GenAI4ELab/papercli-papers huggingface dataset:
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization
Authors: Yifei Wang; Peng Chen; Mert Pilanci; Wuchen Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite samples requires solving a variational problem. We study the variational problem in the family of two-layer networks with squared-ReLU activations, towards which we derive a semi-definite programming (SDP) relaxation. This SDP can be viewed as an approximation of the Wasserstein gradient in a broader function family including two-layer networks. By solving the convex SDP, we obtain the optimal approximation of the Wasserstein gradient direction in this class of functions. Numerical experiments including PDE-constrained Bayesian inference and parameter estimation in COVID-19 modeling demonstrate the effectiveness of the proposed method.
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Authors: Keir Adams; Connor W. Coley
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Shape-based virtual screening is widely used in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D graph structures compared to known ligands. 3D deep generative models can potentially automate this exploration of shape-conditioned 3D chemical space; however, no existing models can reliably generate geometrically realistic drug-like molecules in conformations with a specific shape. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molecular shape and variationally encoding chemical identity. We ensure local geometric and chemical validity of generated molecules by using autoregressive fragment-based generation with heuristic bonding geometries, allowing the model to prioritize the scoring of rotatable bonds to best align the growing conformation to the target shape. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization, demonstrating its utility over virtual screening of enumerated libraries.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Authors: Anshuman Chhabra; Peizhao Li; Prasant Mohapatra; Hongfu Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair model outputs can adversely impact individuals that rely on these applications. To this end, many $\textit{fair}$ clustering approaches have been recently proposed to counteract this issue. Due to the potential for significant harm, it is essential to ensure that fair clustering algorithms provide consistently fair outputs even under adversarial influence. However, fair clustering algorithms have not been studied from an adversarial attack perspective. In contrast to previous research, we seek to bridge this gap and conduct a robustness analysis against fair clustering by proposing a novel $\textit{black-box fairness attack}$. Through comprehensive experiments, we find that state-of-the-art models are highly susceptible to our attack as it can reduce their fairness performance significantly. Finally, we propose Consensus Fair Clustering (CFC), the first $\textit{robust fair clustering}$ approach that transforms consensus clustering into a fair graph partitioning problem, and iteratively learns to generate fair cluster outputs. Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.
Neural Image Compression with a Diffusion-based Decoder
Authors: Noor Fathima Khanum Mohamed Ghouse; Jens Petersen; Auke J. Wiggers; Tianlin Xu; Guillaume Sautiere
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call \emph{DIffuson-based Residual Augmentation Codec (DIRAC)}, is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.
Emergent Communication with Attention
Authors: Ryokan Ri; Ryo Ueda; Jason Naradowsky
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: To develop computational agents that can better communicate with others using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment. Humans often understand a thing or scene as a composite of concepts and those concepts are further mapped onto words. We implement this intuition as attention mechanisms in Speaker and Listener agents in a referential game and show attention leads to more compositional and interpretable emergent language. We also demonstrate how attention helps us understand the learned communication protocol by investigating the attention weights associated with each message symbol and the alignment of attention weights between Speaker and Listener agents. Overall, our results suggest that attention is a promising mechanism for developing more human-like emergent language.
ProtoVAE: Using Prototypical Networks for Unsupervised Disentanglement
Authors: Vaishnavi S Patil; Matthew Evanusa; Joseph JaJa
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely \emph{unsupervised} way. There is still, however, an open area of investigation into guiding the neural network to learn useful or good representations. The problem of unsupervised \textit{Disentanglement} is of particular importance as it offers to learn interpretable representations, with disjoint subsets of the representation encoding different, meaningful factors of variation. Recent work has theoretically grounded the factors of variation, via the lens of group theory, as disentangled actions of the symmetry subgroups which transform only the correspond subspaces of the disentangled representation. We use this mathematical formalism instead to impose constraints on the representations learned by a unsupervised generative neural network, such that transformations of the representation correspond to the actions of a unique symmetry subgroup. To this end, we introduce a novel model, ProtoVAE, that leverages a deep metric learning Prototypical network trained via self-supervision to constrain the latent space of a Variational Autoencoder to decompose into independent subspaces. Further, we actively change or \textit{intervene} in the latent space during training to enforce each dimension of the representation to uniquely and consistently transform the data corresponding to some symmetry subgroup. We demonstrate and evaluate our proposed model on the benchmark DSprites and 3DShapes datasets and compare with other state of the art disentanglement methods via qualitative traversals in the latent space, as well as quantitative disentanglement metrics. We further qualitatively demonstrate the effectiveness of our model on the real-world datasets CelebA which consistently encodes the different factors.
Applying Second Order Optimization to Deep Transformers with Parameter-Efficient Tuning
Authors: Ning Ding; Qiaosen Wang; Yulin Chen; Pengjun Xie; Zhiyuan Liu; Hai-Tao Zheng; Maosong Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite the theoretical superiority in convergence issues, second-order optimizers are generally not among the top choices for training large-scale neural networks due to their high computational and memory cost. Nevertheless, introduced in recent progress of parameter-efficient tuning is a new paradigm that large-scale pre-trained models (PTMs) can be adapted to specific tasks by optimizing a tiny proportion of parameters, which might hopefully change the game. We associate this new paradigm with the computational tractability of second-order optimizers and succeed in applying them to large PTMs that are from hundreds of millions to billions in scale. Beyond verifying their tractability, we further investigate the stability-influencing factors in the optimization process and propose accordingly a Newton-step-clipping approach in which we clip the update tensors rather than the gradients. This approach stabilizes the convergence by gating the magnitude of Newton steps along the optimization trajectories through the rugged landscapes of deep transformers. 
We conduct extensive experiments across different downstream tasks, demonstrating that, when equipped with our Newton-step-clipping strategy, second-order optimizers, especially Kronecker-factored curvature approximation (K-FAC), can attain comparable and even superior results and faster convergence to those state-of-the-art bars implemented with AdamW.  Furthermore, we scale the model up to 3 billion parameters and validate the tractability and effectiveness of our method. This work is not only the first successful application of second-order optimization on such large-scale models but also sheds light on the possibility of further optimization-wise analysis on large-scale models in the future.
A Mixture-of-Expert Approach to RL-based Dialogue Management
Authors: Yinlam Chow; Azamat Tulepbergenov; Ofir Nachum; Dhawal Gupta; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with a combinatorially complex action space even for a medium-size vocabulary. As a result, they struggle to produce a successful and engaging dialogue even if they are warm-started with a pre-trained LM. To address this issue, we develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a particular attribute or personality, and (iii) a RL-based DM that performs dialogue planning with the utterances generated by the experts. Our MoE approach provides greater flexibility to generate sensible utterances with different intents and allows RL to focus on conversational-level DM. We compare it with SOTA baselines on open-domain dialogues and demonstrate its effectiveness both in terms of the diversity and sensibility of the generated utterances and the overall DM performance.
Few-Shot Domain Adaptation For End-to-End Communication
Authors: Jayaram Raghuram; Yijing Zeng; Dolores Garcia; Rafael Ruiz; Somesh Jha; Joerg Widmer; Suman Banerjee
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach. A challenge faced in the practical adoption of this learning approach is that under changing channel conditions (e.g. a wireless link), it requires frequent retraining of the autoencoder in order to maintain a low decoding error rate. Since retraining is both time consuming and requires a large number of samples, it becomes impractical when the channel distribution is changing quickly. We propose to address this problem using a fast and sample-efficient (few-shot) domain adaptation method that does not change the encoder and decoder networks. Different from conventional training-time unsupervised or semi-supervised domain adaptation, here we have a trained autoencoder from a source distribution that we want to adapt (at test time) to a target distribution using only a small labeled dataset, and no unlabeled data. We focus on a generative channel model based on the Gaussian mixture density network (MDN), and propose a regularized, parameter-efficient adaptation of the MDN using a set of affine transformations. The learned affine transformations are then used to design an optimal transformation at the decoder input to compensate for the distribution shift, and effectively present to the decoder inputs close to the source distribution. Experiments on many simulated distribution changes common to the wireless setting, and a real mmWave FPGA testbed demonstrate the effectiveness of our method at adaptation using very few target domain samples~\footnote{Code for our work: \url{https://github.com/jayaram-r/domain-adaptation-autoencoder}}.
Short-Term Memory Convolutions
Authors: Grzegorz Stefański; Krzysztof Arendt; Paweł Daniluk; Bartłomiej Jasik; Artur Szumaczuk
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The real-time processing of time series signals is a critical issue for many real-life applications. The idea of real-time processing is especially important in audio domain as the human perception of sound is sensitive to any kind of disturbance in perceived signals, especially the lag between auditory and visual modalities. The rise of deep learning (DL) models complicated the landscape of signal processing. Although they often have superior quality compared to standard DSP methods, this advantage is diminished by higher latency. In this work we propose novel method for minimization of inference time latency and memory consumption, called Short-Term Memory Convolution (STMC) and its transposed counterpart. The main advantage of STMC is the low latency comparable to long short-term memory (LSTM) networks. Furthermore, the training of STMC-based models is faster and more stable as the method is based solely on convolutional neural networks (CNNs). In this study we demonstrate an application of this solution to a U-Net model for a speech separation task and GhostNet model in acoustic scene classification (ASC) task. In case of speech separation we achieved a 5-fold reduction in inference time and a 2-fold reduction in latency without affecting the output quality. The inference time for ASC task was up to 4 times faster while preserving the original accuracy.
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
Authors: Ansong Ni; Jeevana Priya Inala; Chenglong Wang; Alex Polozov; Christopher Meek; Dragomir Radev; Jianfeng Gao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one reference solution for each problem, despite the fact that there are often alternative solutions resembling different reasoning paths to the final answer. This way, the finetuned models are biased towards the limited reference solutions, which limits their generalization to unseen examples. To mitigate this issue, we propose to let the model perform sampling during training and learn from both self-sampled fully-correct solutions, which yield the correct answer upon execution, and partially-correct solutions, whose intermediate state matches an intermediate state of a known correct solution. We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space. Additionally, we explore various training objectives to support learning from multiple solutions per example and find they greatly affect the performance. Experiments on two math reasoning datasets show the effectiveness of our method compared to learning from a single reference solution with MLE, where we improve PASS@100 from 35.5% to 44.5% for GSM8K, and 27.6% to 36.2% PASS@80 for MathQA. Such improvements are also consistent across different model sizes.
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
Authors: Vasilii Feofanov; Malik Tiomoko; Aladin Virmaux
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a theoretical framework to analyze semi-supervised classification under the low density separation assumption in a high-dimensional regime. In particular, we introduce QLDS, a linear classification model, where the low density separation assumption is implemented via quadratic margin maximization.
The algorithm has an explicit solution with rich theoretical properties, and we show that particular cases of our algorithm are the least-square support vector machine in the supervised case, the spectral clustering in the fully unsupervised regime, and a class of semi-supervised graph-based approaches. As such, QLDS establishes a smooth bridge between these supervised and unsupervised learning methods. Using recent advances in the random matrix theory, we formally derive a theoretical evaluation of the classification error in the asymptotic regime.
As an application, we derive a hyperparameter selection policy that finds the best balance between the supervised and the unsupervised terms of our learning criterion.
Finally, we provide extensive illustrations of our framework, as well as an experimental study on several benchmarks to demonstrate that QLDS, while being computationally more efficient, improves over cross-validation for hyperparameter selection, indicating a high promise of the usage of random matrix theory for semi-supervised model selection.
The Curious Case of Benign Memorization
Authors: Sotiris Anagnostidis; Gregor Bachmann; Lorenzo Noci; Thomas Hofmann
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Despite the empirical advances of deep learning across a variety of learning tasks, our theoretical understanding of its success is still very restricted. One of the key challenges is the overparametrized nature of modern models, enabling complete overfitting of the data even if the labels are randomized, i.e. networks can completely \textit{memorize} all given patterns. While such a memorization capacity seems worrisome, in this work we show that under training protocols that include \textit{data augmentation}, neural networks learn to memorize entirely random labels in a benign way, i.e. they learn embeddings that lead to highly non-trivial performance under nearest neighbour probing. We demonstrate that deep models have the surprising ability to separate noise from signal by distributing the task of memorization and feature learning to different layers. As a result, only the very last layers are used for memorization, while preceding layers encode performant features which remain largely unaffected by the label noise. We explore the intricate role of the augmentations used for training and identify a memorization-generalization trade-off in terms of their diversity, marking a clear distinction to all previous works. Finally, we give a first explanation for the emergence of benign memorization by showing that \textit{malign} memorization under data augmentation is infeasible due to the insufficient capacity of the model for the increased sample size. As a consequence, the network is forced to leverage the correlated nature of the augmentations and as a result learns meaningful features. To complete the picture, a better theory of feature learning in deep neural networks is required to fully understand the origins of this phenomenon.
Towards convergence to Nash equilibria in two-team zero-sum games
Authors: Fivos Kalogiannis; Ioannis Panageas; Emmanouil-Vasileios Vlatakis-Gkaragkounis
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Contemporary applications of machine learning raise important and overlooked theoretical questions regarding optimization in two-team games. Formally, two-team zero-sum games are defined as multi-player games where players are split into two competing sets of agents, each experiencing a utility identical to that of their teammates and opposite to that of the opposing team. We focus on the solution concept of Nash equilibria and prove $\textrm{CLS}$-hardness of computing them in this class of games. To further examine the capabilities of online learning algorithms in games with full-information feedback, we propose a benchmark of a simple ---yet nontrivial--- family of such games. These games do not enjoy the properties used to prove convergence for relevant algorithms. In particular, we use a dynamical systems perspective to demonstrate that gradient descent-ascent, its optimistic variant, optimistic multiplicative weights update, and extra gradient fail to converge (even locally) to a Nash equilibrium. On a brighter note, we propose a first-order method that leverages control theory techniques and under some conditions enjoys last-iterate local convergence to a Nash equilibrium. We also believe our proposed method is of independent interest for general min-max optimization.
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Authors: Kaixuan Huang; Yu Wu; Xuezhou Zhang; Shenyinying Tu; Qingyun Wu; Mengdi Wang; Huazheng Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Online influence maximization aims to maximize the influence spread of a content in a social network with an unknown network model by selecting a few seed nodes. Recent studies followed a non-adaptive setting, where the seed nodes are selected before the start of the diffusion process and network parameters are updated when the diffusion stops. We consider an adaptive version of content-dependent online influence maximization problem where the seed nodes are sequentially activated based on real-time feedback. In this paper, we formulate the problem as an infinite-horizon discounted MDP under a linear diffusion process and present a model-based reinforcement learning solution. Our algorithm maintains a network model estimate and selects seed users adaptively, exploring the social network while improving the optimal policy optimistically. We establish $\widetilde O(\sqrt{T})$ regret bound for our algorithm. Empirical evaluations on synthetic and real-world networks demonstrate the efficiency of our algorithm.