiency without degrading the accuracy. We also empirically studied the impact of buffer size, epochs per task, and finetuning modules to the cost and accuracy of our methods.
A Deep Dive into Dataset Imbalance and Bias in Face Identification
Authors: Valeriia Cherepanova; Steven Reich; Samuel Dooley; Hossein Souri; John P Dickerson; Micah Goldblum; Tom Goldstein
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e., that FR models perform worse on images of non-white people or women because these demographic groups are underrepresented in training data. Recent academic research paints a more nuanced picture of this relationship. However, previous studies of data imbalance in FR have focused exclusively on the face verification setting, while the face identification setting has been largely ignored, despite being deployed in sensitive applications such as law enforcement. This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity. In this work, we address this gap in the research by thoroughly exploring the effects of each kind of imbalance possible in face identification, and discuss other factors which may impact bias in this setting.
Unsupervised Model Selection for Time Series Anomaly Detection
Authors: Mononito Goswami; Cristian Ignacio Challu; Laurent Callot; Lenon Minorics; Andrey Kan
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Anomaly detection in time-series has a wide range of practical applications. While numerous anomaly detection methods have been proposed in the literature, a recent survey concluded that no single method is the most accurate across various datasets. To make matters worse, anomaly labels are scarce and rarely available in practice. The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature. This paper answers this question \textit{i.e.} Given an unlabeled dataset and a set of candidate anomaly detectors, how can we select the most accurate model? To this end, we identify three classes of surrogate (unsupervised) metrics, namely, \textit{prediction error}, \textit{model centrality}, and \textit{performance on injected synthetic anomalies}, and show that some metrics are highly correlated with standard supervised anomaly detection performance metrics such as the $F_1$ score, but to varying degrees. We formulate metric combination with multiple imperfect surrogate metrics as a robust rank aggregation problem. We then provide theoretical justification behind the proposed approach. Large-scale experiments on multiple real-world datasets demonstrate that our proposed unsupervised approach is as effective as selecting the most accurate model based on partially labeled data.
QAID: Question Answering Inspired Few-shot Intent Detection
Authors: Asaf Yehudai; Matan Vetzler; Yosi Mass; Koren Lazar; Doron Cohen; Boaz Carmeli
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same intent. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
Authors: Kundan Krishna; Saurabh Garg; Jeffrey P. Bigham; Zachary Chase Lipton
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: For most natural language processing tasks, the dominant practice is to finetune large pretrained transformer models (e.g., BERT) using smaller downstream datasets. Despite the success of this approach, it remains unclear to what extent these gains are attributable to the massive background corpora employed for pretraining versus to the pretraining objectives themselves. This paper introduces a large-scale study of self-pretraining, where the same (downstream) training data is used for both pretraining and finetuning. In experiments addressing both ELECTRA and RoBERTa models and 10 distinct downstream datasets, we observe that self-pretraining rivals standard pretraining on the BookWiki corpus (despite using around $10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$ datasets, respectively. Surprisingly, these task-specific pretrained models often perform well on other tasks, including the GLUE benchmark. Our results suggest that in many scenarios, performance gains attributable to pretraining are driven primarily by the pretraining objective itself and are not always attributable to the incorporation of massive datasets. These findings are especially relevant in light of concerns about intellectual property and offensive content in web-scale pretraining data.
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Authors: Riashat Islam; Manan Tomar; Alex Lamb; Hongyu Zang; Yonathan Efroni; Dipendra Misra; Xin Li; Harm van Seijen; Remi Tachet des Combes; John Langford
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major challenge in this setting is the presence of input information that is hard to model and irrelevant to controlling the agent. This problem has been approached by the theoretical RL community through the lens of exogenous information, i.e, any control-irrelevant information contained in observations. For example, a robot navigating in busy streets needs to ignore irrelevant information, such as other people walking in the background, textures of objects, or birds in the sky. In this paper, we focus on the setting with visually detailed exogenous information, and introduce new offline RL benchmarks offering the ability to study this problem. We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications. To address these, we propose to use multi-step inverse models, which have seen a great deal of interest in the RL theory community, to learn Agent-Controller Representations for Offline-RL (ACRO). Despite being simple and requiring no reward, we show theoretically and empirically that the representation created by this objective greatly outperforms baselines.
Learning Iterative Neural Optimizers for Image Steganography
Authors: Xiangyu Chen; Varsha Kishore; Kilian Q Weinberger
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Image steganography is the process of concealing secret information in images through imperceptible changes. 
Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes.
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference
Authors: Pierre Glaser; Michael Arbel; Arnaud Doucet; Arthur Gretton
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We introduce two Synthetic Likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fidelity simulator is available. Both methods learn a Conditional Energy-Based Model (EBM) of the likelihood using synthetic data generated by the simulator, conditioned on parameters drawn from a proposal distribution. The learned likelihood can then be combined with any prior to obtain a posterior estimate, from which samples can be drawn using MCMC. 
Our methods uniquely combine a flexible Energy-Based Model and the minimization of a KL loss: this is in contrast to other synthetic likelihood methods, which either rely on normalizing flows, or minimize score-based objectives; choices that come with known pitfalls. Our first method, Amortized Unnormalized Neural Likelihood Estimation (AUNLE), introduces a tilting trick during training that allows to perform inference using efficient MCMC techniques. Our second method, Sequential UNLE (SUNLE), employs a doubly intractable approach in order to re-use simulation data and improve posterior accuracy for a specific observation. 
We demonstrate the properties of both methods on a range of synthetic datasets, and apply it to a neuroscience model of the pyloric network in the crab, matching the performance of other synthetic likelihood methods at a fraction of the simulation budget.
Finding Private Bugs: Debugging Implementations of Differentially Private Stochastic Gradient Descent 
Authors: Congyu Fang; Hengrui Jia; Ali Shahin Shamsabadi; Nicolas Papernot
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: It is important to learn with privacy-preserving algorithms when training data contains sensitive information. Differential privacy (DP) proposes to bound the worst-case privacy leakage of a training algorithm. However, the analytic nature of these algorithmic guarantees makes it difficult to verify that an implementation of a differentially private learner is correct. Research in the field focuses on empirically approximating the analytic bound, which only assesses whether an implementation provides the guarantee claimed for a particular dataset or not. It is also typically costly. In this paper, we take a first step towards providing a simple and lightweight methodology for practitioners to identify common implementation mistakes without imposing any changes to their scripts. Our approach stems from measuring distances between models outputted by the training algorithm. We demonstrate that our method successfully identifies specific mistakes made in the implementation of DP-SGD, the de facto algorithm for differentially private deep learning. These mistakes include improper gradient computations or noise miscalibration. Both approaches invalidate assumptions that are essential to obtaining a rigorous privacy guarantee.
Compositional Semantic Parsing with Large Language Models
Authors: Andrew Drozdov; Nathanael Schärli; Ekin Akyürek; Nathan Scales; Xinying Song; Xinyun Chen; Olivier Bousquet; Denny Zhou
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Humans can reason compositionally when presented with new tasks.  Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN. In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications.
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
Authors: Ryo Karakida; Tomoumi Takase; Tomohiro Hayase; Kazuki Osawa
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Gradient regularization (GR) is a method that penalizes the gradient norm of the training loss during training. Although some studies have reported that GR improves generalization performance in deep learning, little attention has been paid to it from the algorithmic perspective, that is, the algorithms of GR that efficiently improve performance. In this study, we first reveal that a specific finite-difference computation, composed of both gradient ascent and descent steps, reduces the computational cost for GR. In addition, this computation empirically achieves better generalization performance. Next, we theoretically analyze a solvable model, a diagonal linear network, and clarify that GR has a desirable implicit bias. Learning with GR chooses better minima in a certain problem, and the finite-difference GR chooses even better ones as the ascent step size becomes larger. Finally, we demonstrate that finite-difference GR is closely related to some other algorithms based on iterative ascent and descent steps for exploring flat minima: sharpness-aware minimization and the flooding method. In particular, we reveal that flooding performs finite-difference GR in an implicit way. Thus, this work broadens our understanding of GR in both practice and theory.
Contrastive Novelty Learning: Anticipating Outliers with Large Language Models
Authors: Albert Xu; Xiang Ren; Robin Jia
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly. Selective prediction, in which models abstain on low-confidence examples, provides a possible solution, but existing models are often overly confident on OOD examples. To remedy this overconfidence, we introduce Contrastive Novelty Learning (CNL), a two-step method that generates OOD examples representative of novel classes, then trains to decrease confidence on them. First, we generate OOD examples by prompting a large language model twice: we prompt it to enumerate novel classes relevant to the label set, then generate examples from each novel class matching the task format. Second, we train our classifier with a novel contrastive objective that encourages lower confidence on generated OOD examples than training examples. When trained with CNL, classifiers improve in their ability to detect and abstain on OOD examples over prior methods by an average of 2.3% AUAC and 5.5% AUROC across 4 NLP datasets, with no cost to in-distribution accuracy.