GenAI4ELab/papercli-papers huggingface dataset:
Mutual Partial Label Learning with Competitive Label Noise
Authors: Yan Yan; Yuhong Guo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Partial label learning (PLL) is an important weakly supervised learning problem, where each training instance is associated with a set of candidate labels that include both the true label and additional noisy labels. Most existing PLL methods assume the candidate noisy labels are randomly chosen, which hardly holds in real-world learning scenarios. In this paper, we consider a more realistic PLL scenario with competitive label noise that is more difficult to distinguish from the true label than the random label noise. We propose a novel Mutual Learning based PLL approach named ML-PLL to address this challenging problem. ML-PLL learns a prediction network based classifier and a class-prototype based classifier cooperatively through interactive mutual learning and label correction. Moreover, we use a transformation network to model the association relationships between the true label and candidate labels, and learn it together with the prediction network to match the observed candidate labels in the training data and enhance label correction. Extensive experiments are conducted on several benchmark PLL datasets, and the proposed ML-PLL approach demonstrates state-of-the-art performance for partial label learning.
Discovering Latent Knowledge in Language Models Without Supervision
Authors: Collin Burns; Haotian Ye; Dan Klein; Jacob Steinhardt
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.
Class-Incremental Learning with Repetition
Authors: Hamed Hemati; Andrea Cossu; Antonio Carta; Julio Hurtado; Lorenzo Pellegrini; Davide Bacciu; Vincenzo Lomonaco; Damian Borth
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike replay, cannot be controlled by the user. Nowadays, Class-Incremental scenarios represent the leading test-bed for assessing and comparing CL strategies. This family of scenarios is very easy to use, but it never allows revisiting previously seen classes, thus completely disregarding the role of repetition. We focus on the family of Class-Incremental with Repetition (CIR) scenarios, where repetition is embedded in the definition of the stream. We propose two stochastic scenario generators that produce a wide range of CIR scenarios starting from a single dataset and a few control parameters. We conduct the first comprehensive evaluation of repetition in CL by studying the behavior of existing CL strategies under different CIR scenarios. We then present a novel replay strategy that exploits repetition and counteracts the natural imbalance present in the stream. On both CIFAR100 and TinyImageNet, our strategy outperforms other replay approaches, which are not designed for environments with repetition.
Learning Counterfactually Invariant Predictors
Authors: Francesco Quinzan; Cecilia Casolo; Krikamol Muandet; Niki Kilbertus; Yucen Luo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a method to learn predictors that are invariant under counterfactual changes of certain covariates. This method is useful when the prediction target is causally influenced by covariates that should not affect the predictor output. For instance, this could prevent an object recognition model from being influenced by position, orientation, or scale of the object itself. We propose a model-agnostic regularization term based on conditional kernel mean embeddings to enforce counterfactual invariance during training. We prove the soundness of our method, which can handle mixed categorical and continuous multivariate attributes. Empirical results on synthetic and real-world data demonstrate the efficacy of our method in a variety of settings.
Demystifying the Optimization and Generalization of Deep PAC-Bayesian Learning
Authors: Wei Huang; Chunrui Liu; Yilan Chen; Richard Yi Da Xu; Miao Zhang; Tsui-Wei Weng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In addition to being a successful generalization bound analysis tool, the PAC-Bayesian bound can also be incorporated into an objective function to train a probabilistic neural network, which we refer to simply as {\it PAC-Bayesian Learning}. PAC-Bayesian learning has been proven to be able to achieve a competitive expected test set error numerically, while providing a tight generalization bound in practice, through gradient descent training. Despite its empirical success, the theoretical analysis of deep PAC-Bayesian learning for neural networks is rarely explored. To this end, this paper proposes a theoretical convergence and generalization analysis for PAC-Bayesian learning. For a deep and wide probabilistic neural network, we show that when PAC-Bayesian learning is applied, the convergence result corresponds to solving a kernel ridge regression when the probabilistic neural tangent kernel (PNTK) is used as its kernel. Based on this finding, we further obtain an analytic and guaranteed PAC-Bayesian generalization bound for the first time, which is an improvement over the Rademacher complexity-based bound for deterministic neural networks. Finally, drawing insight from our theoretical results, we propose a proxy measure for efficient hyperparameter selection, which is proven to be time-saving on various benchmarks.
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Authors: Hyesu Lim; Byeonggeun Kim; Jaegul Choo; Sungha Choi
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., transductive batch normalization (TBN), which calculates the mean and the variance from the current test batch rather than using the running mean and variance obtained from the source data, i.e., conventional batch normalization (CBN). Adopting TBN that employs test batch statistics mitigates the performance degradation caused by the domain shift. However, re-estimating normalization statistics using test data depends on impractical assumptions that a test batch should be large enough and be drawn from i.i.d. stream, and we observed that the previous methods with TBN show critical performance drop without the assumptions. In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer. Our proposed TTN improves model robustness to shifted domains across a wide range of batch sizes and in various realistic evaluation scenarios. TTN is widely applicable to other test-time adaptation methods that rely on updating model parameters via backpropagation. We demonstrate that adopting TTN further improves their performance and achieves state-of-the-art performance in various standard benchmarks.
Interpolating Compressed Parameter Subspaces
Authors: Siddhartha Datta; Nigel Shadbolt
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Though distribution shifts have caused growing concern for machine learning scalability, solutions tend to specialize towards a specific type of distribution shift. Methods for label shift may not succeed against domain or task shift, and vice versa. We learn that constructing a Compressed Parameter Subspaces (CPS), a geometric structure representing distance-regularized parameters mapped to a set of train-time distributions, can maximize average accuracy over a broad range of distribution shifts concurrently. We show sampling parameters within a CPS can mitigate backdoor, adversarial, permutation, stylization and rotation perturbations. We also show training a hypernetwork representing a CPS can adapt to seen tasks as well as unseen interpolated tasks.
Harnessing spectral representations for subgraph alignment
Authors: Marco Pegoraro; Riccardo Marin; Arianna Rampini; Simone Melzi; Luca Cosmo; Emanuele Rodolà
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: With the rise and advent of graph learning techniques, graph data has become ubiquitous in the machine learning field. However, while several efforts have been devoted to the design of new convolutional architectures, pooling or positional encoding schemes, relatively little focus has been spent on modeling pairwise problems such as signal transfer, graph isomorphism and subgraph correspondence tasks. With this paper, we anticipate the need for a convenient framework to deal with problems that revolve around the notion of a map among graphs, and focus in particular on the challenging subgraph alignment scenario. We claim that, first and foremost, the representation of a map plays a central role in how these problems should be modeled -- be it a map inference problem or a simpler signal transport task. Taking the hint from recent work in geometry processing, we propose the adoption of a spectral representation for maps that is compact, easy to compute, permutation-equivariant, easy to plug into learning pipelines, and especially effective for a wide range of situations, most notably when dealing with subgraph alignment problems. We further report for the first time a surprising phenomenon where the partiality arising in subgraph alignment is manifested in the structure of the map coefficients, even in the absence of exact isomorphism, and which is consistently observed over different families of graphs.
Cold Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator
Authors: Alexander Shekhovtsov
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The problem of estimating the gradient of an expectation in discrete random variables arises in many applications: learning with discrete latent representations, training neural networks with quantized weights, activations, conditional blocks, etc. 
This work contributes to the development of the popular Gumbel-Softmax family of estimator, which is based on approximating argmax with a temperature-parametrized softmax. The state-of-the art in this family, the Gumbel-Rao estimator uses internal MC samples to reduce the variance.
We show that in the limit of zero temperature the internal integration has a closed form solution. The limit estimator, called ZGR, has a favorable bias and variance, is simple to implement and computationally inexpensive and is obviously free of the temperature hyperparameter. Furthermore, ZGR is unbiased for the class of quadratic functions of categorical variables and can be decomposed into a sum of two simple but not very well performing on their own estimators: the straight through estimator and the DARN estimator. Experiments thoroughly validate the method.
Disentangling Learning Representations with Density Estimation
Authors: Eric Yeats; Frank Y Liu; Hai Li
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencoder (GCAE), a method which achieves reliable disentanglement via scalable non-parametric density estimation of the latent space. GCAE avoids the curse of dimensionality of density estimation by disentangling subsets of its latent space with the Dual Total Correlation (DTC) metric, thereby representing its high-dimensional latent joint distribution as a collection of many low-dimensional conditional distributions. In our experiments, GCAE achieves highly competitive and reliable disentanglement scores compared with state-of-the-art baselines.
UTS: When Monotonic Value Factorisation Meets Non-monotonic and Stochastic Targets
Authors: Zeyang Liu; Lipeng Wan; Xue Sui; Xingyu Chen; Xuguang Lan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Extracting decentralised policies from joint action-values is an attractive way to exploit centralised learning. It is possible to apply monotonic value factorisation to guarantee consistency between the centralised and decentralised policies. However, the best strategy for training decentralised policies when the target joint action-values are non-monotonic and stochastic is still unclear. We propose a novel value factorisation method named uncertainty-based target shaping (UTS) to solve this problem. UTS employs networks that estimate the reward and the following state's embedding, where the large prediction error indicates that the target is stochastic. By replacing deterministic targets for the suboptimal with the best per-agent values, we enforce that all shaped targets become a subset of the space that can be represented by monotonic value factorisation. Empirical results show that UTS outperforms state-of-the-art baselines on multiple benchmarks, including matrix games, predator-prey, and challenging tasks in the StarCraft II micromanagement.
SimPer: Simple Self-Supervised Learning of Periodic Targets
Authors: Yuzhe Yang; Xin Liu; Jiang Wu; Silviu Borac; Dina Katabi; Ming-Zher Poh; Daniel McDuff
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervised learning (SSL) methods overlook the intrinsic periodicity in data, and fail to learn representations that capture periodic or frequency attributes. In this paper, we present SimPer, a simple contrastive SSL regime for learning periodic information in data. To exploit the periodic inductive bias, SimPer introduces customized augmentations, feature similarity measures, and a generalized contrastive loss for learning efficient and robust periodic representations. Extensive experiments on common real-world tasks in human behavior analysis, environmental sensing, and healthcare domains verify the superior performance of SimPer compared to state-of-the-art SSL methods, highlighting its intriguing properties including better data efficiency, robustness to spurious correlations, and generalization to distribution shifts.
Implicit regularization via Spectral Neural Networks and non-linear matrix sensing
Authors: Subhroshekhar Ghosh; Thanh Lam; Soumendu Sundar Mukherjee
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The phenomenon of \textit{implicit regularization} has attracted interest in the recent years as a fundamental aspect of the remarkable generalizing ability of neural networks. In a nutshell, it entails that gradient flow dynamics in many neural nets, even without any explicit regularizer in the loss function, converges to the solution of a regularized learning problem. However, known results attempting to theoretically explain this phenomenon focus overwhelmingly on the setting of linear neural nets, and the simplicity of the linear structure is particularly crucial to existing arguments. In this paper, we explore this problem in the context of more realistic neural networks with a general class of non-linear activation functions, and rigorously demonstrate the implicit regularization phenomenon for such networks in the setting of matrix sensing problems. This is coupled with rigorous rate guarantees that ensure exponentially fast convergence of gradient descent, complemented by matching lower bounds which stipulate that the exponential rate is the best achievable. In this vein, we contribute a network architecture called Spectral Neural Networks (\textit{abbrv.} SNN) that is particularly suitable for matrix learning problems. Conceptually, this entails coordinatizing the space of matrices by their singular values and singular vectors, as opposed to by their entries, a potentially fruitful perspective for matrix learning. We demonstrate that the SNN architecture is inherently much more amenable to theoretical analysis than vanilla neural nets and confirm its effectiveness in the context of matrix sensing, supported via both mathematical guarantees and empirical investigations. We believe that the SNN architecture has the potential to be of wide applicability in a broad class of matrix learning scenarios.
TabDDPM: Modelling Tabular Data with Diffusion Models
Authors: Akim Kotelnikov; Dmitry Baranchuk; Ivan Rubachev; Artem Babenko
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention for other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous for general tabular problems, where datapoints are typically represented by vectors of heterogeneous features. The inherent heterogeneity of tabular data makes it quite challenging for accurate modeling, since the individual features can be of completely different nature, i.e., some of them can be continuous and some of them can be discrete. To address such data types, we introduce TabDDPM --- a diffusion model that can be universally applied to any tabular dataset and handles any types of features. We extensively evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority over existing GAN/VAE alternatives, which is consistent with the advantage of diffusion models in other fields. Additionally, we show that TabDDPM can be successfully used in privacy-oriented setups, where the original datapoints cannot be shared.
On the Importance of the Policy Structure in Offline Reinforcement Learning
Authors: Takayuki Osa; Akinobu Hayashi; Pranav Deo; Naoki Morihira; Takahide Yoshiike
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Offline reinforcement learning (RL) has attracted a great deal of attention recently as an approach to utilizing past experience to learn a policy. Recent studies have reported the challenges of offline RL, such as estimating the values of actions that are out of the data distribution. To mitigate the issues of offline RL, we propose an algorithm that leverages a mixture of deterministic policies. With our framework, the state-action space is divided by learning discrete latent variables, and sub-policies corresponding to each region are trained. The proposed algorithm, which we call Value-Weighted Variational Auto-Encoder (V2AE), is derived by considering the variational lower bound of the offline RL objective function. The aim of this work is to shed lights on the importance on the policy structure in offline RL. We show empirically that the use of the proposed mixture policy can reduce the accumulation of the approximation error in offline RL, which was reported in previous studies. Experimental results also indicate that introducing the policy structure improves the performance on tasks with D4RL benchmarking datasets.
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Authors: Haoxuan Li; Yan Lyu; Chunyuan Zheng; Peng Wu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate.
However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce the bias and variance simultaneously for existing DR approaches when the error  imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.
Change Detection for bi-temporal images classification based on Siamese Variational AutoEncoder and Transfer Learning
Authors: Chouikhi Farah; Ali Ben Abbes; Imed Riadh Farah
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Siamese structures empower Deep Learning (DL) models to increase their efficiency by learning how to extract the relevant temporal features from the input data.  In this paper, a Siamese Variational Auto-Encoder (VAE) model based on transfer learning (TL) is applied for change detection (CD) using bi-temporal images. The introduced method is trained in a supervised strategy for classification tasks. Firstly, the suggested generative method utilizes two VAEs to extract features from bi-temporal images. Subsequently, concatenates them into a feature vector. To get a classification map of the source scene, the classifier receives this vector and the ground truth data as input. The source model is fine-tuned to be applied to the target scene with less ground truth data using a TL strategy. Experiments were carried out in two study areas in the arid regions of southern Tunisia. The obtained results reveal that the proposed method outperformed the Siamese Convolution Neural Network (SCNN) by achieving an accuracy of more than 98%, in the source scene, and increased the accuracy in the target scene by 1.25% by applying the TL strategy.