nils-herrmann/clean_scirepeval huggingface dataset:
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
Authors: Charan Reddy, Soroush Mehri, Deepak Sharma, Samira Shabanian, Sina Honari

With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning. In this paper, we evaluate different fairness methods trained with deep neural networks on a common synthetic dataset to obtain a better insight into the working of these methods. In particular, we train about 2000 different models in various setups, including unbalanced and correlated data configurations, to verify the limits of the current models and better understand in which setups they are subject to failure. In doing so we present a dataset, a large subset of proposed fairness metrics in the literature, and rigorously evaluate recent promising debiasing algorithms in a common framework hoping the research community would take this benchmark as a common entry point for fair deep learning.
Novel positional encodings to enable tree-structured transformers
Authors: Vighnesh Leonardo Shiv, Chris Quirk

With interest in program synthesis and similarly ﬂavored problems rapidly increasing, neural models optimized for tree-domain problems are of great value. In the sequence domain, transformers can learn relationships across arbitrary pairs of positions with less bias than recurrent models. Under the intuition that a similar property would be beneficial in the tree domain, we propose a method to extend transformers to tree-structured inputs and/or outputs. Our approach abstracts transformer's default sinusoidal positional encodings, allowing us to substitute in a novel custom positional encoding scheme that represents node positions within a tree. We evaluated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over the vanilla transformer model on several tasks.
Communicating Natural Programs to Humans and Machines
Authors: Sam Acquaviva, Yewen Pu, Marta Kryven, Catherine Wong, Theodoros Sechopoulos, Gabrielle Ecanow, Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum

The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI. What makes building intelligent systems that can generalize to novel situations such as ARC difficult?
We posit that the answer might be found by studying the difference of \emph{language}: While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language that they can precisely execute.
We present LARC, the \textit{Language-complete ARC}: a collection of natural language descriptions by a group of human participants  who instruct each other on how to solve ARC tasks using language alone, which contains successful instructions for 88\% of the ARC tasks.
We analyze the collected instructions as `natural programs', finding that while they resemble computer programs, they are distinct in two ways: First, they contain a wide range of primitives; Second, they frequently leverage communicative strategies beyond directly executable codes. We demonstrate that these two distinctions prevent current program synthesis techniques from leveraging LARC to its full potential, and give concrete suggestions on how to build the next-generation program synthesizers.
Learning When and What to Ask: a Hierarchical Reinforcement Learning Framework
Authors: Khanh Xuan Nguyen, Yonatan Bisk, Hal Daumé III

Reliable AI agents should be mindful of the limits of their knowledge and consult humans when sensing that they do not have sufficient knowledge to make sound decisions. We formulate a hierarchical reinforcement learning framework for learning to decide when to request additional information from humans and what type of information would be helpful to request. Our framework extends partially-observed Markov decision processes (POMDPs) by allowing an agent to interact with an assistant to leverage their knowledge in accomplishing tasks. Results on a simulated human-assisted navigation problem demonstrate the effectiveness of our framework: aided with an interaction policy learned by our method, a navigation policy achieves up to a 7× improvement in task success rate compared to performing tasks only by itself.  The interaction policy is also efficient: on average, only a quarter of all actions taken during a task execution are requests for information. We analyze benefits and challenges of learning with a hierarchical policy structure and suggest directions for future work.
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
Authors: Hansen Wang, Zefang Zong, Tong Xia, Shuyu Luo, Meng Zheng, Depeng Jin, Yong Li

The large-scale vehicle routing problems are defined based on the classical VRPs with thousands of customers. It is of great importance to find an efficient and high-quality solution for real-world applications. However, existing algorithms for VRPs including non-learning heuristics and RL-based methods, only perform well on small-scale instances with usually no more than a hundred customers. They are unable to solve large-scale VRPs due to either high computation cost or explosive solution space that results in model divergence. 
Inspired by the classical idea of Divide-and-Conquer, we present a novel Rewriting-by-Generating(RBG) framework with hierarchical RL agents to solve large-scale VRPs. RBG consists of a rewriter agent that refines the customer division globally and an elementary generator to infer regional solutions locally. Extensive experiments demonstrate the effectiveness and efficiency of our proposed RBG framework. It outperforms LKH3, the state-of-the-art method for CVRPs, by $2.43\%$ when customer number $N=2000$ and shortens the inference time by about 100 times.
Semi-Supervised Few-Shot Learning with a Controlled Degree of Task-Adaptive Conditioning
Authors: Sung Whan Yoon, Jun Seo, Jaekyun Moon

Few-shot learning aims to handle previously unseen tasks using only a small amount of new training data. In preparing (or meta-training) a few-shot learner, however, massive labeled data are necessary. In the real world, unfortunately, labeled data are expensive and/or scarce. In this work, we propose a few-shot learner that can work well under the semi-supervised setting where a large portion of training data is unlabeled. Our method employs explicit task-conditioning in which unlabeled sample clustering for the current task takes place in a new projection space different from the embedding feature space. The conditioned clustering space is linearly constructed so as to quickly close the gap between the class centroids for the current task and the independent per-class reference vectors meta-trained across tasks. In a more general setting, our method introduces a concept of controlling the degree of task-conditioning for meta-learning: the amount of task-conditioning varies with the number of repetitive updates for the clustering space. During each update, the soft labels of the unlabeled samples estimated in the conditioned clustering space are used to update the class averages in the original embedded space, which in turn are used to reconstruct the clustering space. Extensive simulation results based on the miniImageNet and tieredImageNet datasets show state-of-the-art semi-supervised few-shot classification performance of the proposed method. Simulation results also indicate that the proposed task-adaptive clustering shows graceful degradation with a growing number of distractor samples, i.e., unlabeled samples coming from outside the candidate classes.
Knockoff-Inspired Feature Selection via Generative Models
Authors: Marco F. Duarte, Siwei Feng

We propose a feature selection algorithm for supervised learning inspired by the recently introduced 
knockoff framework for variable selection in statistical regression. While variable selection in statistics aims 
to distinguish between true and false predictors, feature selection in machine learning aims to reduce the 
dimensionality of the data while preserving the performance of the learning method. The knockoff framework 
has attracted significant interest due to its strong control of false discoveries while preserving predictive 
power. In contrast to the original approach and later variants that assume a given probabilistic model for the 
variables, our proposed approach relies on data-driven generative models that learn mappings from data 
space to a parametric space that characterizes the probability distribution of the data. Our approach 
requires only the availability of mappings from data space to a distribution in parametric space and from 
parametric space to a distribution in data space; thus, it can be integrated with multiple popular generative 
models from machine learning. We provide example knockoff designs using a variational autoencoder and 
a Gaussian process latent variable model. We also propose a knockoff score metric for a softmax classifier 
that accounts for the contribution of each feature and its knockoff during supervised learning. Experimental 
results with multiple benchmark datasets for feature selection showcase the advantages of our knockoff 
designs and the knockoff framework with respect to existing approaches.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Authors: Yiping Song, Rui Yan, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao

Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.
Self-Contrastive Learning
Authors: Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, SeungJong Noh, Se-Young Yun

This paper proposes a novel contrastive learning framework, called Self-Contrastive (SelfCon) Learning, that self-contrasts within multiple outputs from the different levels of a multi-exit network. SelfCon learning does not require additional augmented samples, which resolves the concerns of multi-viewed batch (e.g., high computational cost and generalization error). Furthermore, we prove that SelfCon loss guarantees the lower bound of label-conditional mutual information between the intermediate and the last feature. In our experiments including ImageNet-100, SelfCon surpasses cross-entropy and Supervised Contrastive (SupCon) learning without the need for a multi-viewed batch. We demonstrate that the success of SelfCon learning is related to the regularization effect associated with the single-view and sub-network.
Autoencoder-based Initialization for Recurrent Neural Networks with a Linear Memory
Authors: Antonio Carta, Alessandro Sperduti, Davide Bacciu

Orthogonal recurrent neural networks address the vanishing gradient problem by parameterizing the recurrent connections using an orthogonal matrix. This class of models is particularly effective to solve tasks that require the memorization of long sequences. We propose an alternative solution based on explicit memorization using linear autoencoders for sequences. We show how a recently proposed recurrent architecture, the Linear Memory Network, composed of a nonlinear feedforward layer and a separate linear recurrence, can be used to solve hard memorization tasks. We propose an initialization schema that sets the weights of a recurrent architecture to approximate a linear autoencoder of the input sequences, which can be found with a closed-form solution. The initialization schema can be easily adapted to any recurrent architecture.
    We argue that this approach is superior to a random orthogonal initialization due to the autoencoder, which allows the memorization of long sequences even before training. The empirical analysis show that our approach achieves competitive results against alternative orthogonal models, and the LSTM, on sequential MNIST, permuted MNIST and TIMIT.
Private Multi-Winner Voting For Machine Learning
Authors: Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang

Private multi-winner voting is the task of revealing k-hot binary vectors that satisfy a bounded differential privacy guarantee. This task has been understudied in the machine learning literature despite its prevalence in many domains such as healthcare. We propose three new privacy-preserving multi-label mechanisms: Binary, $\tau$, and Powerset voting. Binary voting operates independently per label through composition. $\tau$ voting bounds votes optimally in their $\ell_2$ norm. Powerset voting operates over the entire binary vector by viewing the possible outcomes as a power set. We theoretically analyze tradeoffs showing that Powerset voting requires strong correlations between labels to outperform Binary voting. We use these mechanisms to enable privacy-preserving multi-label learning by extending the canonical single-label technique: PATE. We empirically compare our techniques with DPSGD on large real-world healthcare data and standard multi-label benchmarks. We find that our techniques outperform all others in the centralized setting. We enable multi-label CaPC and show that our mechanisms can be used to collaboratively improve models in a multi-site (distributed) setting.
Training Provably Robust Models by Polyhedral Envelope Regularization
Authors: Chen Liu, Mathieu Salzmann, Sabine Süsstrunk

Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we use a linear approximation to bound model’s output given an input adversarial budget. This allows us to bound the adversary-free region in the data neighborhood by a polyhedral envelope and yields finer-grained certified robustness than existing methods. We further exploit this certifier to introduce a framework called polyhedral envelope regular- ization (PER), which encourages larger polyhedral envelopes and thus improves the provable robustness of the models. We demonstrate the flexibility and effectiveness of our framework on standard benchmarks; it applies to networks with general activation functions and obtains comparable or better robustness guarantees than state-of-the-art methods, with very little cost in clean accuracy, i.e., without over-regularizing the model.
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective
Authors: Motasem Alfarra, Adel Bibi, Hasan Abed Al Kader Hammoud, Mohamed Gaafar, Bernard Ghanem

This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations. We use tropical geometry, a new development in the area of algebraic geometry, to characterize the decision boundaries of a simple network of the form (Affine, ReLU, Affine). Our main finding is that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes. The generators of these zonotopes are functions of the network parameters. This geometric characterization provides new perspectives to three tasks. Specifically, we propose a new tropical perspective to the lottery ticket hypothesis, where we view the effect of different initializations on the tropical geometric representation of a network's decision boundaries. Moreover, we propose new tropical based optimization problems that directly influence the decision boundaries of the network for the tasks of network pruning (removing network parameters not contributing to the tropical geometric representation of the decision boundaries) and the generation of adversarial attacks.
Low Rank Training of Deep Neural Networks for Emerging Memory Technology
Authors: Albert Gural, Phillip Nadeau, Mehul Tikekar, Boris Murmann

The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge." However, this work has traditionally focused on neural network inference, rather than training, due to memory and compute limitations, especially in emerging non-volatile memory systems, where writes are energetically costly and reduce lifespan. Yet, the ability to train at the edge is becoming increasingly important as it enables applications such as real-time adaptability to device drift and environmental variation, user customization, and federated learning across devices. In this work, we address four key challenges for training on edge devices with non-volatile memory: low weight update density, weight quantization, low auxiliary memory, and online learning. We present a low-rank training scheme that addresses these four challenges while maintaining computational efficiency. We then demonstrate the technique on a representative convolutional neural network across several adaptation problems, where it out-performs standard SGD both in accuracy and in number of weight updates.
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks
Authors: John Lim, Fabian Monrose, Jan-Michael Frahm

Keystroke inference attacks are a form of side-channels attacks in which an attacker leverages various techniques to recover a user’s keystrokes as she inputs information into some display (for example, while sending a text message or entering her pin). Typically, these attacks leverage machine learning approaches, but assessing the realism of the threat space has lagged behind the pace of machine learning advancements, due in-part, to the challenges in curating large real-life datasets. This paper aims to overcome the challenge of having limited number of real data by introducing a video domain adaptation technique that is able to leverage synthetic data through supervised disentangled learning. Specifically, for a given domain, we decompose the observed data into two factors of variation: Style and Content. Doing so provides four learned representations: real-life style, synthetic style, real-life content and synthetic content. Then, we combine them into feature representations from all combinations of style-content pairings across domains, and train a model on these combined representations to classify the content (i.e., labels) of a given datapoint in the style of another domain. We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover. We show that our method prevents our model from overfitting to a small real-life training set, indicating that our method is an effective form of data augmentation. Code and data will be released after reviewal.
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
Authors: Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Recent works find that DNNs are  vulnerable to adversarial examples, whose changes from the benign ones are imperceptible and yet lead DNNs to make wrong predictions. One can find various adversarial examples for the same input to a DNN using different attack methods. In other words, there is a population of adversarial examples, instead of only one, for any input to a DNN. By explicitly modeling this adversarial population with a Gaussian distribution, we propose a new black-box attack called NATTACK. The adversarial attack is hence formalized as an optimization problem, which searches the mean of the Gaussian under the guidance of increasing the target DNN's prediction error. NATTACK achieves 100%  attack success rate  on six out of eleven recently published defense methods (and greater than 90% for four), all using the same algorithm. Such results are on par with or better than  powerful state-of-the-art white-box attacks. While the white-box attacks are often model-specific or defense-specific, the proposed black-box NATTACK is universally applicable to different defenses.
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation
Authors: Matt Angus, Krzysztof Czarnecki, Rick Salay

The detection of out of distribution samples for image classification has been widely researched. Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing the image to be out of distribution. This paper adapts state-of-the-art methods for detecting out of distribution images for image classification to the new task of detecting out of distribution pixels, which can localise the unusual objects. It further experimentally compares the adapted methods on two new datasets derived from existing semantic segmentation datasets using PSPNet and DeeplabV3+ architectures, as well as proposing a new metric for the task. The evaluation shows that the performance ranking of the compared methods does not transfer to the new task and every method performs significantly worse than their image-level counterparts.
Multi-Task Neural Processes
Authors: Jiayi Shen, Xiantong Zhen, Marcel Worring, Ling Shao

Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can encode contextual data in the network's function space, they offer a new way to model task relatedness in multi-task learning. To study its potential, we develop multi-task neural processes, a new variant of neural processes for multi-task learning. In particular, we propose to explore transferable knowledge from related tasks in the function space to provide inductive bias for improving each individual task. To do so, we derive the function priors in a hierarchical Bayesian inference framework, which enables each task to incorporate the shared knowledge provided by related tasks into its context of the prediction function. Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning. The proposed multi-task neural processes are capable of learning multiple tasks with limited labeled data and in the presence of domain shift. We perform extensive experimental evaluations on several benchmarks for the multi-task regression and classification tasks. The results demonstrate the effectiveness of multi-task neural processes in transferring useful knowledge among tasks for multi-task learning and superior performance in multi-task classification and brain image segmentation.