nils-herrmann/clean_scirepeval huggingface dataset:
Discovering Classification Rules for Interpretable Learning with Linear Programming
Authors: Hakan Akyuz, Ilker Birbil

Rules embody a set of if-then statements which include one or more conditions to classify a subset of samples in a dataset. In various applications such classification rules are considered to be interpretable by the decision makers. We introduce two new algorithms for interpretability and learning. Both algorithms take advantage of linear programming, and hence, they are scalable to large data sets. The first algorithm extracts rules for interpretation of trained models that are based on tree/rule ensembles. The second algorithm generates a set of classification rules through a column generation approach. The proposed algorithms return a set of rules along with their optimal weights indicating the importance of each rule for classification. Moreover, our algorithms allow assigning cost coefficients, which could relate to different attributes of the rules, such as; rule lengths, estimator weights, number of false negatives, and so on. Thus, the decision makers can adjust these coefficients to divert the training process and obtain a set of rules that are more appealing for their needs. We have tested the performances of both algorithms on a collection of datasets and presented a case study to elaborate on optimal rule weights. Our results show that a good compromise between interpretability and accuracy can be obtained by the proposed algorithms.
Improved Detection of Adversarial Attacks via Penetration Distortion Maximization
Authors: Shai Rozenberg, Gal Elidan, Ran El-Yaniv

This paper is concerned with the defense of deep models against adversarial at-
tacks. We develop an adversarial detection method, which is inspired by the cer-
tificate defense approach, and captures the idea of separating class clusters in the
embedding space so as to increase the margin. The resulting defense is intuitive,
effective, scalable and can be integrated into any given neural classification model.
Our method demonstrates state-of-the-art detection performance under all threat
models.
Generative Modeling with Optimal Transport Maps
Authors: Litu Rout, Alexander Korotin, Evgeny Burnaev

With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as the loss for training GANs. In contrast to this approach, we show that the OT map itself can be used as a generative model, providing comparable performance. Previous analogous approaches consider OT maps as generative models only in the latent spaces due to their poor performance in the original high-dimensional ambient space. In contrast, we apply OT maps directly in the ambient space, e.g., a space of high-dimensional images. First, we derive a min-max optimization algorithm to efficiently compute OT maps for the quadratic cost (Wasserstein-2 distance). Next, we extend the approach to the case when the input and output distributions are located in the spaces of different dimensions and derive error bounds for the computed OT map. We evaluate the algorithm on image generation and unpaired image restoration tasks. In particular, we consider denoising, colorization, and inpainting, where the optimality of the restoration map is a desired attribute, since the output (restored) image is expected to be close to the input (degraded) one.
How does Contrastive Pre-training Connect Disparate Domains?
Authors: Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang

Pre-training on massive unlabeled datasets greatly improves accuracy under distribution shifts. As a first step toward understanding this, we study a popular pre-training method, contrastive learning, in the unsupervised domain adaptation (UDA) setting where we only have labeled data from a source domain and unlabeled data from a target domain. We begin by showing on 4 benchmark datasets that out-of-the-box contrastive pre-training (even without large-scale unlabeled data) is competitive with other UDA methods. Intuitions from classical UDA methods such as domain adversarial training focus on bringing the domains together in feature space to improve generalization from source to target. Surprisingly, we find that contrastive pre-training learns features that are very far apart between the source and target domains. How then does contrastive learning improve robustness to distribution shift? We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart. We propose a new measure of connectivity ---the relative connection strengths between same and different classes across domains---that governs the success of contrastive pre-training for domain adaptation in a simple example and strongly correlates with our results on benchmark datasets.
TRL: Discriminative Hints for Scalable Reverse Curriculum Learning
Authors: Chen Wang, Xiangyu Chen, Zelin Ye, Jialu Wang, Ziruo Cai, Shixiang Gu, Cewu Lu

Deep reinforcement learning algorithms have proven successful in a variety of domains. However, tasks with sparse rewards remain challenging when the state space is large. Goal-oriented tasks are among the most typical problems in this domain, where a reward can only be received when the final goal is accomplished. In this work, we propose a potential solution to such problems with the introduction of an experience-based tendency reward mechanism, which provides the agent with additional hints based on a discriminative learning on past experiences during an automated reverse curriculum. This mechanism not only provides dense additional learning signals on what states lead to success, but also allows the agent to retain only this tendency reward instead of the whole histories of experience during multi-phase curriculum learning. We extensively study the advantages of our method on the standard sparse reward domains like Maze and Super Mario Bros and show that our method performs more efficiently and robustly than prior approaches in tasks with long time horizons and large state space. In addition, we demonstrate that using an optional keyframe scheme with very small quantity of key states, our approach can solve difficult robot manipulation challenges directly from perception and sparse rewards.
Weighted Training for Cross-Task Learning
Authors: Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, Weijie J Su

In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tuning, and enjoys non-asymptotic learning-theoretic guarantees. The effectiveness of TAWT is corroborated through extensive experiments with BERT on four sequence tagging tasks in natural language processing (NLP), including part-of-speech (PoS) tagging, chunking, predicate detection, and named entity recognition (NER). As a byproduct, the proposed representation-based task distance allows one to reason in a theoretically principled way about several critical aspects of cross-task learning, such as the choice of the source data and the impact of fine-tuning.
textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR
Authors: Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schuetze

We address two challenges of probabilistic topic modelling in order to better estimate
the probability of a word in a given context, i.e., P(wordjcontext) : (1) No
Language  Probabilistic topic models ignore word order by
summarizing a given context as a “bag-of-word” and consequently the semantics
of words in the context is lost. In this work, we incorporate language structure
by combining a neural autoregressive topic model (TM) with a LSTM based language
model (LSTM-LM) in a single probabilistic framework. The LSTM-LM
learns a vector-space representation of each word by accounting for word order
in local collocation patterns, while the TM simultaneously learns a latent representation
from the entire document. In addition, the LSTM-LM models complex
characteristics of language (e.g., syntax and semantics), while the TM discovers
the underlying thematic structure in a collection of documents. We unite two complementary
paradigms of learning the meaning of word occurrences by combining
a topic model and a language model in a unified probabilistic framework, named
as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of documents:
In settings with a small number of word occurrences (i.e., lack of context)
in short text or data sparsity in a corpus of few documents, the application of TMs
is challenging. We address this challenge by incorporating external knowledge
into neural autoregressive topic models via a language modelling approach: we
use word embeddings as input of a LSTM-LM with the aim to improve the wordtopic
mapping on a smaller and/or short-text corpus. The proposed DocNADE
extension is named as ctx-DocNADEe.

We present novel neural autoregressive topic model variants coupled with neural
language models and embeddings priors that consistently outperform state-of-theart
generative topic models in terms of generalization (perplexity), interpretability
(topic coherence) and applicability (retrieval and classification) over 6 long-text
and 8 short-text datasets from diverse domains.
Prioritized Level Replay
Authors: Minqi Jiang, Edward Grefenstette, Tim Rocktäschel

Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents. Every level in such an environment is algorithmically created, thereby exhibiting a unique configuration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions. Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents. While prior work samples training levels in a direct way (e.g.~uniformly) for the agent to learn from, we investigate the hypothesis that different levels provide different learning progress for an agent at specific times during training. We introduce Prioritized Level Replay, a general framework for estimating the future learning potential of a level given the current state of the agent's policy. We find that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level's future learning potential when the agent replays (that is, revisits) that level to generate entirely new episodes of experiences from it. We report significantly improved sample-efficiency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments. Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels.
Adversarially Robust Conformal Prediction
Authors: Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano

Conformal prediction is a model-agnostic tool for constructing prediction sets that are valid under the common i.i.d. assumption, which has been applied to quantify the prediction uncertainty of deep net classifiers. In this paper, we generalize this framework to the case where adversaries exist during inference time, under which the i.i.d. assumption is grossly violated. By combining conformal prediction with randomized smoothing, our proposed method forms a prediction set with finite-sample coverage guarantee that holds for any data distribution with $\ell_2$-norm bounded adversarial noise, generated by any adversarial attack algorithm. The core idea is to bound the Lipschitz constant of the non-conformity score by smoothing it with Gaussian noise and leverage this knowledge to account for the effect of the unknown adversarial perturbation. We demonstrate the necessity of our method in the adversarial setting and the validity of our theoretical guarantee on three widely used benchmark data sets: CIFAR10, CIFAR100, and ImageNet.
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Authors: Seanie Lee, Hae Beom Lee, Juho Lee, Sung Ju Hwang

Multilingual models jointly pretrained on multiple languages have achieved remarkable performance on various multilingual downstream tasks. Moreover, models finetuned on a single monolingual downstream task have shown to generalize to unseen languages. In this paper, we first show that it is crucial for those tasks to align gradients between them in order to maximize knowledge transfer while minimizing negative transfer. Despite its importance, the existing methods for gradient alignment either have a completely different purpose, ignore inter-task alignment, or aim to solve continual learning problems in rather inefficient ways. As a result of the misaligned gradients between tasks, the model suffers from severe negative transfer in the form of catastrophic forgetting of the knowledge acquired from the pretraining. To overcome the limitations, we propose a simple yet effective method that can efficiently align gradients between tasks. Specifically, we perform each inner-optimization by sequentially sampling batches from all the tasks, followed by a Reptile outer update. Thanks to the gradients aligned between tasks by our method, the model becomes less vulnerable to negative transfer and catastrophic forgetting. We extensively validate our method on various multi-task learning and zero-shot cross-lingual transfer tasks, where our method largely outperforms all the relevant baselines we consider.
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach
Authors: Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely  when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiveness simultaneously, leading to redundant explanations. We propose the variational information bottleneck for interpretation, VIBI, a system-agnostic interpretable method that provides a brief but comprehensive explanation. VIBI adopts an information theoretic principle, information bottleneck principle, as a criterion for finding such explanations. For each instance, VIBI selects key features that are maximally compressed about an input (briefness), and informative about a decision made by a black-box system on that input (comprehensive). We evaluate VIBI on three datasets and compare with state-of-the-art interpretable machine learning methods in terms of both interpretability and fidelity evaluated by human and quantitative metrics.
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning
Authors: Shentao Yang, Zhendong Wang, Huangjie Zheng, Mingyuan Zhou

Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective agents, we propose a framework that supports learning a flexible and well-regularized policy, which consists of a fully implicit policy and a regularization through the state-action visitation frequency induced by the current policy and that induced by the data-collecting behavior policy. We theoretically show the equivalence between policy-matching and state-action-visitation matching, and thus the compatibility of many prior work with our framework. An effective instantiation of our framework through the GAN structure is provided, together with some techniques to explicitly smooth the state-action mapping for robust generalization beyond the static dataset. Extensive experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
Learning Abstract Models for Long-Horizon Exploration
Authors: Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang

In high-dimensional reinforcement learning settings with sparse rewards, performing
effective exploration to even obtain any reward signal is an open challenge.
While model-based approaches hold promise of better exploration via planning, it
is extremely difficult to learn a reliable enough Markov Decision Process (MDP)
in high dimensions (e.g., over 10^100 states). In this paper, we propose learning
an abstract MDP over a much smaller number of states (e.g., 10^5), which we can
plan over for effective exploration. We assume we have an abstraction function
that maps concrete states (e.g., raw pixels) to abstract states (e.g., agent position,
ignoring other objects). In our approach, a manager maintains an abstract
MDP over a subset of the abstract states, which grows monotonically through targeted
exploration (possible due to the abstract MDP). Concurrently, we learn a
worker policy to travel between abstract states; the worker deals with the messiness
of concrete states and presents a clean abstraction to the manager. On three of
the hardest games from the Arcade Learning Environment (Montezuma's,
Pitfall!, and Private Eye), our approach outperforms the previous
state-of-the-art by over a factor of 2 in each game. In Pitfall!, our approach is
the first to achieve superhuman performance without demonstrations.
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks
Authors: Dhanush Dharmaretnam, Chris Foster, Alona Fyshe

As deep neural net architectures minimize loss, they build up information in a hierarchy of learned representations that ultimately serve their final goal.  Different architectures tackle this problem in slightly different ways, but all models aim to  create representational spaces that accumulate information through the depth of the network.  Here we build on previous work that indicated that two very different model classes trained on two very different tasks actually build knowledge representations that have similar underlying representations.  Namely, we compare word embeddings from SkipGram (trained to predict co-occurring words) to several CNN architectures (trained for image classification) in order to understand how this accumulation of knowledge behaves in CNNs.  We improve upon previous work by including 5 times more ImageNet classes in our experiments, and further expand the scope of the analyses to include a network trained on CIFAR-100.  We  characterize network behavior in pretrained models, and also during training, misclassification, and adversarial attack.  Our work illustrates the power of using one model to explore another, gives new insights for CNN models, and provides a framework for others to perform similar analyses when developing new architectures.
STORM: Sketch Toward Online Risk Minimization
Authors: Gaurav Gupta, Benjamin Coleman, John Chen, Anshumali Shrivastava

Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression and classification models.
The growing concerns about the high energy cost of training and the increased prevalence of massive streaming datasets have led many ML practitioners to look for approximate ERM models that can achieve low cost on memory and latency for training.
To this end, we propose STORM, an online sketching-based method for empirical risk minimization. STORM compresses a data stream into a tiny array of integer counters. This sketch is sufficient to estimate a variety of surrogate losses over the original dataset. We provide rigorous theoretical analysis and show that STORM can estimate a carefully chosen surrogate loss for regularized least-squares regression and a margin loss for classification. 
We perform an exhaustive experimental comparison for regression and classification training on real-world datasets, achieving an approximate solution with a size even less than a data sample.
Graph Convolutional Networks via Adaptive Filter Banks
Authors: Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

Graph convolutional networks have been a powerful tool in representation learning of networked data. However, most architectures of message passing graph convolutional networks (MPGCNs) are limited as they employ a single message passing strategy and typically focus on low-frequency information,  especially when graph features or signals are heterogeneous in different dimensions.  Then, existing spectral graph convolutional operators lack a proper sharing scheme between filters, which may result in overfitting problems with numerous parameters. In this paper, we present a novel graph convolution operator, termed BankGCN, which extends the capabilities of MPGCNs beyond single `low-pass' features and simplifies spectral methods with a carefully designed sharing scheme between filters. BankGCN decomposes multi-channel signals on arbitrary graphs into subspaces and shares adaptive filters to represent information in each subspace. The filters of all subspaces differ in frequency response and together form a filter bank. The filter bank and the signal decomposition permit to adaptively capture diverse spectral characteristics of graph data for target applications with a compact architecture. We finally show through extensive experiments that BankGCN achieves excellent performance on a collection of benchmark graph datasets.
Capsule Graph Neural Network
Authors: Zhang Xinyi, Lihui Chen

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings.

Inspired by the Capsule Neural Network (CapsNet), we propose the Capsule Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms. By extracting node features in the form of capsules, routing mechanism can be utilized to capture important information at the graph level. As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects. The attention module incorporated in CapsGNN is used to tackle graphs with various sizes which also enables the model to focus on critical parts of the graphs.

Our extensive evaluations with 10 graph-structured datasets demonstrate that CapsGNN has a powerful mechanism that operates to capture macroscopic properties of the whole graph by data-driven. It outperforms other SOTA techniques on several graph classification tasks, by virtue of the new instrument.