nils-herrmann/clean_scirepeval huggingface dataset:
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control
Authors: Seo Taek Kong, Soomin Jeon, Jaewon Lee, Hong-Seok Lee, Kyu-Hwan Jung

Reducing the sample complexity associated with deep learning (DL) remains one of the most important problems in both theory and practice since its advent. Semi-supervised learning (SSL) tackles this task by leveraging unlabeled instances which are usually more accessible than their labeled counterparts. Active learning (AL) directly seeks to reduce the sample complexity by training a classification network and querying unlabeled instances to be annotated by a human-in-the-loop. Under relatively strict settings, it has been shown that both SSL and AL can theoretically achieve the same performance of fully-supervised learning (SL) using far less labeled samples. While empirical works have shown that SSL can attain this benefit in practice, DL-based AL algorithms have yet to show their success to the extent achieved by SSL. Given the accessible pool of unlabeled instances in pool-based AL, we argue that the annotation efficiency brought by AL algorithms that seek diversity on labeled samples can be improved upon when using SSL as the training scheme. Equipped with a few theoretical insights, we designed an AL algorithm that rather focuses on controlling the convergence rate of a classification network by actively querying instances to improve the rate of convergence upon inclusion to the labeled set. We name this AL scheme convergence rate control (CRC), and our experiments show that a deep neural network trained using a combination of CRC and a recently proposed SSL algorithm can quickly achieve high performance using far less labeled samples than SL. In contrast to a few works combining independently developed AL and SSL (ASSL) algorithms, our method is a natural fit to ASSL, and we hope our work can catalyze research combining AL and SSL as opposed to an exclusion of either.
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations
Authors: Ruicheng Xian, Heng Ji, Han Zhao

Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from non-parallel texts and enabling zero-shot downstream transfer. While their success is often attributed to shared representations, quantitative analyses are limited. Towards a better understanding, through empirical analyses, we show that the invariance of feature representations across languages—an effect of shared representations—strongly correlates with transfer performance. We also observe that distributional shifts in class priors between source and target language task data negatively affect performance, a largely overlooked issue that could cause negative transfer with existing unsupervised approaches. Based on these findings, we propose and evaluate a method for unsupervised transfer, called importance-weighted domain alignment (IWDA), that performs representation alignment with prior shift estimation and correction using unlabeled target language task data. Experiments demonstrate its superiority under large prior shifts, and show further performance gains when combined with existing semi-supervised learning techniques.
Semantic Pruning for Single Class Interpretability
Authors: Kamila Abdiyeva, Martin Lukac, Kanat Alimanov

Convolutional Neural Networks (CNN) have achieved state-of-the-art performance in different computer vision tasks, but at a price of being computationally and power intensive. At the same time, only a few attempts were made toward a deeper understanding of CNNs. In this work, we propose to use semantic pruning technique toward not only CNN optimization but also as a way toward getting some insight information on convolutional filters correlation and interference. We start with a pre-trained network and prune it until it behaves as a single class classifier for a selected class. Unlike the more traditional approaches which apply retraining to the pruned CNN, the proposed semantic pruning  does not use retraining. Conducted experiments showed that a) for each class there is a pruning ration which allows removing filters with either an increase or no loss of classification accuracy, b) pruning can improve the interference between filters used for classification of different classes c) effect between classification accuracy and correlation between pruned filters groups specific for different classes.
Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation
Authors: Tom Hosking, Sebastian Riedel

Question generation is an important task for improving our ability to process natural language data, with additional challenges over other sequence transformation tasks. Recent approaches use modifications to a Seq2Seq architecture inspired by advances in machine translation, but unlike translation the input and output vocabularies overlap significantly, and there are many different valid questions for each input. Approaches using copy mechanisms and reinforcement learning have shown promising results, but there are ambiguities in the exact implementation that have not yet been investigated. We show that by removing inductive bias from the model and allowing the choice of generation path to become latent, we achieve substantial improvements over implementations biased with both naive and smart heuristics. We perform a human evaluation to confirm these findings. We show that although policy gradient methods may be used to decouple training from the ground truth and optimise directly for quality metrics that have previously been assumed to be good choices, these objectives are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source. Finally, we show that an adversarial objective learned directly from the ground truth data is not able to generate a useful training signal.
PAC-Bayes Information Bottleneck
Authors: Zifeng Wang, Shao-Lun Huang, Ercan Engin Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NNs generalization based on the PAC-Bayes theorem. However, no solution of IIW has ever been provided, which builds a barrier for further investigation of the IIW's property and its potential in practical deep learning. In this paper, we propose an algorithm for the efficient approximation of IIW. Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB. From PIB, we can empirically identify the fitting to compressing phase transition during NNs' training and the concrete connection between the IIW compression and the generalization. Besides, we verify that IIW is able to explain NNs in broad cases, e.g., varying batch sizes, over-parameterization, and noisy labels. Moreover, we propose an MCMC-based algorithm to sample from the optimal weight posterior characterized by PIB, which fulfills the potential of IIW in enhancing NNs in practice.
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
Authors: Marin Toromanoff, Emilie Wirbel, Fabien Moutarde

Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance. In this work, we discuss the difficulties of comparing different agents trained on ALE. In order to take a step further towards reproducible and comparable DRL, we introduce SABER, a Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our methodology extends previous recommendations and contains a complete set of environment parameters as well as train and test procedures. We then use SABER to evaluate the current state of the art, Rainbow. Furthermore, we introduce a human world records baseline, and argue that previous claims of expert or superhuman performance of DRL might not be accurate. Finally, we propose Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading to new state-of-the-art performance. Source code is available for reproducibility.
Geometric Transformers for Protein Interface Contact Prediction
Authors: Alex Morehead, Chen Chen, Jianlin Cheng

Computational methods for predicting the interface contacts between proteins come highly sought after for drug discovery as they can significantly advance the accuracy of alternative approaches, such as protein-protein docking, protein function analysis tools, and other computational methods for protein bioinformatics. In this work, we present the Geometric Transformer, a novel geometry-evolving graph transformer for rotation and translation-invariant protein interface contact prediction, packaged within DeepInteract, an end-to-end prediction pipeline. DeepInteract predicts partner-specific protein interface contacts (i.e., inter-protein residue-residue contacts) given the 3D tertiary structures of two proteins as input. In rigorous benchmarks, DeepInteract, on challenging protein complex targets from the 13th and 14th CASP-CAPRI experiments as well as Docking Benchmark 5, achieves 14% and 1.1% top L/5 precision (L: length of a protein unit in a complex), respectively. In doing so, DeepInteract, with the Geometric Transformer as its graph-based backbone, outperforms existing methods for interface contact prediction in addition to other graph-based neural network backbones compatible with DeepInteract, thereby validating the effectiveness of the Geometric Transformer for learning rich relational-geometric features for downstream tasks on 3D protein structures.
OFFER PERSONALIZATION USING TEMPORAL CONVOLUTION NETWORK AND OPTIMIZATION
Authors: Ankur Verma

Lately, personalized marketing has become important for retail/e-retail firms due to significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers, and hence, rolling out optimal offers has become imperative to maintain balance between number of transactions and profit. In this paper, we propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting. To optimize offer, we first build a generalized non-linear model using Temporal Convolutional Network to predict the item purchase probability at consumer level for the given time period. Secondly, we establish the functional relationship between historical offer values and purchase probabilities obtained from the model, which is then used to estimate offer-elasticity of purchase probability at consumer item granularity. Finally, using estimated elasticities, we optimize offer values using constraint based optimization technique. This paper describes our detailed methodology and presents the results of modelling and optimization across categories.
Combining Model-based and Model-free RL via Multi-step Control Variates
Authors: Tong Che, Yuchen Lu, George Tucker, Surya Bhupatiraju, Shane Gu, Sergey Levine, Yoshua Bengio

Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance. Model-based RL algorithms are sample-efficient on the other hand, while learning accurate global models of complex dynamic environments has turned out to be tricky in practice, which leads to the unsatisfactory performance of the learned policies. In this work, we combine the sample-efficiency of model-based algorithms and the accuracy of model-free algorithms. We leverage multi-step neural network based predictive models by embedding real trajectories into imaginary rollouts of the model, and use the imaginary cumulative rewards as control variates for model-free algorithms. In this way, we achieved the strengths of both sides and derived an estimator which is not only sample-efficient, but also unbiased and of very low variance. We present our evaluation on the MuJoCo and OpenAI Gym benchmarks.
META LEARNING SHARED HIERARCHIES
Authors: Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman

We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps. Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.
Adversarial Environment Generation for Learning  to Navigate the Web
Authors: Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, Aleksandra Faust

Learning to autonomously navigate the web is a difficult sequential decision making task. The state and action spaces are large and combinatorial in nature, and successful navigation may require traversing several partially-observed pages. One of the bottlenecks of training web navigation agents is providing a learnable curriculum of training environments that can cover the large variety of real-world websites. Therefore, we propose using Adversarial Environment Generation (AEG) to generate challenging web environments in which to train reinforcement learning (RL) agents. We introduce a new benchmarking environment, gMiniWoB, which enables an RL adversary to use compositional primitives to learn to generate complex websites. To train the adversary, we present a new decoder-like architecture that can directly control the difficulty of the environment, and a new training technique Flexible b-PAIRED. Flexible b-PAIRED jointly trains the adversary and a population of navigator agents and incentivizes the adversary to generate ”just-the-right-challenge” environments by simultaneously learning two policies encoded in the adversary’s architecture. First, for its environment complexity choice (difficulty budget), the adversary is rewarded with the performance of the best-performing agent in the population. Second, for selecting the design elements the adversary learns to maximize the regret using the difference in capabilities of navigator agents in population (flexible regret). The results show that the navigator agent trained with Flexible b-PAIRED generalizes to new environments, significantly outperforms competitive automatic curriculum generation baselines—including a state-of-the-art RL web navigation approach and prior methods for minimax regret AEG—on a set of challenging unseen test environments that are order of magnitude more complex than the previous benchmarks. The navigator agent achieves more than 75% success rate on all tasks, yielding 4x higher success rate that the strongest baseline.
Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice
Authors: Jicong Fan, Rui Chen, Chris Ding

This paper presents a novel recommendation method called neuron-enhanced autoencoder based collaborative filtering (NE-AECF). The method uses an additional neural network to enhance the reconstruction capability of autoencoder. Different from the main neural network implemented in a layer-wise manner, the additional neural network is implemented in an element-wise manner. They are trained simultaneously to construct an enhanced autoencoder of which the activation function in the output layer is learned adaptively to approximate possibly complicated response functions in real data. We provide theoretical analysis for NE-AECF to investigate the generalization ability of autoencoder and deep learning in collaborative filtering. We prove that the element-wise neural network is able to reduce the upper bound of the prediction error for the unknown ratings, the data sparsity is not problematic but useful, and the prediction performance is closely related to the difference between the number of users and the number of items.
Numerical results show that our NE-AECF has promising performance on a few benchmark datasets.
What are effective labels for augmented data? Improving robustness with AutoLabel
Authors: Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed Chi, Alex Beutel

A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean data and what is the appropriate label is less clear. Despite this, most existing work simply reuses the original label from the clean data, and the choice of label accompanying the augmented data is relatively less explored. In this paper, we propose AutoLabel to automatically learn the labels for augmented data, based on the distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We show that AutoLabel is a generic framework that can be easily applied to existing data augmentation methods, including AugMix, mixup, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel can improve models' accuracy and calibration performance, especially under distributional shift. Additionally, we demonstrate that AutoLabel can help adversarial training by bridging the gap between clean accuracy and adversarial robustness.
Directing Generative Networks with Weighted Maximum Mean Discrepancy
Authors: Maurice Diesendruck, Guy W. Cole, Sinead Williamson

The maximum mean discrepancy (MMD) between two probability measures P
and Q is a metric that is zero if and only if all moments of the two measures
are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples
from P and Q, Gretton et al. (2012) show that we can construct an unbiased
estimator for the square of the MMD between the two distributions. If P is a
distribution of interest and Q is the distribution implied by a generative neural
network with stochastic inputs, we can use this estimator to train our neural network.
However, in practice we do not always have i.i.d. samples from our target
of interest. Data sets often exhibit biases—for example, under-representation of
certain demographics—and if we ignore this fact our machine learning algorithms
will propagate these biases. Alternatively, it may be useful to assume our data has
been gathered via a biased sample selection mechanism in order to manipulate
properties of the estimating distribution Q.
In this paper, we construct an estimator for the MMD between P and Q when we
only have access to P via some biased sample selection mechanism, and suggest
methods for estimating this sample selection mechanism when it is not already
known. We show that this estimator can be used to train generative neural networks
on a biased data sample, to give a simulator that reverses the effect of that
bias.
Proactive Sequence Generator via Knowledge Acquisition
Authors: Qing Sun, James Cross, Dmitriy Genzel

Sequence-to-sequence models such as transformers, which are now being used in a wide variety of NLP tasks, typically need to have very high capacity in order to perform well. Unfortunately, in production, memory size and inference speed are all strictly constrained.  To address this problem, Knowledge Distillation (KD), a technique to train small models to mimic larger pre-trained models, has drawn lots of attention.  The KD approach basically attempts to maximize recall, i.e., ranking Top-k”tokens in teacher models as higher as possible, however, whereas precision is more important for sequence generation  because of exposure bias. Motivated by this, we develop Knowledge Acquisition (KA) where student models receive log q(y_t|y_{<t},x) as rewards when producing the next token y_t given previous tokens y_{<t} and the source sentence x.   We demonstrate the effectiveness of our approach on WMT’17 De-En and IWSLT’15 Th-En translation tasks, with experimental results showing that our approach gains +0.7-1.1 BLEU score compared to token-level knowledge distillation.
Oblique Decision Trees from Derivatives of ReLU Networks
Authors: Guang-He Lee, Tommi S. Jaakkola

We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al., 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.
Learning Curves for Deep Neural Networks: A field theory perspective
Authors: Omry Cohen, Or Malka, Zohar Ringel

A series of recent works established a rigorous correspondence between very wide deep neural networks (DNNs), trained in a particular manner, and noiseless Bayesian Inference with a certain Gaussian Process (GP) known as the Neural Tangent Kernel (NTK). Here we extend a known field-theory formalism for GP inference to get a detailed understanding of learning-curves in DNNs trained in the regime of this correspondence (NTK regime). In particular, a renormalization-group approach is used to show that noiseless GP inference using NTK, which lacks a good analytical handle, can be well approximated by noisy GP inference on a related kernel we call the renormalized NTK. Following this, a perturbation-theory analysis is carried in one over the dataset-size yielding analytical expressions for the (fixed-teacher/fixed-target) leading and sub-leading asymptotics of the learning curves. At least for uniform datasets, a coherent picture emerges wherein fully-connected DNNs have a strong implicit bias towards functions which are low order polynomials of the input.