OpenRaiser/Intern-Atlas huggingface dataset:
Learning hidden features for contextual bandits
Authors: Huazheng Wang, Qingyun Wu, Hongning Wang
Venue: Conference CIKM 2016
Year: 2016

Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. Most contextual bandit algorithms simply assume the learner would have access to the entire set of features, which govern the generation of payoffs from a user to an item. However, in practice it is challenging to exhaust all relevant features ahead of time, and oftentimes due to privacy or sampling constraints many factors are unobservable to the algorithm. Failing to model such hidden factors leads a system to make constantly suboptimal predictions. In this paper, we propose to learn the hidden features for contextual bandit algorithms. Hidden features are explicitly introduced in our reward generation assumption, in addition to the observable contextual features. A scalable bandit algorithm is achieved via coordinate descent, in which closed form solutions exist at each iteration for both hidden features and bandit parameters. Most importantly, we rigorously prove that the developed contextual bandit algorithm achieves a sublinear upper regret bound with high probability, and a linear regret is inevitable if one fails to model such hidden features. Extensive experimentation on both simulations and large-scale real-world datasets verified the advantages of the proposed algorithm compared with several state-of-the-art contextual bandit algorithms and existing ad-hoc combinations between bandit algorithms and matrix factorization methods.
Copula guided neural topic modelling for short texts
Authors: Lihui Lin, Hongyu Jiang, Yanghui Rao
Venue: Conference SIGIR 2020
Year: 2020

Extracting the topical information from documents is important for public opinion analysis, text classification, and information retrieval tasks. Compared with identifying a wide variety of topics from long documents, it is challenging to generate a concentrated topic distribution for each short message. Although this problem can be tackled by adjusting the hyper-parameters in traditional topic models such as Latent Dirichlet Allocation, it remains an open problem in neural topic modelling. In this paper, we focus on adapting the popular Auto-Encoding Variational Bayes based neural topic models to short texts, by exploring the Archimedean copulas to guide the estimated topic distributions derived from linear projected samples of re-parameterized posterior distributions. Experimental results show the superiority of our method when compared with existing neural topic models in terms of perplexity, topic coherence, and classification accuracy.
Sparsemax and relaxed wasserstein for topic sparsity
Authors: Tianyi Lin, Zhiyue Hu, Xin Guo
Venue: Conference WSDM 2019
Year: 2019

Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a narrow range of terms instead of a wide coverage of the vocabulary. Understanding this topic sparsity is especially important for analyzing user-generated web content and social media, which are featured in the form of extremely short posts and discussions. As topic sparsity of individual documents in online social media increases, so does the difficulty of analyzing the online text sources using traditional methods. In this paper, we propose two novel neural models by providing sparse posterior distributions over topics based on the Gaussian sparsemax construction, enabling efficient training by stochastic backpropagation. We construct an inference network conditioned on the input data and infer the variational distribution with the relaxed Wasserstein (RW) divergence. Unlike existing works based on Gaussian softmax construction and Kullback-Leibler (KL) divergence, our approaches can identify latent topic sparsity with training stability, predictive performance, and topic coherence. Experiments on different genres of large text corpora have demonstrated the effectiveness of our models as they outperform both probabilistic and neural methods.
Asymptotics of wide networks from Feynman diagrams
Authors: Ethan Dyer, Guy Gur-Ari
Venue: Conference ICLR 2020
Year: 2020

Understanding the asymptotic behavior of wide networks is of considerable interest. In this work, we present a general method for analyzing this large width behavior. The method is an adaptation of Feynman diagrams, a standard tool for computing multivariate Gaussian integrals. We apply our method to study training dynamics, improving existing bounds and deriving new results on wide network evolution during stochastic gradient descent. Going beyond the strict large width limit, we present closed-form expressions for higher-order terms governing wide network training, and test these predictions empirically.
Asymmetric minwise hashing for indexing binary inner products and set containment
Authors: Anshumali Shrivastava, Ping Li
Venue: Conference WWW 2015
Year: 2015

Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards smaller sets, which adversely affects its performance in applications where such a penalization is not desirable. In this paper, we propose asymmetric minwise hashing ({\em MH-ALSH}), to provide a solution to this well-known problem. The new scheme utilizes asymmetric transformations to cancel the bias of traditional minhash towards smaller sets, making the final ``collision probability'' monotonic in the inner product. Our theoretical comparisons show that, for the task of retrieving with binary inner products, asymmetric minhash is provably better than traditional minhash and other recently proposed hashing algorithms for general inner products. Thus, we obtain an algorithmic improvement over existing approaches in the literature. Experimental evaluations on four publicly available high-dimensional datasets validate our claims. The proposed scheme outperforms, often significantly, other hashing algorithms on the task of near neighbor retrieval with set containment. Our proposal is simple and easy to implement in practice.
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
Authors: Jiayuan Mao, Chuang Gan 0001, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu 0001
Venue: Conference ICLR 2019
Year: 2019

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.
Semantic structure from motion
Authors: Sid Ying-Ze Bao, Silvio Savarese
Venue: Conference CVPR 2011
Year: 2011

Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this paper, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in the scene (Fig. 1). These properties allow us to recover not only the structure and motion but also the 3D locations, poses, and categories of objects in the scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-point-based SFM algorithms, iii) improve object detections given multiple un-calibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets - LiDAR cars, street-view pedestrians, and Kinect office desktop - verify our theoretical claims.
Model-based overlapping clustering
Authors: Arindam Banerjee, Chase Krumpelman, Joydeep Ghosh, Sugato Basu, Raymond J. Mooney
Venue: Conference KDD 2005
Year: 2005

While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters have come from work on analysis of biological datasets. In this paper, we interpret an overlapping clustering model proposed by Segal et al. [23] as a generalization of Gaussian mixture models, and we extend it to an overlapping clustering model based on mixtures of any regular exponential family distribution and the corresponding Bregman divergence. We provide the necessary algorithm modifications for this extension, and present results on synthetic data as well as subsets of 20-Newsgroups and EachMovie datasets.
Adaspeech: Adaptive text to speech for custom voice
Authors: Mingjian Chen, Xu Tan 0003, Bohan Li 0003, Yanqing Liu, Tao Qin 0001, Sheng Zhao 0002, Tie-Yan Liu
Venue: Conference ICLR 2021
Year: 2021

Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech data. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model needs to handle diverse acoustic conditions that could be very different from source speech data, and 2) to support a large number of customers, the adaptation parameters need to be small enough for each target speaker to reduce memory usage while maintaining high voice quality. In this work, we propose AdaSpeech, an adaptive TTS system for high-quality and efficient customization of new voices. We design several techniques in AdaSpeech to address the two challenges in custom voice: 1) To handle different acoustic conditions, we use two acoustic encoders to extract an utterance-level vector and a sequence of phoneme-level vectors from the target speech during training; in inference, we extract the utterance-level vector from a reference speech and use an acoustic predictor to predict the phoneme-level vectors. 2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation. We pre-train the source TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets (with different acoustic conditions from LibriTTS) with few adaptation data, e.g., 20 sentences, about 1 minute speech. Experiment results show that AdaSpeech achieves much better adaptation quality than baseline methods, with only about 5K specific parameters for each speaker, which demonstrates its effectiveness for custom voice. Audio samples are available at https://speechresearch.github.io/adaspeech/.
Variance Reduction for Reinforcement Learning in Input-Driven Environments
Authors: Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh
Venue: Conference ICLR 2019
Year: 2019

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.
Learning to selectively update state neurons in recurrent networks
Authors: Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner
Venue: Conference CIKM 2020
Year: 2020

Recurrent Neural Networks (RNNs) are the state-of-the-art approach to sequential learning. However, standard RNNs use the same amount of computation to generate their hidden states at each timestep, regardless of the input data. Recent works have begun to tackle this rigid assumption by imposing a priori-determined patterns for updating the states at each step. These approaches could lend insights into the dynamics of RNNs and possibly speed up inference. However, the pre-determined nature of the current update strategies limits their application. To overcome this, we instead design the first fully-learned approach, SA-RNN, that augments any RNN by predicting discrete update patterns at the fine granularity of individual hidden state neurons. This is achieved through the parameterization of a distribution of update-likelihoods driven by the input data. Unlike related methods, our approach imposes no assumptions on the structure of the update patterns. Better yet, our method adapts its update patterns online, allowing different dimensions to be updated conditionally based on the input. To learn which dimensions to update, the model solves a multi-objective optimization problem, maximizing task performance while minimizing the number of updates based on a unified control. Using five publicly-available datasets spanning three sequential learning settings, we demonstrate that our method consistently achieves higher accuracy with fewer updates compared to state-of-the-art alternatives. We also show the benefits of learning to sparsely-update a large hidden state as opposed to densely-update a small hidden state. As an added benefit, our method can be directly applied to a wide variety of models containing RNN architectures.
Gaussian process with graph convolutional kernel for relational learning
Authors: Jinyuan Fang, Shangsong Liang, Zaiqiao Meng, Qiang Zhang
Venue: Conference KDD 2021
Year: 2021

Gaussian Process (GP) offers a principled non-parametric framework for learning stochastic functions. The generalization capability of GPs depends heavily on the kernel function, which implicitly imposes the smoothness assumptions of the data. However, common feature-based kernel functions are inefficient to model the relational data, where the smoothness assumptions implied by the kernels are violated. To model the complex and non-differentiable functions over relational data, we propose a novel Graph Convolutional Kernel, which enables to incorporate relational structures to feature-based kernels to capture the statistical structure of data. To validate the effectiveness of proposed kernel function in modeling relational data, we introduce GP models with Graph Convolutional Kernel in two relational learning settings, i.e., unsupervised settings of link prediction and semi-supervised settings of object classification. The parameters of our GP models are optimized through the scalable variational inducing point method. However, the highly structured likelihood objective requires densely sampling from variational distributions, which is costly and makes its optimization challenging in the unsupervised settings. To tackle this challenge, we propose a Local Neighbor Sampling technique with a provable more efficient computational complexity. Experimental results on real-world datasets demonstrate that our model achieves state-of-the-art performance in two relational learning tasks.
Program guided agent
Authors: Shao-Hua Sun, Te-Lin Wu, Joseph J. Lim
Venue: Conference ICLR 2020
Year: 2020

Developing agents that can learn to follow natural language instructions has been an emerging research direction. While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans. To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks. We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task. Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy. We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.
Route recommendations for idle taxi drivers: Find me the shortest route to a customer!
Authors: Nandani Garg, Sayan Ranu
Venue: Conference KDD 2018
Year: 2018

We study the problem of route recommendation to idle taxi drivers such that the distance between the taxi and an anticipated customer request is minimized. Minimizing the distance to the next anticipated customer leads to more productivity for the taxi driver and less waiting time for the customer. To anticipate when and where future customer requests are likely to come from and accordingly recom- mend routes, we develop a route recommendation engine called MDM: Minimizing Distance through Monte Carlo Tree Search. In contrast to existing techniques, MDM employs a continuous learning platform where the underlying model to predict future customer requests is dynamically updated. Extensive experiments on real taxi data from New York and San Francisco reveal that MDM is up to 70% better than the state of the art and robust to anomalous events such as concerts, sporting events, etc.
Probabilistic robust route recovery with spatio-temporal dynamics
Authors: Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, Wei Wang
Venue: Conference KDD 2016
Year: 2016

Vehicle trajectories are one of the most important data in location-based services. The quality of trajectories directly affects the services. However, in the real applications, trajectory data are not always sampled densely. In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory. Most existing works solve the problem without using those informative historical data or solve it in an empirical way. We claim that a data-driven and probabilistic approach is actually more suitable as long as data sparsity can be well handled. We propose a novel route recovery system in a fully probabilistic way which incorporates both temporal and spatial dynamics and addresses all the data sparsity problem introduced by the probabilistic method. It outperforms the existing works with a high accuracy (over 80%) and shows a strong robustness even when the length of routes to be recovered is very long (about 30 road segments) or the data is very sparse.
Beyond shortest paths: Route recommendations for ride-sharing
Authors: Chak Fai Yuen, Abhishek Pratap Singh, Sagar Goyal, Sayan Ranu, Amitabha Bagchi
Venue: Conference WWW 2019
Year: 2019

In taxi ride-sharing, multiple customers are allotted to the same taxi as long as they are compatible, i.e., if none of them suffers a detour beyond a permissible threshold. To attract customers to ride-sharing, taxi operators promise a reduced fare upfront. As a result, if the taxi fails to pair the initial customer with additional compatible passengers, the taxi operator incurs a financial loss. Hence, it is important to ensure that the taxi finds compatible customers once it has picked up the initial customer. In the current scenario, the appearance of subsequent compatible customers is left to luck: a taxi moves along the shortest (or quickest) path for the existing customer and hopes to find additional compatible customers on its way. In this paper, we ask: Is the shortest path the optimal path for ride-sharing? To investigate this question, we develop a route recommendation algorithm called Share, which predicts the route with the highest probability of finding compatible customers, while staying within the detour limits. Through extensive evaluations on real datasets, we show that Share reduces failure rate in ride-sharing by up to 40%, while also improving waiting time for customers by 20%. Technically, the route recommendation problem is NP-hard. We overcome this bottleneck, by reducing the search space of the entire road network into a smaller directed acyclic graph, on which we perform dynamic programming to identify the optimal route. This strategy allows us to answer queries within 0.2 seconds, and thereby making Share deployable on real road conditions.
Detecting extrapolation with local ensembles
Authors: David Madras, James Atwood, Alexander D&apos, Amour
Venue: Conference ICLR 2020
Year: 2020

We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.