GenAI4ELab/papercli-papers huggingface dataset:
On student-teacher deviations in distillation: does it pay to disobey?
Authors: Vaishnavh Nagarajan; Aditya Krishna Menon; Srinadh Bhojanapalli; Hossein Mobahi; Sanjiv Kumar
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Knowledge distillation has been widely-used to improve the performance of a ``student'' network by hoping to mimic the soft probabilities of a  ``teacher'' network. Yet, for self-distillation to work, the student {\em must} deviate from the teacher in some manner \citep{stanton21does}. We conduct a variety of experiments across image and language classification datasets to more precisely understand the nature of student-teacher deviations and how they relate to accuracy gains. Our first key empirical observation is that in a majority of our settings, the student underfits points that the teacher finds hard. Next, we find that student-teacher deviations during the \textit{initial} phase training are \textit{not} crucial to get the benefits of distillation --- simply switching to distillation in the middle of training can recover a significant fraction of distillation's accuracy gains.
We then provide two parallel theoretical perspectives of student-teacher deviations, one casting distillation as a regularizer in eigenspace, and another as a denoiser of gradients. In both these views, we argue how our empirically reported student-teacher deviations may emerge, and how they may relate to generalization. Importantly, our analysis bridges key gaps between existing theory and practice by focusing on gradient descent and avoiding label noise assumptions.
TextShield: Beyond Successfully Detecting Adversarial Sentences in text classification
Authors: Lingfeng Shen; Ze Zhang; Haiyun Jiang; Ying Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones. However, {the core limitation of previous detection methods is being incapable of giving correct predictions on adversarial sentences unlike defense methods from other paradigms.} To solve this issue, this paper proposes TextShield: (1) we discover a link between text attack and saliency information, and then we propose a saliency-based detector, which can effectively detect whether an input sentence is adversarial or not. (2) We design a saliency-based corrector, which converts the detected adversary sentences to benign ones. By combining the saliency-based detector and corrector, TextShield extends the detection-only paradigm to a detection-correction paradigm, thus filling the gap in the existing detection-based defense. Comprehensive experiments show that (a) TextShield consistently achieves higher or comparable performance than state-of-the-art defense methods across various attacks on different benchmarks. (b) our saliency-based detector outperforms existing detectors for detecting adversarial sentences.
Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation
Authors: Shuzhou Sun; Shuaifeng Zhi; Janne Heikkilä; Li Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Scene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting current AL methods to the SGG task poses the following challenges: 1) unreliable uncertainty estimates, and 2) data bias problems. To deal with these challenges, we propose EDAL (\textbf{E}vidential Uncertainty and \textbf{D}iversity Guided Deep \textbf{A}ctive \textbf{L}earning), a novel AL framework tailored for the SGG task. For challenge 1), we start with Evidential Deep Learning (EDL) coupled with a global relationship mining approach to estimate uncertainty, which can effectively overcome the perturbations of open-set relationships and background-relationships to obtain reliable uncertainty estimates. To address challenge 2), we seek the diversity-based method and design the Context Blocking Module (CBM) and Image Blocking Module (IBM) to alleviate context-level bias and image-level bias, respectively. Experiments show that our AL framework can approach the performance of a fully supervised SGG model with only about $10\%$ annotation cost. Furthermore, our ablation studies indicate that introducing AL into the SGG will face many challenges not observed in other vision tasks that are successfully overcome by our new modules.
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
Authors: Mateusz Olko; Michał Zając; Aleksandra Nowak; Nino Scherrer; Yashas Annadani; Stefan Bauer; Łukasz Kuciński; Piotr Miłoś
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system’s causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental design approaches for causal discovery aim to minimize the number of interventions by estimating the most informative intervention target. In this work, we propose a novel gradient-based intervention targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.
Hardware-aware compression with Random Operation Access Specific Tile (ROAST) hashing
Authors: Aditya Desai; Keren Zhou; Anshumali Shrivastava
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Advancements in deep learning are often associated with increasing model sizes.
Training and deploying large models require sophisticated hardware and incur
significantly higher costs. Thus, model compression is a widely explored approach
to solving the problem. However, SOTA techniques fall short in one or more
desirable aspects of compression - for instance, pruning does not reduce memory
for training, quantization can only provide up to $32\times$ compression, HashedNet
is cache-inefficient, etc. This paper proposes a model-agnostic, cache-friendly,
and hardware-aware model compression approach: Random Operation Access
Specific Tile (ROAST) hashing. ROAST collapses the parameters by clubbing them
through a lightweight mapping. While clubbing these parameters, ROAST utilizes
cache hierarchies by aligning the memory access pattern with the parameter access
pattern. ROAST is up to $\sim 25 \times$ faster to train and $\sim 50 \times$ faster to infer than the
popular parameter sharing method HashedNet. Additionally, ROAST introduces
global weight sharing, which is empirically and theoretically superior to local
weight sharing in HashedNet, and can be of independent interest. With ROAST, we
can efficiently train and deploy the model using a much smaller memory footprint
($\sim 10 \times - 100 \times$ lesser) in text and image classification tasks
Is Attention All That NeRF Needs?
Authors: Mukund Varma T; Peihao Wang; Xuxi Chen; Tianlong Chen; Subhashini Venugopalan; Zhangyang Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to render novel views on the fly from source views. While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages. (1) The view transformer leverages multi-view geometry as an inductive bias for attention-based scene representation, and predicts coordinate-aligned features by aggregating information from epipolar lines on the neighboring views. (2) The ray transformer renders novel views using attention to decode the features from the view transformer along the sampled points during ray marching. Our experiments demonstrate that when optimized on a single scene, GNT can successfully reconstruct NeRF without an explicit rendering formula due to the learned ray renderer. When trained on multiple scenes, GNT consistently achieves state-of-the-art performance when transferring to unseen scenes and outperform all other methods by ~10% on average. Our analysis of the learned attention maps to infer depth and occlusion indicate that attention enables learning a physically-grounded rendering. Our results show the promise of transformers as a universal modeling tool for graphics. Please refer to our project page for video results: https://vita-group.github.io/GNT/
Spotting Expressivity Bottlenecks and Fixing Them Optimally 
Authors: Manon Verbockhaven; Guillaume Charpiat
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning tasks are generally formulated as optimization problems, where one searches for an optimal function within a certain functional space. In practice, parameterized functional spaces are considered, in order to be able to perform gradient descent. Typically, a neural network architecture is chosen and fixed, and its parameters (connection weights) are optimized, yielding an architecture-dependent result. This way of proceeding however forces the evolution of the function during training to lie within the realm of what is expressible with the chosen architecture, and prevents any optimization across possible architectures. Costly architectural hyper-parameter optimization is often performed to compensate for this. Instead, we propose to adapt the architecture on the fly during training. We show that the information about desirable architectural changes, due to expressivity bottlenecks when attempting to follow the functional gradient, can be extracted from the back-propagation.  To do this, we propose a new mathematically well-grounded method to detect expressivity bottlenecks on the fly and solve them by adding suitable neurons when and where needed. Thus, while the standard approach requires large networks, in terms of number of neurons per layer, for expressivity and optimization reasons, we are able to start with  very small neural networks and let them grow appropriately.  As a proof of concept, we show convincing results on the MNIST dataset, matching large neural network accuracy, with competitive training time, while removing the need for standard architectural hyper-parameter optimization.
Vera Verto: Multimodal Hijacking Attack
Authors: Minxing Zhang; Ahmed Salem; Michael Backes; Yang Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The increasing cost of training machine learning (ML) models has led to the inclusion of new parties to the training pipeline, such as users who contribute training data and companies that provide computing resources. This involvement of such new parties in the ML training process has introduced new attack surfaces for an adversary to exploit. A recent attack in this domain is the model hijacking attack, whereby an adversary hijacks a victim model to implement their own -- possibly malicious -- hijacking tasks. However, the scope of the model hijacking attack is so far limited to computer vision-related tasks. In this paper, we transform the model hijacking attack into a more general multimodal setting, where the hijacking and original tasks are performed on data of different modalities. Specifically, we focus on the setting where an adversary implements a natural language processing (NLP) hijacking task into an image classification model. To mount the attack, we propose a novel encoder-decoder based framework, namely the Blender, which relies on advanced image and language models. Experimental results show that our modal hijacking attack achieves strong performances in different settings. For instance, our attack achieves 94%, 94%, and 95% attack success rate when using the Sogou news dataset to hijack STL10, CIFAR-10, and MNIST classifiers.
Probability flow solution of the Fokker-Planck equation
Authors: Nicholas Matthew Boffi; Eric Vanden-Eijnden
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The method of choice for integrating the time-dependent Fokker-Planck equation in high-dimension is to generate samples from the solution via integration of the associated stochastic differential equation. Here, we introduce an alternative scheme based on integrating an ordinary differential equation that describes the flow of probability. Acting as a transport map, this equation deterministically pushes samples from the initial density onto samples from the solution at any later time. Unlike integration of the stochastic dynamics, the method has the advantage of giving direct access to quantities that are challenging to estimate from trajectories alone, such as the probability current, the density itself, and its entropy. The probability flow equation depends on the gradient of the logarithm of the solution (its "score"), and so is a-priori unknown. To resolve this dependence, we model the score with a deep neural network that is learned on-the-fly by propagating a set of samples according to the instantaneous probability current. We consider several high-dimensional examples from the physics of interacting particle systems to highlight the efficiency and precision of the approach; we find that the method accurately matches analytical solutions computed by hand and moments computed via Monte-Carlo.
FONDUE: an Algorithm to Find the Optimal Dimensionality of the Latent Representations of Variational Autoencoders
Authors: Lisa Bonheme; Marek Grzes
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: When training a variational autoencoder (VAE) on a given dataset, determining the optimal number of latent variables is mostly done by grid search: a costly process in terms of computational time and carbon footprint.
In this paper, we explore the intrinsic dimension estimation (IDE) of the data and latent representations learned by VAEs.
We show that the discrepancies between the IDE of the mean and sampled representations of a VAE after only a few steps of training reveal the presence of passive variables in the latent space, which, in well-behaved VAEs, indicates a superfluous number of dimensions.
Using this property, we propose FONDUE: an algorithm which quickly finds the number of latent dimensions after which the mean and sampled representations start to diverge (i.e., when passive variables are introduced), providing a principled method for selecting the number of latent dimensions for VAEs and autoencoders.
A sampling framework for value-based reinforcement learning
Authors: Frank Shih; Faming Liang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Value-based algorithms have achieved great successes in solving Reinforcement Learning problems via minimizing the mean squared Bellman error (MSBE). Temporal-difference (TD) algorithms such as Q-learning and SARSA often use stochastic gradient descent based optimization approaches to estimate the value function parameters, but fail to quantify their uncertainties. In our work, under the Kalman filtering paradigm, we establish a novel and scalable sampling framework based on stochastic gradient Markov chain Monte Carlo, which allows us to efficiently generate samples from the posterior distribution of deep neural network parameters. For TD-learning with both linear and nonlinear function approximation, we prove that the proposed algorithm converges to a stationary distribution, which allows us to measure uncertainties of the value function and its parameters.
kNN-Diffusion: Image Generation via Large-Scale Retrieval
Authors: Shelly Sheynin; Oron Ashual; Adam Polyak; Uriel Singer; Oran Gafni; Eliya Nachmani; Yaniv Taigman
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled.
In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a substantially small and efficient text-to-image diffusion model using only pre-trained multi-modal embeddings, but without an explicit text-image dataset, (2) generating out-of-distribution images by simply swapping the retrieval database at inference time, and (3) performing text-driven local semantic manipulations while preserving object identity. To demonstrate the robustness of our method, we apply our kNN approach on two state-of-the-art diffusion backbones, and show results on several different datasets. As evaluated by human studies and automatic metrics, our method achieves state-of-the-art results compared to existing approaches that train text-to-image generation models using images-only dataset.
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning
Authors: Weize Chen; Xu Han; Yankai Lin; Zhiyuan Liu; Maosong Sun; Jie Zhou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Parameter-efficient tuning methods (PETs) have achieved promising results in tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and additional tunable parameters as systems and controls respectively, PETs can be theoretically grounded to optimal control and further viewed as optimizing terminal cost and running cost in the optimal control literature. Despite the elegance of this theoretical grounding, in practice, existing PETs often ignore the running cost and only optimize the terminal cost, i.e., focus on optimizing the loss function of the output state, regardless of the running cost that depends on the intermediate states. Since it is non-trivial to directly model the intermediate states and design a running cost function, we propose to use latent stochastic bridges to regularize the intermediate states and serve as the running cost of PETs. As the first work to propose regularized PETs that use stochastic bridges as the regularizers (running costs) for intermediate states, we show the effectiveness and generality of this regularization across different tasks, PLMs and PETs. In view of the great potential and capacity, we believe more sophisticated regularizers can be designed for PETs and better performance can be achieved in the future.
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning 
Authors: Zaid Khan; Yun Fu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Contrastive vision-language models (e.g. CLIP) are typically created by updating all the parameters of a vision model and language model through contrastive training. Can such models be created by a small number of parameter updates to an already-trained language model and vision model? The literature describes techniques that can create vision-language models by updating a small number of parameters in a language model, but these require already aligned visual representations and are non-contrastive, hence unusable for latency-sensitive applications such as neural search. We explore the feasibility and benefits of parameter-efficient contrastive vision-language alignment through transfer learning: creating a model such as CLIP by minimally updating an already-trained vision and language model. We find that a minimal set of parameter updates ($<$7\%) can achieve the same performance as full-model training, and updating specific components ($<$1\% of parameters) can match 75\% of full-model training. We describe a series of experiments: we show that existing knowledge is conserved more strongly in parameter-efficient training and that parameter-efficient scaling scales with model and dataset size. Where paired-image text data is scarce but strong multilingual language models exist (e.g. low resource languages), parameter-efficient training is even preferable to full-model training. Given a fixed compute budget, parameter-efficient training allows training larger models on the same hardware, achieving equivalent performance in less time. Parameter-efficient training hence constitutes an energy-efficient and effective training strategy for contrastive vision-language models that may be preferable to the full-model training paradigm for common use cases.
Code and weights at https://github.com/codezakh/LilT.
How does Uncertainty-aware Sample-selection Help Decision against Action Noise?
Authors: Xingrui Yu; Bo Han; Ivor Tsang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning from imperfect demonstrations has become a vital problem in imitation learning (IL). Since the assumption of the collected demonstrations are optimal cannot always hold in real-world tasks, many previous works considers learning from a mixture of optimal and sub-optimal demonstrations. On the other hand, video records can be hands-down demonstrations in practice. Leveraging such demonstrations requires labors to output action for each frame. However, action noise always occurs when the labors are not domain experts, or meet confusing state frames. Previous IL methods can be vulnerable to such demonstrations with state-dependent action noise. To tackle this problem, we propose a robust learning paradigm called USN, which bridges Uncertainty-aware Sample-selection with Negative learning. First, IL model feeds forward all demonstration data and estimates its predictive uncertainty. Then, we select large-loss samples in the light of the uncertainty measures. Next, we update the model parameters with additional negative learning on the selected samples. Empirical results on Box2D tasks and Atari games demonstrate that USN improves the performance of state-of-the-art IL methods by more than 10% under a large portion of action noise.
FairGBM: Gradient Boosting with Fairness Constraints
Authors: André Cruz; Catarina G Belém; João Bravo; Pedro Saleiro; Pedro Bizarro
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to their scalability, performance, and low training cost. While fairness in these domains is a foremost concern, existing in-processing Fair ML methods are either incompatible with GBDT, or incur in significant performance losses while taking considerably longer to train. We present FairGBM, a dual ascent learning framework for training GBDT under fairness constraints, with little to no impact on predictive performance when compared to unconstrained GBDT. Since observational fairness metrics are non-differentiable, we propose smooth convex error rate proxies for common fairness criteria, enabling gradient-based optimization using a ``proxy-Lagrangian'' formulation. Our implementation shows an order of magnitude speedup in training time relative to related work, a pivotal aspect to foster the widespread adoption of FairGBM by real-world practitioners.